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Sample records for self-organizing map algorithm

  1. An Algorithm Based on the Self-Organized Maps for the Classification of Facial Features

    Directory of Open Access Journals (Sweden)

    Gheorghe Gîlcă

    2015-12-01

    Full Text Available This paper deals with an algorithm based on Self Organized Maps networks which classifies facial features. The proposed algorithm can categorize the facial features defined by the input variables: eyebrow, mouth, eyelids into a map of their grouping. The groups map is based on calculating the distance between each input vector and each output neuron layer , the neuron with the minimum distance being declared winner neuron. The network structure consists of two levels: the first level contains three input vectors, each having forty-one values, while the second level contains the SOM competitive network which consists of 100 neurons. The proposed system can classify facial features quickly and easily using the proposed algorithm based on SOMs.

  2. Semi-automatic mapping of linear-trending bedforms using 'Self-Organizing Maps' algorithm

    Science.gov (United States)

    Foroutan, M.; Zimbelman, J. R.

    2017-09-01

    Increased application of high resolution spatial data such as high resolution satellite or Unmanned Aerial Vehicle (UAV) images from Earth, as well as High Resolution Imaging Science Experiment (HiRISE) images from Mars, makes it necessary to increase automation techniques capable of extracting detailed geomorphologic elements from such large data sets. Model validation by repeated images in environmental management studies such as climate-related changes as well as increasing access to high-resolution satellite images underline the demand for detailed automatic image-processing techniques in remote sensing. This study presents a methodology based on an unsupervised Artificial Neural Network (ANN) algorithm, known as Self Organizing Maps (SOM), to achieve the semi-automatic extraction of linear features with small footprints on satellite images. SOM is based on competitive learning and is efficient for handling huge data sets. We applied the SOM algorithm to high resolution satellite images of Earth and Mars (Quickbird, Worldview and HiRISE) in order to facilitate and speed up image analysis along with the improvement of the accuracy of results. About 98% overall accuracy and 0.001 quantization error in the recognition of small linear-trending bedforms demonstrate a promising framework.

  3. Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

    Science.gov (United States)

    Karaca, Yeliz; Cattani, Carlo

    Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

  4. An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2016-01-01

    Full Text Available Virtual machines (VM on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

  5. Macromolecular target prediction by self-organizing feature maps.

    Science.gov (United States)

    Schneider, Gisbert; Schneider, Petra

    2017-03-01

    Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

  6. Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

    Directory of Open Access Journals (Sweden)

    Leonhard Suchenwirth

    2014-07-01

    Full Text Available Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM and the k-nearest neighbor (kNN algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE. Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.

  7. Obtaining parton distribution functions from self-organizing maps

    International Nuclear Information System (INIS)

    Honkanen, H.; Liuti, S.; Loitiere, Y.C.; Brogan, D.; Reynolds, P.

    2007-01-01

    We present an alternative algorithm to global fitting procedures to construct Parton Distribution Functions parametrizations. The proposed algorithm uses Self-Organizing Maps which at variance with the standard Neural Networks, are based on competitive-learning. Self-Organizing Maps generate a non-uniform projection from a high dimensional data space onto a low dimensional one (usually 1 or 2 dimensions) by clustering similar PDF representations together. The SOMs are trained on progressively narrower selections of data samples. The selection criterion is that of convergence towards a neighborhood of the experimental data. All available data sets on deep inelastic scattering in the kinematical region of 0.001 ≤ x ≤ 0.75, and 1 ≤ Q 2 ≤ 100 GeV 2 , with a cut on the final state invariant mass, W 2 ≥ 10 GeV 2 were implemented. The proposed fitting procedure, at variance with standard neural network approaches, allows for an increased control of the systematic bias by enabling the user to directly control the data selection procedure at various stages of the process. (author)

  8. Comparison between genetic algorithm and self organizing map to detect botnet network traffic

    Science.gov (United States)

    Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

    2017-11-01

    In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

  9. Asymmetric neighborhood functions accelerate ordering process of self-organizing maps

    International Nuclear Information System (INIS)

    Ota, Kaiichiro; Aoki, Takaaki; Kurata, Koji; Aoyagi, Toshio

    2011-01-01

    A self-organizing map (SOM) algorithm can generate a topographic map from a high-dimensional stimulus space to a low-dimensional array of units. Because a topographic map preserves neighborhood relationships between the stimuli, the SOM can be applied to certain types of information processing such as data visualization. During the learning process, however, topological defects frequently emerge in the map. The presence of defects tends to drastically slow down the formation of a globally ordered topographic map. To remove such topological defects, it has been reported that an asymmetric neighborhood function is effective, but only in the simple case of mapping one-dimensional stimuli to a chain of units. In this paper, we demonstrate that even when high-dimensional stimuli are used, the asymmetric neighborhood function is effective for both artificial and real-world data. Our results suggest that applying the asymmetric neighborhood function to the SOM algorithm improves the reliability of the algorithm. In addition, it enables processing of complicated, high-dimensional data by using this algorithm.

  10. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Directory of Open Access Journals (Sweden)

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  11. 10th Workshop on Self-Organizing Maps

    CERN Document Server

    Schleif, Frank-Michael; Kaden, Marika; Lange, Mandy

    2014-01-01

    The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.   This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks.   Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, ...

  12. Theoretical and applied aspects of the self-organizing maps

    OpenAIRE

    Cottrell , Marie; Olteanu , Madalina; Rossi , Fabrice; Villa-Vialaneix , Nathalie

    2016-01-01

    International audience; The Self-Organizing Map (SOM) is widely used, easy to implement , has nice properties for data mining by providing both clustering and visual representation. It acts as an extension of the k-means algorithm that preserves as much as possible the topological structure of the data. However, since its conception, the mathematical study of the SOM remains difficult and has be done only in very special cases. In WSOM 2005, Jean-Claude Fort presented the state of the art, th...

  13. New Angle on the Parton Distribution Functions: Self-Organizing Maps

    International Nuclear Information System (INIS)

    Honkanen, H.; Liuti, S.

    2009-01-01

    Neural network (NN) algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations, providing an alternative to standard global fitting procedures. Here we explore a novel technique using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. We train our SOMs with stochastically generated PDF samples. On every optimization iteration the PDFs are clustered on the SOM according to a user-defined feature and the most promising candidates are used as a seed for the subsequent iteration using the topology of the map to guide the PDF generating process. Our goal is a fitting procedure that, at variance with the standard neural network approaches, will allow for an increased control of the systematic bias by enabling user interaction in the various stages of the process.

  14. Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map

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    Satoshi Suzuki

    2012-01-01

    Full Text Available We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trained SOM. This method enables to omit the troublesome process to specify types of information which should be used to build the estimator. Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed. As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts. On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes. Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate. And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered.

  15. 9th Workshop on Self-Organizing Maps

    CERN Document Server

    Príncipe, José; Zegers, Pablo

    2013-01-01

    Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods.

  16. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Lin Liang

    2015-01-01

    Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

  17. Classification of perovskites with supervised self-organizing maps

    International Nuclear Information System (INIS)

    Kuzmanovski, Igor; Dimitrovska-Lazova, Sandra; Aleksovska, Slobotka

    2007-01-01

    In this work supervised self-organizing maps were used for structural classification of perovskites. For this purpose, structural data for total number of 286 perovskites, belonging to ABO 3 and/or A 2 BB'O 6 types, were collected from literature: 130 of these are cubic, 85 orthorhombic and 71 monoclinic. For classification purposes, the effective ionic radii of the cations, electronegativities of the cations in B-position, as well as, the oxidation states of these cations, were used as input variables. The parameters of the developed models, as well as, the most suitable variables for classification purposes were selected using genetic algorithms. Two-third of all the compounds were used in the training phase. During the optimization process the performances of the models were checked using cross-validation leave-1/10-out. The performances of obtained solutions were checked using the test set composed of the remaining one-third of the compounds. The obtained models for classification of these three classes of perovskite compounds show very good results. Namely, the classification of the compounds in the test set resulted in small number of discrepancies (4.2-6.4%) between the actual crystallographic class and the one predicted by the models. All these results are strong arguments for the validity of supervised self-organizing maps for performing such types of classification. Therefore, the proposed procedure could be successfully used for crystallographic classification of perovskites in one of these three classes

  18. Self-Organization in Coupled Map Scale-Free Networks

    International Nuclear Information System (INIS)

    Xiao-Ming, Liang; Zong-Hua, Liu; Hua-Ping, Lü

    2008-01-01

    We study the self-organization of phase synchronization in coupled map scale-free networks with chaotic logistic map at each node and find that a variety of ordered spatiotemporal patterns emerge spontaneously in a regime of coupling strength. These ordered behaviours will change with the increase of the average links and are robust to both the system size and parameter mismatch. A heuristic theory is given to explain the mechanism of self-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns

  19. Risk-based fault detection using Self-Organizing Map

    International Nuclear Information System (INIS)

    Yu, Hongyang; Khan, Faisal; Garaniya, Vikram

    2015-01-01

    The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring

  20. An new self-organizing maps strategy for solving the traveling salesman problem

    International Nuclear Information System (INIS)

    Bai Yanping; Zhang Wendong; Jin Zhen

    2006-01-01

    This paper presents an approach to the well-known traveling salesman problem (TSP) using self-organizing maps (SOM). There are many types of SOM algorithms to solve the TSP found in the literature, whereas the purpose of this paper is to look for the incorporation of an efficient initialization methods and the definition of a parameters adaptation law to achieve better results and a faster convergence. Aspects of parameters adaptation, selecting the number of nodes of neurons, index of winner neurons and effect of the initial ordering of the cities, as well as the initial synaptic weights of the modified SOM algorithm are discussed. The complexity of the modified SOM algorithm is analyzed. The simulated results show an average deviation of 2.32% from the optimal tour length for a set of 12 TSP instances

  1. An new self-organizing maps strategy for solving the traveling salesman problem

    Energy Technology Data Exchange (ETDEWEB)

    Bai Yanping [Key Lab of Instrument Science and Dynamic Measurement of Ministry of Education, North University of China, No. 3, Xueyuan Road, TaiYuan, ShanXi 030051 (China)]. E-mail: baiyp@nuc.edu.cn; Zhang Wendong [Key Lab of Instrument Science and Dynamic Measurement of Ministry of Education, North University of China, No. 3, Xueyuan Road, TaiYuan, ShanXi 030051 (China)]. E-mail: wdzhang@nuc.edu.cn; Jin Zhen [Department of Applied Mathematics, North University of China, No. 3 Xueyuan Road, TaiYuan, ShanXi 030051 (China)

    2006-05-15

    This paper presents an approach to the well-known traveling salesman problem (TSP) using self-organizing maps (SOM). There are many types of SOM algorithms to solve the TSP found in the literature, whereas the purpose of this paper is to look for the incorporation of an efficient initialization methods and the definition of a parameters adaptation law to achieve better results and a faster convergence. Aspects of parameters adaptation, selecting the number of nodes of neurons, index of winner neurons and effect of the initial ordering of the cities, as well as the initial synaptic weights of the modified SOM algorithm are discussed. The complexity of the modified SOM algorithm is analyzed. The simulated results show an average deviation of 2.32% from the optimal tour length for a set of 12 TSP instances.

  2. 11th Workshop on Self-Organizing Maps

    CERN Document Server

    Mendenhall, Michael; O'Driscoll, Patrick

    2016-01-01

    This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and ...

  3. Clustering analysis of malware behavior using Self Organizing Map

    DEFF Research Database (Denmark)

    Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

  4. Business Client Segmentation in Banking Using Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Bach Mirjana Pejić

    2014-11-01

    Full Text Available Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means. The goal of the paper is to demonstrate that self-organizing maps (SOM effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export, annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

  5. Autonomous Data Collection Using a Self-Organizing Map.

    Science.gov (United States)

    Faigl, Jan; Hollinger, Geoffrey A

    2018-05-01

    The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.

  6. Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

    Science.gov (United States)

    Rahmani, S.; Teimoorinia, H.; Barmby, P.

    2018-05-01

    The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

  7. Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Javier Blesa

    2009-11-01

    Full Text Available The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps, in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

  8. Comparative investigation of two different self-organizing map ...

    African Journals Online (AJOL)

    Purpose: To demonstrate the ability and investigate the performance of two different wavelength selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) regression for analysis of pharmaceutical binary mixtures with strongly overlapping spectra. Methods: Two different variable ...

  9. Self-organized spectrum chunk selection algorithm for Local Area LTE-Advanced

    DEFF Research Database (Denmark)

    Kumar, Sanjay; Wang, Yuanye; Marchetti, Nicola

    2010-01-01

    This paper presents a self organized spectrum chunk selection algorithm in order to minimize the mutual intercell interference among Home Node Bs (HeNBs), aiming to improve the system throughput performance compared to the existing frequency reuse one scheme. The proposed algorithm is useful...

  10. Self-organizing maps: A tool to ascertain taxonomic relatedness ...

    Indian Academy of Sciences (India)

    MADHU

    what is known as numerical taxonomy (Garrity et al. 2001). ... Curvilinear component analysis; self-organizing maps; principal component analysis. Abbreviations used: ... This tool undergoes unsupervised learning and is particularly useful in ...

  11. An application of the Self Organizing Map Algorithm to computer aided classification of ASTER multispectral data

    Directory of Open Access Journals (Sweden)

    Ferdinando Giacco

    2008-01-01

    Full Text Available In this paper we employ the Kohonen’s Self Organizing Map (SOM as a strategy for an unsupervised analysis of ASTER multispectral (MS images. In order to obtain an accurate clusterization we introduce as input for the network, in addition to spectral data, some texture measures extracted from IKONOS images, which gives a contribution to the classification of manmade structures. After clustering of SOM outcomes, we associated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed.

  12. Gaining insight in domestic violence with emergent self organizing maps

    NARCIS (Netherlands)

    Poelmans, J.; Elzinga, P.; Viaene, S.; van Hulle, M.M.; Dedene, G.

    2009-01-01

    Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the past years, new types of Self Organizing Maps (SOM) were introduced in the literature, including the recent Emergent SOM. The ESOM tool is used here to analyze a large set of police

  13. Mobile Anomaly Detection Based on Improved Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Chunyong Yin

    2017-01-01

    Full Text Available Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

  14. Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.

    Science.gov (United States)

    Dafonte, Carlos; Garabato, Daniel; Álvarez, Marco A; Manteiga, Minia

    2018-05-03

    Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

  15. Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †

    Directory of Open Access Journals (Sweden)

    Carlos Dafonte

    2018-05-01

    Full Text Available Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

  16. Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

    Directory of Open Access Journals (Sweden)

    Mengxue eCao

    2014-03-01

    Full Text Available Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic--semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners; a reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1 I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2 clear auditory and semantic boundaries can be found in the network representation; (3 cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4 reinforcing-by-link training leads to well-perceived auditory--semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

  17. Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

    Science.gov (United States)

    Wu, Hongle; Kato, Takafumi; Yamada, Tomomi; Numao, Masayuki; Fukui, Ken-Ichi

    2017-07-01

    We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Resting state cortico-cerebellar functional connectivity networks: A comparison of anatomical and self-organizing map approaches

    Directory of Open Access Journals (Sweden)

    Jessica A Bernard

    2012-08-01

    Full Text Available The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Buckner et al., 2011; Krienen & Buckner, 2009; O’Reilly et al., 2009. However, none of this work has taken an anatomically-driven approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011, it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven cerebellar connectivity atlas. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into motor and non-motor regions. We also used a self-organizing map algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our self-organizing map algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not indicative of functional boundaries, though anatomical divisions can be useful, as is the case of the anterior cerebellum. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

  19. A privacy-preserving sharing method of electricity usage using self-organizing map

    Directory of Open Access Journals (Sweden)

    Yuichi Nakamura

    2018-03-01

    Full Text Available Smart meters for measuring electricity usage are expected in electricity usage management. Although the relevant power supplier stores the measured data, the data are worth sharing among power suppliers because the entire data of a city will be required to control the regional grid stability or demand–supply balance. Even though many techniques and methods of privacy-preserving data mining have been studied to share data while preserving data privacy, a study on sharing electricity usage data is still lacking. In this paper, we propose a sharing method of electricity usage while preserving data privacy using a self-organizing map. Keywords: Privacy preserving, Data sharing, Self-Organizing map

  20. High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

    Science.gov (United States)

    Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

    2018-05-04

    Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Self-organized modularization in evolutionary algorithms.

    Science.gov (United States)

    Dauscher, Peter; Uthmann, Thomas

    2005-01-01

    The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

  2. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Science.gov (United States)

    Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

    2005-01-01

    The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

  3. CLUSTER ANALYSIS UNTUK MEMPREDIKSI TALENTA PEMAIN BASKET MENGGUNAKAN JARINGAN SARAF TIRUAN SELF ORGANIZING MAPS (SOM

    Directory of Open Access Journals (Sweden)

    Gregorius Satia Budhi

    2008-01-01

    Full Text Available Basketball World has grown rapidly as the time goes on. This is signed by many competition and game all over the world. With the result there are many basketball players with their different playing characteristics. Demand for a coach or scout to look for or search great players to make a solid team as a coach requirement. With this application, a coach or scout will be helped in analyzing in decision making. This application uses Self Organizing Maps algorithm (SOM for Cluster Analysis. The real NBA player data is used for competitive learning or training process and real player data from Indonesian or Petra Christian University Basketball Players is used for testing process. The NBA Player data is prepared through cleaning process and then is transformed into a form that can be processed by SOM Algorithm. After that, the data is clustered with the SOM algorithm. The result of that clusters is displayed into a form that is easy to view and analyze. This result can be saved into a text file. By using the output / result of this application, that are the clusters of NBA player, the user can see the statistics of each cluster. With these cluster statistics coach or scout can predict the statistic and the position of a testing player who is in the same cluster. This information can give a support for the coach or scout to make a decision. Abstract in Bahasa Indonesia : Dunia bola basket telah berkembang dengan pesat seiring dengan berjalannya waktu. Hal ini ditandai dengan munculnya berbagai macam dan jenis kompetisi dan pertandingan baik dunia maupun dalam negeri. Sehingga makin banyak dilahirkannya pemain berbakat dengan berbagai karakteristik permainan yang berbeda. Tuntutan bagi seorang pelatih/pemandu bakat, untuk dapat melihat secara jeli dalam memenuhi kebutuhan tim untuk membentuk tim yang solid. Dengan dibuatnya aplikasi ini, maka akan membantu proses analisis dan pengambilan keputusan bagi pelatih maupun pemandu bakat Aplikasi ini

  4. Self-organizing map classifier for stressed speech recognition

    Science.gov (United States)

    Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

    2016-05-01

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

  5. Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

    Science.gov (United States)

    Piastra, Marco

    2013-05-01

    Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Seismic facies analysis from pre-stack data using self-organizing maps

    International Nuclear Information System (INIS)

    Kourki, Meysam; Ali Riahi, Mohammad

    2014-01-01

    In facies analysis, seismic data are clustered in different groups. Each group represents subsurface points with similar physical properties. Different groups can be related to differences in lithology, physical properties of rocks and fluid changes in the rocks. The supervised and unsupervised data clustering are known as two types of clustering architecture. In supervised clustering, the number of clusters is predefined, while in unsupervised clustering, a collection of patterns partitions into groups without predefined clusters. In this study, the pre-stack data clustering is used for seismic facies analysis. In this way, a horizon was selected from pre-stack data, followed by sorting of data using offset. A trace associated with each CDP is constructed, for which the first and second samples are related to the first and second offsets, respectively. The created trace is called consolidated trace which is characteristic of subsurface points. These consolidated traces are clustered by using self-organizing maps (SOM). In proposed pre-stack seismic data clustering, points with similar physical properties are placed in one cluster. Seismic data associated with hydrocarbon reservoirs have very different characteristics that are easily recognized. The efficiency of the proposed method was tested on both synthetic and real seismic data. The results showed that the algorithm improves the data classification and the points of different properties are noticeable in final maps. (paper)

  7. Colour segmentation of multi variants tuberculosis sputum images using self organizing map

    Science.gov (United States)

    Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

    2017-05-01

    Lung tuberculosis detection is still identified from Ziehl-Neelsen sputum smear images in low and middle countries. The clinicians decide the grade of this disease by counting manually the amount of tuberculosis bacilli. It is very tedious for clinicians with a lot number of patient and without standardization for sputum staining. The tuberculosis sputum images have multi variant characterizations in colour, because of no standardization in staining. The sputum has more variants colour and they are difficult to be identified. For helping the clinicians, this research examined the Self Organizing Map method for colouring image segmentation in sputum images based on colour clustering. This method has better performance than k-means clustering which also tried in this research. The Self Organizing Map could segment the sputum images with y good result and cluster the colours adaptively.

  8. A Contribution to the Study of Ensemble of Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Leandro Antonio Pasa

    2015-01-01

    Full Text Available This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

  9. Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

    Directory of Open Access Journals (Sweden)

    Aline Regina Walkoff

    2017-10-01

    Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

  10. Authoring Tool for Identifying Learning Styles, Using Self-Organizing Maps on Mobile Devices

    Directory of Open Access Journals (Sweden)

    Ramón Zatarain Cabada

    2011-05-01

    Full Text Available This work explores a methodological proposal whose main objective is the identification of learning styles using a method of self-organizing maps designed to work, for the most part, on mobile devices. These maps can work in real time and without direct student interaction, which implies the absence of prior information. The results generated are an authoring tool for adaptive courses in Web 2.0 environments.

  11. The morphological classification of normal and abnormal red blood cell using Self Organizing Map

    Science.gov (United States)

    Rahmat, R. F.; Wulandari, F. S.; Faza, S.; Muchtar, M. A.; Siregar, I.

    2018-02-01

    Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

  12. QUALITATIVE ANALYSIS OF OFFICIAL MILK CONTROL IN VALENCIA COMMUNITY (SPAIN BY SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    Carlos Javier Fernandez

    2009-06-01

    Full Text Available Breeding programs in dairy goats are mainly based on milk production and composition. Murciano-Granadina goats are located principally in the central and southern regions of Spain. This study is focused in Valencia Community (Spain and the objective is to study the Murciano-Granadina livestock based on the database from Murciano-Granadina Goat Breeders Association of Valencia (AMURVAL.  The aim of this study is to analyze the relationship among different variables related with milk production; milk yield, fat, protein, lactose, SCC, the number of births, lactation number and season. This analysis is carried out by using the Self Organizing Map. This tool allows mapping high-dimensional input spaces into much lower-dimensional spaces, thus making much more straightforward to understand any representation of data. These representations enable to visually extract qualitative relationships among variables (Visual Data Mining. A total of 3221 Murciano-Granadina dairy goats from AMURVAL were chosen. Self Organizing Maps (SOM were used to analyze data with the system identification toolbox of MATLAB v7. Data were obtained from Official Milk Control during 2006 campaign. SOM considered in this study is formed by 21´14 neurons (294 neurons; the chosen architecture is given by the range of the input variables used. The map shown that more than 70% of the goats has milk yield greater than 300 kg per lactation and goat, indicating good performance of farms. Besides, the SOM obtained indicate a group of neurons that included goats with high SSC (2%. The use of Self Organizing Maps in the descriptive analysis of this kind of data sets has proven to be highly valuable in extracting qualitative conclusions and guiding in improving the performance of farms.

  13. Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Mika Liukkonen

    2010-01-01

    Full Text Available Efficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components and to create multivariate nonlinear models describing their formation in the process. First, a generic process model was created by using a self-organizing map, which was clustered with the k-means algorithm to create subsets representing the different states of the process. Characteristically, these process states may include high- and low- load situations and transition states where the load is increased or decreased. Then emission models were constructed for both the entire process and for the process state of high boiler load. The main conclusion is that the methodology used is able to reveal such phenomena that occur within the process states and that could otherwise be difficult to observe.

  14. Self-organizing maps based on limit cycle attractors.

    Science.gov (United States)

    Huang, Di-Wei; Gentili, Rodolphe J; Reggia, James A

    2015-03-01

    Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Image Fusion Based on the Self-Organizing Feature Map Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhaoli; SUN Shenghe

    2001-01-01

    This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

  16. An Information-Theoretic-Cluster Visualization for Self-Organizing Maps.

    Science.gov (United States)

    Brito da Silva, Leonardo Enzo; Wunsch, Donald C

    2018-06-01

    Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, an information-theoretic-based method for cluster visualization using self-organizing maps (SOMs) is presented. The information-theoretic visualization (IT-vis) has the same structure as the unified distance matrix, but instead of depicting Euclidean distances between adjacent neurons, it displays the similarity between the distributions associated with adjacent neurons. Each SOM neuron has an associated subset of the data set whose cardinality controls the granularity of the IT-vis and with which the first- and second-order statistics are computed and used to estimate their probability density functions. These are used to calculate the similarity measure, based on Renyi's quadratic cross entropy and cross information potential (CIP). The introduced visualizations combine the low computational cost and kernel estimation properties of the representative CIP and the data structure representation of a single-linkage-based grouping algorithm to generate an enhanced SOM-based visualization. The visual quality of the IT-vis is assessed by comparing it with other visualization methods for several real-world and synthetic benchmark data sets. Thus, this paper also contains a significant literature survey. The experiments demonstrate the IT-vis cluster revealing capabilities, in which cluster boundaries are sharply captured. Additionally, the information-theoretic visualizations are used to perform clustering of the SOM. Compared with other methods, IT-vis of large SOMs yielded the best results in this paper, for which the quality of the final partitions was evaluated using external validity indices.

  17. Visualizing the topical structure of the medical sciences: a self-organizing map approach.

    Directory of Open Access Journals (Sweden)

    André Skupin

    Full Text Available We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1 little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2 post-training geometric and semiotic transformations of the SOM tend to be limited, and (3 no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.

  18. Using self-organizing maps to determine observation threshold limit predictions in highly variant data

    Science.gov (United States)

    Paganoni, C.A.; Chang, K.C.; Robblee, M.B.

    2006-01-01

    A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

  19. Self-Organizing Maps for Fingerprint Image Quality Assessment

    DEFF Research Database (Denmark)

    Olsen, Martin Aastrup; Tabassi, Elham; Makarov, Anton

    2013-01-01

    Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification and iden......Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification...... machine learning techniques. We train a self-organizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between...

  20. Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

    Science.gov (United States)

    Tosida, E. T.; Maryana, S.; Thaheer, H.; Hardiani

    2017-01-01

    Information technology and communication (telematics) is one of the most rapidly developing business sectors in Indonesia. It has strategic position in its contribution towards planning and implementation of developmental, economics, social, politics and defence strategies in business, communication and education. Aid absorption for the national telecommunication SMEs is relatively low; therefore, improvement is needed using analysis on business support cluster of which basis is types of business. In the study, the business support cluster analysis is specifically implemented for Indonesian telecommunication service. The data for the business are obtained from the National Census of Economic (Susenas 2006). The method used to develop cluster model is an Artificial Neural Network (ANN) system called Self-Organizing Maps (SOM) algorithm. Based on Index of Davies Bouldin (IDB), the accuracy level of the cluster model is 0.37 or can be categorized as good. The cluster model is developed to find out telecommunication business clusters that has influence towards the national economy so that it is easier for the government to supervise telecommunication business.

  1. Multiscale visual quality assessment for cluster analysis with self-organizing maps

    Science.gov (United States)

    Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

    2011-01-01

    Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

  2. Self-organizing maps for measuring similarity of audiovisual speech percepts

    DEFF Research Database (Denmark)

    Bothe, Hans-Heinrich

    The goal of this work is to find a way to measure similarity of audiovisual speech percepts. Phoneme-related self-organizing maps (SOM) with a rectangular basis are trained with data material from a (labeled) video film. For the training, a combination of auditory speech features and corresponding....... Dependent on the training data, these other units may also be contextually immediate neighboring units. The poster demonstrates the idea with text material spoken by one individual subject using a set of simple audio-visual features. The data material for the training process consists of 44 labeled...... sentences in German with a balanced phoneme repertoire. As a result it can be stated that (i) the SOM can be trained to map auditory and visual features in a topology-preserving way and (ii) they show strain due to the influence of other audio-visual units. The SOM can be used to measure similarity amongst...

  3. SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

    Science.gov (United States)

    Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

    2018-01-01

    Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

  4. Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

    OpenAIRE

    Veldin A. Talorete Jr.; Sherwin A Guirnaldo

    2017-01-01

    This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifie...

  5. Growing hierarchical probabilistic self-organizing graphs.

    Science.gov (United States)

    López-Rubio, Ezequiel; Palomo, Esteban José

    2011-07-01

    Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

  6. Topological mappings of video and audio data.

    Science.gov (United States)

    Fyfe, Colin; Barbakh, Wesam; Ooi, Wei Chuan; Ko, Hanseok

    2008-12-01

    We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).(1) But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.(2) We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map.

  7. Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

    Energy Technology Data Exchange (ETDEWEB)

    Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Lezzerini, Marco [Department of Earth Sciences, University of Pisa, Via S. Maria 53, 56126 Pisa (Italy); Lorenzetti, Giulia [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Palleschi, Vincenzo, E-mail: vincenzo.palleschi@cnr.it [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Department of Civilizations and Forms of Knowledge, University of Pisa, Via L. Galvani 1, 56126 Pisa (Italy)

    2015-01-01

    In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed.

  8. Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

    International Nuclear Information System (INIS)

    Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lezzerini, Marco; Lorenzetti, Giulia; Palleschi, Vincenzo

    2015-01-01

    In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed

  9. The dynamics of ant mosaics in tropical rainforests characterized using the Self-Organizing Map algorithm.

    Science.gov (United States)

    Dejean, Alain; Azémar, Frédéric; Céréghino, Régis; Leponce, Maurice; Corbara, Bruno; Orivel, Jérôme; Compin, Arthur

    2016-08-01

    Ants, the most abundant taxa among canopy-dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self-Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved. © 2015 Institute of Zoology, Chinese Academy of Sciences.

  10. Characterization of Suicidal Behaviour with Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    José M. Leiva-Murillo

    2013-01-01

    Full Text Available The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.

  11. Curbing domestic violence: Instantiating C-K theory with formal concept analysis and emergent self-organizing maps

    NARCIS (Netherlands)

    Poelmans, J.; Elzinga, P.; Viaene, S.; Dedene, G.

    2010-01-01

    We propose a human-centred process for knowledge discovery from unstructured text that makes use of formal concept analysis and emergent self-organizing maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the concept-knowledge (C-K) theory design

  12. Identification of lithofacies using Kohonen self-organizing maps

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

    2002-01-01

    Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

  13. Usage of self-organizing neural networks in evaluation of consumer behaviour

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2010-01-01

    Full Text Available This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.

  14. Modeling Poroelastic Wave Propagation in a Real 2-D Complex Geological Structure Obtained via Self-Organizing Maps

    Science.gov (United States)

    Itzá Balam, Reymundo; Iturrarán-Viveros, Ursula; Parra, Jorge O.

    2018-03-01

    Two main stages of seismic modeling are geological model building and numerical computation of seismic response for the model. The quality of the computed seismic response is partly related to the type of model that is built. Therefore, the model building approaches become as important as seismic forward numerical methods. For this purpose, three petrophysical facies (sands, shales and limestones) are extracted from reflection seismic data and some seismic attributes via the clustering method called Self-Organizing Maps (SOM), which, in this context, serves as a geological model building tool. This model with all its properties is the input to the Optimal Implicit Staggered Finite Difference (OISFD) algorithm to create synthetic seismograms for poroelastic, poroacoustic and elastic media. The results show a good agreement between observed and 2-D synthetic seismograms. This demonstrates that the SOM classification method enables us to extract facies from seismic data and allows us to integrate the lithology at the borehole scale with the 2-D seismic data.

  15. Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

    Science.gov (United States)

    Hortos, William S.

    2006-05-01

    A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the

  16. Self-Organizing Map Models of Language Acquisition

    Directory of Open Access Journals (Sweden)

    Ping eLi

    2013-11-01

    Full Text Available Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic PDP architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development.

  17. Self-enhancement learning: target-creating learning and its application to self-organizing maps.

    Science.gov (United States)

    Kamimura, Ryotaro

    2011-05-01

    In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

  18. Identifying changes in dissolved organic matter content and characteristics by fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis during wastewater treatment.

    Science.gov (United States)

    Yu, Huibin; Song, Yonghui; Liu, Ruixia; Pan, Hongwei; Xiang, Liancheng; Qian, Feng

    2014-10-01

    The stabilization of latent tracers of dissolved organic matter (DOM) of wastewater was analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis (CART) in wastewater treatment performance. DOM of water samples collected from primary sedimentation, anaerobic, anoxic, oxic and secondary sedimentation tanks in a large-scale wastewater treatment plant contained four fluorescence components: tryptophan-like (C1), tyrosine-like (C2), microbial humic-like (C3) and fulvic-like (C4) materials extracted by self-organizing map. These components showed good positive linear correlations with dissolved organic carbon of DOM. C1 and C2 were representative components in the wastewater, and they were removed to a higher extent than those of C3 and C4 in the treatment process. C2 was a latent parameter determined by CART to differentiate water samples of oxic and secondary sedimentation tanks from the successive treatment units, indirectly proving that most of tyrosine-like material was degraded by anaerobic microorganisms. C1 was an accurate parameter to comprehensively separate the samples of the five treatment units from each other, indirectly indicating that tryptophan-like material was decomposed by anaerobic and aerobic bacteria. EEM fluorescence spectroscopy in combination with self-organizing map and CART analysis can be a nondestructive effective method for characterizing structural component of DOM fractions and monitoring organic matter removal in wastewater treatment process. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Experiences with Implementing a Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Gathering on a Real-Life Sensor Network Platform

    NARCIS (Netherlands)

    Zhang, Y.; Chatterjea, Supriyo; Havinga, Paul J.M.

    2007-01-01

    We report our experiences with implementing a distributed and self-organizing scheduling algorithm designed for energy-efficient data gathering on a 25-node multihop wireless sensor network (WSN). The algorithm takes advantage of spatial correlations that exist in readings of adjacent sensor nodes

  20. Electron dose map inversion based on several algorithms

    International Nuclear Information System (INIS)

    Li Gui; Zheng Huaqing; Wu Yican; Fds Team

    2010-01-01

    The reconstruction to the electron dose map in radiation therapy was investigated by constructing the inversion model of electron dose map with different algorithms. The inversion model of electron dose map based on nonlinear programming was used, and this model was applied the penetration dose map to invert the total space one. The realization of this inversion model was by several inversion algorithms. The test results with seven samples show that except the NMinimize algorithm, which worked for just one sample, with great error,though,all the inversion algorithms could be realized to our inversion model rapidly and accurately. The Levenberg-Marquardt algorithm, having the greatest accuracy and speed, could be considered as the first choice in electron dose map inversion.Further tests show that more error would be created when the data close to the electron range was used (tail error). The tail error might be caused by the approximation of mean energy spectra, and this should be considered to improve the method. The time-saving and accurate algorithms could be used to achieve real-time dose map inversion. By selecting the best inversion algorithm, the clinical need in real-time dose verification can be satisfied. (authors)

  1. a Laser-Slam Algorithm for Indoor Mobile Mapping

    Science.gov (United States)

    Zhang, Wenjun; Zhang, Qiao; Sun, Kai; Guo, Sheng

    2016-06-01

    A novel Laser-SLAM algorithm is presented for real indoor environment mobile mapping. SLAM algorithm can be divided into two classes, Bayes filter-based and graph optimization-based. The former is often difficult to guarantee consistency and accuracy in largescale environment mapping because of the accumulative error during incremental mapping. Graph optimization-based SLAM method often assume predetermined landmarks, which is difficult to be got in unknown environment mapping. And there most likely has large difference between the optimize result and the real data, because the constraints are too few. This paper designed a kind of sub-map method, which could map more accurately without predetermined landmarks and avoid the already-drawn map impact on agent's location. The tree structure of sub-map can be indexed quickly and reduce the amount of memory consuming when mapping. The algorithm combined Bayes-based and graph optimization-based SLAM algorithm. It created virtual landmarks automatically by associating data of sub-maps for graph optimization. Then graph optimization guaranteed consistency and accuracy in large-scale environment mapping and improved the reasonability and reliability of the optimize results. Experimental results are presented with a laser sensor (UTM 30LX) in official buildings and shopping centres, which prove that the proposed algorithm can obtain 2D maps within 10cm precision in indoor environment range from several hundreds to 12000 square meter.

  2. A Self-Organizing Map-Based Approach to Generating Reduced-Size, Statistically Similar Climate Datasets

    Science.gov (United States)

    Cabell, R.; Delle Monache, L.; Alessandrini, S.; Rodriguez, L.

    2015-12-01

    Climate-based studies require large amounts of data in order to produce accurate and reliable results. Many of these studies have used 30-plus year data sets in order to produce stable and high-quality results, and as a result, many such data sets are available, generally in the form of global reanalyses. While the analysis of these data lead to high-fidelity results, its processing can be very computationally expensive. This computational burden prevents the utilization of these data sets for certain applications, e.g., when rapid response is needed in crisis management and disaster planning scenarios resulting from release of toxic material in the atmosphere. We have developed a methodology to reduce large climate datasets to more manageable sizes while retaining statistically similar results when used to produce ensembles of possible outcomes. We do this by employing a Self-Organizing Map (SOM) algorithm to analyze general patterns of meteorological fields over a regional domain of interest to produce a small set of "typical days" with which to generate the model ensemble. The SOM algorithm takes as input a set of vectors and generates a 2D map of representative vectors deemed most similar to the input set and to each other. Input predictors are selected that are correlated with the model output, which in our case is an Atmospheric Transport and Dispersion (T&D) model that is highly dependent on surface winds and boundary layer depth. To choose a subset of "typical days," each input day is assigned to its closest SOM map node vector and then ranked by distance. Each node vector is treated as a distribution and days are sampled from them by percentile. Using a 30-node SOM, with sampling every 20th percentile, we have been able to reduce 30 years of the Climate Forecast System Reanalysis (CFSR) data for the month of October to 150 "typical days." To estimate the skill of this approach, the "Measure of Effectiveness" (MOE) metric is used to compare area and overlap

  3. On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping

    Directory of Open Access Journals (Sweden)

    Abhijeet Ravankar

    2016-05-01

    Full Text Available Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM. We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization.

  4. Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles

    Directory of Open Access Journals (Sweden)

    Fabio Stella

    2013-09-01

    Full Text Available An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering.

  5. Automatic lithofacies segmentation from well-logs data. A comparative study between the Self-Organizing Map (SOM) and Walsh transform

    Science.gov (United States)

    Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar

    2013-04-01

    The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html

  6. Detecting domestic violence: Showcasing a knowledge browser based on formal concept analysis and emergent self organizing maps

    NARCIS (Netherlands)

    Elzinga, P.; Poelmans, J.; Viaene, S.; Dedene, G.; Cordeiro, J.; Filipe, J.

    2009-01-01

    Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the

  7. Interpretation of fingerprint image quality features extracted by self-organizing maps

    Science.gov (United States)

    Danov, Ivan; Olsen, Martin A.; Busch, Christoph

    2014-05-01

    Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

  8. Self-organizing map models of language acquisition

    Science.gov (United States)

    Li, Ping; Zhao, Xiaowei

    2013-01-01

    Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061

  9. Hierarchical Self Organizing Map for Novelty Detection using Mobile Robot with Robust Sensor

    International Nuclear Information System (INIS)

    Sha'abani, M N A H; Miskon, M F; Sakidin, H

    2013-01-01

    This paper presents a novelty detection method based on Self Organizing Map neural network using a mobile robot. Based on hierarchical neural network, the network is divided into three networks; position, orientation and sensor measurement network. A simulation was done to demonstrate and validate the proposed method using MobileSim. Three cases of abnormal events; new, missing and shifted objects are employed for performance evaluation. The result of detection was then filtered for false positive detection. The result shows that the inspection produced less than 2% false positive detection at high sensitivity settings

  10. A differential algebraic integration algorithm for symplectic mappings in systems with three-dimensional magnetic field

    International Nuclear Information System (INIS)

    Chang, P.; Lee, S.Y.; Yan, Y.T.

    2006-01-01

    A differential algebraic integration algorithm is developed for symplectic mapping through a three-dimensional (3-D) magnetic field. The self-consistent reference orbit in phase space is obtained by making a canonical transformation to eliminate the linear part of the Hamiltonian. Transfer maps from the entrance to the exit of any 3-D magnetic field are then obtained through slice-by-slice symplectic integration. The particle phase-space coordinates are advanced by using the integrable polynomial procedure. This algorithm is a powerful tool to attain nonlinear maps for insertion devices in synchrotron light source or complicated magnetic field in the interaction region in high energy colliders

  11. A Differential Algebraic Integration Algorithm for Symplectic Mappings in Systems with Three-Dimensional Magnetic Field

    International Nuclear Information System (INIS)

    Chang, P

    2004-01-01

    A differential algebraic integration algorithm is developed for symplectic mapping through a three-dimensional (3-D) magnetic field. The self-consistent reference orbit in phase space is obtained by making a canonical transformation to eliminate the linear part of the Hamiltonian. Transfer maps from the entrance to the exit of any 3-D magnetic field are then obtained through slice-by-slice symplectic integration. The particle phase-space coordinates are advanced by using the integrable polynomial procedure. This algorithm is a powerful tool to attain nonlinear maps for insertion devices in synchrotron light source or complicated magnetic field in the interaction region in high energy colliders

  12. Multi-SOM: an Algorithm for High-Dimensional, Small Size Datasets

    Directory of Open Access Journals (Sweden)

    Shen Lu

    2013-04-01

    Full Text Available Since it takes time to do experiments in bioinformatics, biological datasets are sometimes small but with high dimensionality. From probability theory, in order to discover knowledge from a set of data, we have to have a sufficient number of samples. Otherwise, the error bounds can become too large to be useful. For the SOM (Self- Organizing Map algorithm, the initial map is based on the training data. In order to avoid the bias caused by the insufficient training data, in this paper we present an algorithm, called Multi-SOM. Multi-SOM builds a number of small self-organizing maps, instead of just one big map. Bayesian decision theory is used to make the final decision among similar neurons on different maps. In this way, we can better ensure that we can get a real random initial weight vector set, the map size is less of consideration and errors tend to average out. In our experiments as applied to microarray datasets which are highly intense data composed of genetic related information, the precision of Multi-SOMs is 10.58% greater than SOMs, and its recall is 11.07% greater than SOMs. Thus, the Multi-SOMs algorithm is practical.

  13. A Systematic Approach to Modified BCJR MAP Algorithms for Convolutional Codes

    Directory of Open Access Journals (Sweden)

    Patenaude François

    2006-01-01

    Full Text Available Since Berrou, Glavieux and Thitimajshima published their landmark paper in 1993, different modified BCJR MAP algorithms have appeared in the literature. The existence of a relatively large number of similar but different modified BCJR MAP algorithms, derived using the Markov chain properties of convolutional codes, naturally leads to the following questions. What is the relationship among the different modified BCJR MAP algorithms? What are their relative performance, computational complexities, and memory requirements? In this paper, we answer these questions. We derive systematically four major modified BCJR MAP algorithms from the BCJR MAP algorithm using simple mathematical transformations. The connections between the original and the four modified BCJR MAP algorithms are established. A detailed analysis of the different modified BCJR MAP algorithms shows that they have identical computational complexities and memory requirements. Computer simulations demonstrate that the four modified BCJR MAP algorithms all have identical performance to the BCJR MAP algorithm.

  14. Self-organizing networks for extracting jet features

    International Nuclear Information System (INIS)

    Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.

    1991-01-01

    Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)

  15. Self-Organized Fission Control for Flocking System

    Directory of Open Access Journals (Sweden)

    Mingyong Liu

    2015-01-01

    Full Text Available This paper studies the self-organized fission control problem for flocking system. Motivated by the fission behavior of biological flocks, information coupling degree (ICD is firstly designed to represent the interaction intensity between individuals. Then, from the information transfer perspective, a “maximum-ICD” based pairwise interaction rule is proposed to realize the directional information propagation within the flock. Together with the “separation/alignment/cohesion” rules, a self-organized fission control algorithm is established that achieves the spontaneous splitting of flocking system under conflict external stimuli. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

  16. Segmentation algorithm on smartphone dual camera: application to plant organs in the wild

    Science.gov (United States)

    Bertrand, Sarah; Cerutti, Guillaume; Tougne, Laure

    2018-04-01

    In order to identify the species of a tree, the different organs that are the leaves, the bark, the flowers and the fruits, are inspected by botanists. So as to develop an algorithm that identifies automatically the species, we need to extract these objects of interest from their complex natural environment. In this article, we focus on the segmentation of flowers and fruits and we present a new method of segmentation based on an active contour algorithm using two probability maps. The first map is constructed via the dual camera that we can find on the back of the latest smartphones. The second map is made with the help of a multilayer perceptron (MLP). The combination of these two maps to drive the evolution of the object contour allows an efficient segmentation of the organ from a natural background.

  17. Segmentation of color images by chromaticity features using self-organizing maps

    Directory of Open Access Journals (Sweden)

    Farid García-Lamont

    2016-05-01

    Full Text Available Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.

  18. Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

    Directory of Open Access Journals (Sweden)

    Mike van Diest

    Full Text Available Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating exergame.Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM, an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar. Additionally a k Nearest Neighbor (kNN classifier was trained to discriminate between young and older adults based on the SOM features.Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%.Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

  19. To develop a universal gamut mapping algorithm

    International Nuclear Information System (INIS)

    Morovic, J.

    1998-10-01

    When a colour image from one colour reproduction medium (e.g. nature, a monitor) needs to be reproduced on another (e.g. on a monitor or in print) and these media have different colour ranges (gamuts), it is necessary to have a method for mapping between them. If such a gamut mapping algorithm can be used under a wide range of conditions, it can also be incorporated in an automated colour reproduction system and considered to be in some sense universal. In terms of preliminary work, a colour reproduction system was implemented, for which a new printer characterisation model (including grey-scale correction) was developed. Methods were also developed for calculating gamut boundary descriptors and for calculating gamut boundaries along given lines from them. The gamut mapping solution proposed in this thesis is a gamut compression algorithm developed with the aim of being accurate and universally applicable. It was arrived at by way of an evolutionary gamut mapping development strategy for the purposes of which five test images were reproduced between a CRT and printed media obtained using an inkjet printer. Initially, a number of previously published algorithms were chosen and psychophysically evaluated whereby an important characteristic of this evaluation was that it also considered the performance of algorithms for individual colour regions within the test images used. New algorithms were then developed on their basis, subsequently evaluated and this process was repeated once more. In this series of experiments the new GCUSP algorithm, which consists of a chroma-dependent lightness compression followed by a compression towards the lightness of the reproduction cusp on the lightness axis, gave the most accurate and stable performance overall. The results of these experiments were also useful for improving the understanding of some gamut mapping factors - in particular gamut difference. In addition to looking at accuracy, the pleasantness of reproductions obtained

  20. Improvement of algorithm using Kohonen`s self-organizing feature map for the traveling salesman problem; Kohonen jiko soshikika tokucho mappu wo mochiita ukai serusuman mondai kaiho no kairyo

    Energy Technology Data Exchange (ETDEWEB)

    Fujimura, K.; Tokutaka, H.; Tanaka, H.; Kishida, S. [Tottori Univ., Tottori (Japan); Oshima, Y. [Mita Industrial Co. Ltd., Osaka (Japan)

    1996-02-20

    Traveling salesman problem (TSP) is one of the combinatorial optimization problems. The solution of this problem is to seek the way of how to visit every city only once within the shortest traveling distance. The solutions of this problem are studied a lot hitherto since they are the index for observing the basic properties of optimization algorithm. The method of Angeniol using the elf-organizing feature map is greatly forceful from the viewpoint of its short calculating time. In this study, regarding the algorithm of Angeniol, the conditions of obtaining the shortest tour length within shorter time are examined. Namely, a half of calculating time is reduced by changing Angeniol method into the method of making the node create after the searches of M cities. Additionally, the calculating time for unchanged tour length is reduced to one fourth by adding an inertia item in accordance with the variation of the number of total nodes. 14 refs., 8 figs.

  1. Self-organizing representations

    Energy Technology Data Exchange (ETDEWEB)

    Kohonen, T.

    1983-01-01

    A property which is commonplace in the brain but which has always been ignored in learning machines is the spatial order of the processing units. This order is clearly highly significant and in nature it develops gradually during the lifetime of the organism. It then serves as the basis for perceptual and cognitive processes, and memory, too. The spatial order in biological organisms is often believed to be genetically determined. It is therefore intriguing to learn that a meaningful and optimal spatial order is formed in an extremely simple self-organizing process whereby certain feature maps are formed automatically. 8 references.

  2. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

    Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

  3. Implementation of self-organizing neural networks for visuo-motor control of an industrial robot.

    Science.gov (United States)

    Walter, J A; Schulten, K I

    1993-01-01

    The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the ;neural-gas' network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed.

  4. Algorithms for mapping high-throughput DNA sequences

    DEFF Research Database (Denmark)

    Frellsen, Jes; Menzel, Peter; Krogh, Anders

    2014-01-01

    of data generation, new bioinformatics approaches have been developed to cope with the large amount of sequencing reads obtained in these experiments. In this chapter, we first introduce HTS technologies and their usage in molecular biology and discuss the problem of mapping sequencing reads...... to their genomic origin. We then in detail describe two approaches that offer very fast heuristics to solve the mapping problem in a feasible runtime. In particular, we describe the BLAT algorithm, and we give an introduction to the Burrows-Wheeler Transform and the mapping algorithms based on this transformation....

  5. Functional grouping and establishment of distribution patterns of invasive plants in China using self-organizing maps and indicator species analysis

    Directory of Open Access Journals (Sweden)

    Wang Zi-Bo

    2009-01-01

    Full Text Available In the present study, we introduce two techniques - self-organizing maps (SOM and indicator species analysis (INDVAL - for understanding the richness patterns of invasive species. We first employed SOM to identify functional groups and then used INDVAL to identify the representative areas characterizing these functional groups. Quantitative traits and distributional information on 127 invasive plants in 28 provinces of China were collected to form the matrices for our study. The results indicate Jiangsu to be the top province with the highest number of invasive species, while Ningxia was the lowest. Six functional groups were identified by the SOM method, and five of them were found to have significantly representative provinces by the INDVAL method. Our study represents the first attempt to combine self-organizing maps and indicator species analysis to assess the macro-scale distribution of exotic species.

  6. Artificial neural network with self-organizing mapping for reactor stability monitoring

    International Nuclear Information System (INIS)

    Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

    2008-01-01

    In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

  7. FPGA implementation of self organizing map with digital phase locked loops.

    Science.gov (United States)

    Hikawa, Hiroomi

    2005-01-01

    The self-organizing map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.

  8. Expression cartography of human tissues using self organizing maps

    Directory of Open Access Journals (Sweden)

    Löffler Markus

    2011-07-01

    Full Text Available Abstract Background Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs. SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand. Results The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues. SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues

  9. Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

    Directory of Open Access Journals (Sweden)

    Meilian Xu

    2011-01-01

    architectures that exploit data parallel applications, medical imaging algorithms such as OS-SART can be studied to produce increased performance. In this paper, we map OS-SART on cell broadband engine (Cell BE. We effectively use the architectural features of Cell BE to provide an efficient mapping. The Cell BE consists of one powerPC processor element (PPE and eight SIMD coprocessors known as synergetic processor elements (SPEs. The limited memory storage on each of the SPEs makes the mapping challenging. Therefore, we present optimization techniques to efficiently map the algorithm on the Cell BE for improved performance over CPU version. We compare the performance of our proposed algorithm on Cell BE to that of Sun Fire ×4600, a shared memory machine. The Cell BE is five times faster than AMD Opteron dual-core processor. The speedup of the algorithm on Cell BE increases with the increase in the number of SPEs. We also experiment with various parameters, such as number of subsets, number of processing elements, and number of DMA transfers between main memory and local memory, that impact the performance of the algorithm.

  10. Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

    Science.gov (United States)

    Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

    2017-08-01

    This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

  11. Parallel algorithms for mapping pipelined and parallel computations

    Science.gov (United States)

    Nicol, David M.

    1988-01-01

    Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.

  12. Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

    Directory of Open Access Journals (Sweden)

    Zhicong Zhang

    2018-01-01

    Full Text Available We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

  13. Use of the self-organizing feature map to diagnose abnormal engineering change

    Science.gov (United States)

    Lu, Ruei-Shan; Wu, Zhi-Ting; Peng, Kuo-Wei; Yu, Tai-Yi

    2015-07-01

    This study established identification manners with self-organizing feature map (SOM) to achieve the goal of monitoring Engineering Change (EC) based on historical data of a company that specializes in computers and peripherals. The product life cycle of this company is 3-6 months. The historical data were divided into three parts, each covering four months. The first part, comprising 2,343 records from January to April (the training period), comprise the Control Group. The second and third parts comprise Experimental Groups (EG) 1 and 2, respectively. For EG 1 and 2, the successful rate of recognizing information on abnormal ECs was approximately 96% and 95%, respectively. This paper shows the importance and screening procedures of abnormal engineering change for a particular company specializing in computers and peripherals.

  14. Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps

    Directory of Open Access Journals (Sweden)

    Jie Yang

    2014-01-01

    Full Text Available A self-organizing feature map (SOM was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower’s velocity, relative velocity, and gap while the output signals represented the response (the follower’s acceleration. Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

  15. A learning heuristic for space mapping and searching self-organizing systems using adaptive mesh refinement

    Science.gov (United States)

    Phillips, Carolyn L.

    2014-09-01

    In a complex self-organizing system, small changes in the interactions between the system's components can result in different emergent macrostructures or macrobehavior. In chemical engineering and material science, such spontaneously self-assembling systems, using polymers, nanoscale or colloidal-scale particles, DNA, or other precursors, are an attractive way to create materials that are precisely engineered at a fine scale. Changes to the interactions can often be described by a set of parameters. Different contiguous regions in this parameter space correspond to different ordered states. Since these ordered states are emergent, often experiment, not analysis, is necessary to create a diagram of ordered states over the parameter space. By issuing queries to points in the parameter space (e.g., performing a computational or physical experiment), ordered states can be discovered and mapped. Queries can be costly in terms of resources or time, however. In general, one would like to learn the most information using the fewest queries. Here we introduce a learning heuristic for issuing queries to map and search a two-dimensional parameter space. Using a method inspired by adaptive mesh refinement, the heuristic iteratively issues batches of queries to be executed in parallel based on past information. By adjusting the search criteria, different types of searches (for example, a uniform search, exploring boundaries, sampling all regions equally) can be flexibly implemented. We show that this method will densely search the space, while preferentially targeting certain features. Using numerical examples, including a study simulating the self-assembly of complex crystals, we show how this heuristic can discover new regions and map boundaries more accurately than a uniformly distributed set of queries.

  16. Development, application and evaluation of a computational tool for management high voltage break disconnector based on self-organizing maps and image processing

    International Nuclear Information System (INIS)

    Freitas Colaco, Daniel; Alexandria, Auzuir R. de; Cortez, Paulo Cesar; Frota, Joao Batista B.; Nunes de Lima, Jose Nunes de; Albuquerque, Victor Hugo C. de

    2010-01-01

    This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.

  17. Development, application and evaluation of a computational tool for management high voltage break disconnector based on self-organizing maps and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Freitas Colaco, Daniel, E-mail: colaco@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Alexandria, Auzuir R. de, E-mail: auzuir@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Cortez, Paulo Cesar, E-mail: cortez@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Frota, Joao Batista B., E-mail: jb@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Nunes de Lima, Jose Nunes de, E-mail: josenl@chesf.gov.b [Companhia Hidro Eletrica do Sao Francisco (CHESF), Rua Delmiro Gouveia, 333, 50761-901 Recife, Pernambuco (Brazil); Albuquerque, Victor Hugo C. de, E-mail: victor.albuquerque@fe.up.p [Universidade de Fortaleza (UNIFOR), Centro de Ciencias Tecnologicas (CCT), Nucleo de Pesquisas Tecnologicas - NPT, Av. Washington Soares, 1321, Sala NPT/CCT, CEP 60.811-905, Edson Queiroz (Brazil); Universidade Federal da Paraiba (UFPB), Departamento de Engenharia Mecanica (DEM), Cidade Universitaria, S/N, 58059-900 Joao Pessoa, Paraiba (Brazil)

    2010-11-15

    This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.

  18. APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

    Directory of Open Access Journals (Sweden)

    Khuat Thanh Tung

    2016-11-01

    Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.

  19. Identifying regions of interest in medical images using self-organizing maps.

    Science.gov (United States)

    Teng, Wei-Guang; Chang, Ping-Lin

    2012-10-01

    Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

  20. CUSTOMER SEGMENTATION DENGAN METODE SELF ORGANIZING MAP (STUDI KASUS: UD. FENNY

    Directory of Open Access Journals (Sweden)

    A. A. Gde Bagus Ariana

    2012-11-01

    Full Text Available Saat ini persaingan bisnis pada perusahaan retail tidak hanya dengan menggunakan perangkat sistem informasi namun sudah dilengkapi dengan sistem pendukung keputusan. Salah satu metode sistem pendukung keputusan yang digunakan adalah data mining. Data mining digunakan untuk menemukan pola-pola yang tersembunyi pada database. UD. Fenny sebagai perusahaan retail ingin menemukan pola segmentasi pelanggan dengan menggunakan model RFM (Recency, Frequency, Monetary. Metode data mining untuk melakukan proses segmentasi adalah metode clustering. Clustering merupakan proses penggugusan data menjadi kelompok-kelompok yang memiliki kemiripan secara tidak terawasi (unsupervised. Sebelum melakukan proses clustering, dilakukan proses persiapan data dengan membuat datawarehouse menggunakan skema bintang (star scema. Selanjutnya dilakukan proses clustering dengan menggunakan metode Self Organizing Map (SOM/Kohonen. Metode ini merupakan salah satu model jaringan saraf tiruan yang menggunakan metode unsupervised. Dari hasil percobaan metode SOM melakukan proses clustering dan menggambarkan hasil clustering pada SOM plot. Dengan melakukan proses clustering, pihak pengambil keputusan dapat memahami segmentasi customer dan melakukan upaya peningkatan pelayanan customer.

  1. Self-Organizing Maps on the Cell Broadband Engine Architecture

    International Nuclear Information System (INIS)

    McConnell, Sabine M

    2010-01-01

    We present and evaluate novel parallel implementations of Self-Organizing Maps for the Cell Broadband Engine Architecture. Motivated by the interactive nature of the data-mining process, we evaluate the scalability of the implementations on two clusters using different network characteristics and incarnations (PS3 TM console and PowerXCell 8i) of the architecture. Our implementations use varying combinations of the Power Processing Elements (PPEs) and Synergistic Processing Elements (SPEs) found in the Cell architecture. For a single processor, our implementation scaled well with the number of SPEs regardless of the incarnation. When combining multiple PS3 TM consoles, the synchronization over the slower network resulted in poor speedups and demonstrated that the use of such a low-cost cluster may be severely restricted, even without the use of SPEs. When using multiple SPEs for the PowerXCell 8i cluster, the speedup grew linearly with increasing number of SPEs for a given number of processors, and linear up to a maximum with the number of processors for a given number of SPEs. Our implementation achieved a worst-case efficiency of 67% for the maximum number of processing elements involved in the computation, but consistently higher values for smaller numbers of processing elements with speedups of up to 70.

  2. Space mapping optimization algorithms for engineering design

    DEFF Research Database (Denmark)

    Koziel, Slawomir; Bandler, John W.; Madsen, Kaj

    2006-01-01

    A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first...... to a benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of coupled-line band-pass filter....

  3. Mapping robust parallel multigrid algorithms to scalable memory architectures

    Science.gov (United States)

    Overman, Andrea; Vanrosendale, John

    1993-01-01

    The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids or anisotropic operators. The usual cure for this is the use of line or plane relaxation. However, multigrid algorithms based on line and plane relaxation have limited and awkward parallelism and are quite difficult to map effectively to highly parallel architectures. Newer multigrid algorithms that overcome anisotropy through the use of multiple coarse grids rather than relaxation are better suited to massively parallel architectures because they require only simple point-relaxation smoothers. In this paper, we look at the parallel implementation of a V-cycle multiple semicoarsened grid (MSG) algorithm on distributed-memory architectures such as the Intel iPSC/860 and Paragon computers. The MSG algorithms provide two levels of parallelism: parallelism within the relaxation or interpolation on each grid and across the grids on each multigrid level. Both levels of parallelism must be exploited to map these algorithms effectively to parallel architectures. This paper describes a mapping of an MSG algorithm to distributed-memory architectures that demonstrates how both levels of parallelism can be exploited. The result is a robust and effective multigrid algorithm for distributed-memory machines.

  4. Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

    Science.gov (United States)

    Cattinelli, Isabella; Bolzoni, Elena; Barbieri, Carlo; Mari, Flavio; Martin-Guerrero, José David; Soria-Olivas, Emilio; Martinez-Martinez, José Maria; Gomez-Sanchis, Juan; Amato, Claudia; Stopper, Andrea; Gatti, Emanuele

    2012-03-01

    The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.

  5. Self-organized neural network for the quality control of 12-lead ECG signals

    International Nuclear Information System (INIS)

    Chen, Yun; Yang, Hui

    2012-01-01

    Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

  6. A fast image encryption algorithm based on chaotic map

    Science.gov (United States)

    Liu, Wenhao; Sun, Kehui; Zhu, Congxu

    2016-09-01

    Derived from Sine map and iterative chaotic map with infinite collapse (ICMIC), a new two-dimensional Sine ICMIC modulation map (2D-SIMM) is proposed based on a close-loop modulation coupling (CMC) model, and its chaotic performance is analyzed by means of phase diagram, Lyapunov exponent spectrum and complexity. It shows that this map has good ergodicity, hyperchaotic behavior, large maximum Lyapunov exponent and high complexity. Based on this map, a fast image encryption algorithm is proposed. In this algorithm, the confusion and diffusion processes are combined for one stage. Chaotic shift transform (CST) is proposed to efficiently change the image pixel positions, and the row and column substitutions are applied to scramble the pixel values simultaneously. The simulation and analysis results show that this algorithm has high security, low time complexity, and the abilities of resisting statistical analysis, differential, brute-force, known-plaintext and chosen-plaintext attacks.

  7. Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

    Directory of Open Access Journals (Sweden)

    Kwang Baek Kim

    2015-01-01

    Full Text Available Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

  8. Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game

    OpenAIRE

    Lara-Cabrera, Raúl; Cotta, Carlos; Fernández Leiva, Antonio J.

    2013-01-01

    This work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based ...

  9. An algorithm for symplectic implicit Taylor-map tracking

    International Nuclear Information System (INIS)

    Yan, Y.; Channell, P.; Syphers, M.

    1992-10-01

    An algorithm has been developed for converting an ''order-by-order symplectic'' Taylor map that is truncated to an arbitrary order (thus not exactly symplectic) into a Courant-Snyder matrix and a symplectic implicit Taylor map for symplectic tracking. This algorithm is implemented using differential algebras, and it is numerically stable and fast. Thus, lifetime charged-particle tracking for large hadron colliders, such as the Superconducting Super Collider, is now made possible

  10. The linkage between the lifestyle of knowledge-workers and their intra-metropolitan residential choice: A clustering approach based on self-organizing maps

    DEFF Research Database (Denmark)

    Frenkel, Amnon; Bendit, Edward; Kaplan, Sigal

    2013-01-01

    -Aviv metropolitan area and are analyzed with self-organizing maps for pattern recognition and classification. Five clusters are identified: nest-builders, bon-vivants, careerists, entrepreneurs and laid-back. Bon-vivants and entrepreneurs differ in their dwelling size and home-ownership, although both prefer...

  11. Characterizing synoptic and cloud variability in the northern atlantic using self-organizing maps

    Science.gov (United States)

    Fish, Carly

    Low-level clouds have a significant influence on the Earth's radiation budget and it is thus imperative to understand their behavior within the marine boundary layer (MBL). The cloud properties in the Northeast Atlantic region are highly variable in space and time and are a research focus for many atmospheric scientists. Characterizing the synoptic patterns in the region through the implementation of self-organizing maps (SOMs) enables a climatological grasp of cloud and atmospheric fields. ERA -- Interim and MODIS provide the platform to explore the variability in the Northeast Atlantic for over 30 years of data. Station data comes from CAP -- MBL on Graciosa Island in the Azores, which lies in a strong gradient of cloud and other atmospheric fields, offer an opportunity to incorporate an observational aspect for the years of 2009 and 2010.

  12. Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

    Science.gov (United States)

    Zhang, WenJun

    2007-07-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

  13. Feature-based alert correlation in security systems using self organizing maps

    Science.gov (United States)

    Kumar, Munesh; Siddique, Shoaib; Noor, Humera

    2009-04-01

    The security of the networks has been an important concern for any organization. This is especially important for the defense sector as to get unauthorized access to the sensitive information of an organization has been the prime desire for cyber criminals. Many network security techniques like Firewall, VPN Concentrator etc. are deployed at the perimeter of network to deal with attack(s) that occur(s) from exterior of network. But any vulnerability that causes to penetrate the network's perimeter of defense, can exploit the entire network. To deal with such vulnerabilities a system has been evolved with the purpose of generating an alert for any malicious activity triggered against the network and its resources, termed as Intrusion Detection System (IDS). The traditional IDS have still some deficiencies like generating large number of alerts, containing both true and false one etc. By automatically classifying (correlating) various alerts, the high-level analysis of the security status of network can be identified and the job of network security administrator becomes much easier. In this paper we propose to utilize Self Organizing Maps (SOM); an Artificial Neural Network for correlating large amount of logged intrusion alerts based on generic features such as Source/Destination IP Addresses, Port No, Signature ID etc. The different ways in which alerts can be correlated by Artificial Intelligence techniques are also discussed. . We've shown that the strategy described in the paper improves the efficiency of IDS by better correlating the alerts, leading to reduced false positives and increased competence of network administrator.

  14. Algorithmic chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Fontana, W.

    1990-12-13

    In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.

  15. Fault detection of sensors in nuclear reactors using self-organizing maps

    Energy Technology Data Exchange (ETDEWEB)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Guarulhos, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  16. Artificial neural network with self-organizing mapping for reactor stability monitoring

    International Nuclear Information System (INIS)

    Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

    2009-01-01

    In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

  17. Fault detection of sensors in nuclear reactors using self-organizing maps

    International Nuclear Information System (INIS)

    Barbosa, Paulo Roberto; Tiago, Graziela Marchi; Bueno, Elaine Inacio; Pereira, Iraci Martinez

    2011-01-01

    In this work a Fault Detection System was developed based on the self-organizing maps methodology. This method was applied to the IEA-R1 research reactor at IPEN using a database generated by a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab Guide toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. In order to test the model ability for fault detection, faults were artificially produced. As the value of the maximum calibration error for special thermocouples is +- 0.5 deg C, it had been inserted faults in the sensor signals with the purpose to produce the database considered in this work. The results show a high percentage of correct classification, encouraging the use of the technique for this type of industrial application. (author)

  18. Tropospheric Ozonesonde Profiles at Long-term U.S. Monitoring Sites: 1. A Climatology Based on Self-Organizing Maps

    Science.gov (United States)

    Stauffer, Ryan M.; Thompson, Anne M.; Young, George S.

    2016-01-01

    Sonde-based climatologies of tropospheric ozone (O3) are vital for developing satellite retrieval algorithms and evaluating chemical transport model output. Typical O3 climatologies average measurements by latitude or region, and season. A recent analysis using self-organizing maps (SOM) to cluster ozonesondes from two tropical sites found that clusters of O3 mixing ratio profiles are an excellent way to capture O3variability and link meteorological influences to O3 profiles. Clusters correspond to distinct meteorological conditions, e.g., convection, subsidence, cloud cover, and transported pollution. Here the SOM technique is extended to four long-term U.S. sites (Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA) with4530 total profiles. Sensitivity tests on k-means algorithm and SOM justify use of 3 3 SOM (nine clusters). Ateach site, SOM clusters together O3 profiles with similar tropopause height, 500 hPa height temperature, and amount of tropospheric and total column O3. Cluster means are compared to monthly O3 climatologies.For all four sites, near-tropopause O3 is double (over +100 parts per billion by volume; ppbv) the monthly climatological O3 mixing ratio in three clusters that contain 1316 of profiles, mostly in winter and spring.Large midtropospheric deviations from monthly means (6 ppbv, +710 ppbv O3 at 6 km) are found in two of the most populated clusters (combined 3639 of profiles). These two clusters contain distinctly polluted(summer) and clean O3 (fall-winter, high tropopause) profiles, respectively. As for tropical profiles previously analyzed with SOM, O3 averages are often poor representations of U.S. O3 profile statistics.

  19. Development of test algorithm for semiconductor package with defects by using probabilistic neural network

    International Nuclear Information System (INIS)

    Kim, Jae Yeol; Sim, Jae Gi; Ko, Myoung Soo; Kim, Chang Hyun; Kim, Hun Cho

    2001-01-01

    In this study, researchers developing the estimative algorithm for artificial defects in semiconductor packages and performing it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Probabilistic Neural Network. Self-Organizing Map and Probabilistic Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages. This study presumes probability density function from a sample of learning and present which is automatically determine method. PNN can distinguish flaws very difficult distinction as well as. This can do parallel process to stand in a row we confirm that is very efficiently classifier if we applied many data real the process.

  20. A Fast Map Merging Algorithm in the Field of Multirobot SLAM

    Directory of Open Access Journals (Sweden)

    Yanli Liu

    2013-01-01

    Full Text Available In recent years, the research on single-robot simultaneous localization and mapping (SLAM has made a great success. However, multirobot SLAM faces many challenging problems, including unknown robot poses, unshared map, and unstable communication. In this paper, a map merging algorithm based on virtual robot motion is proposed for multi-robot SLAM. The thinning algorithm is used to construct the skeleton of the grid map’s empty area, and a mobile robot is simulated in one map. The simulated data is used as information sources in the other map to do partial map Monte Carlo localization; if localization succeeds, the relative pose hypotheses between the two maps can be computed easily. We verify these hypotheses using the rendezvous technique and use them as initial values to optimize the estimation by a heuristic random search algorithm.

  1. Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation

    Directory of Open Access Journals (Sweden)

    Hong SeungHo

    2011-01-01

    Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.

  2. New segmentation-based tone mapping algorithm for high dynamic range image

    Science.gov (United States)

    Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

    2017-07-01

    The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

  3. Segmentation of head magnetic resonance image using self-mapping characteristic

    International Nuclear Information System (INIS)

    Madokoro, Hirokazu; Sato, Kazuhito; Ishii, Masaki; Kadowaki, Sakura

    2004-01-01

    In this paper, we proposed a segmentation method, for head magnetic resonance (MR) images. Our method used self mapping characteristic of a self-organization map (SOM), and it does not need the setting of the representative point by the operator. We considered the continuity and boundary in the brain tissues by the definition of the local block. In the evaluation experiment, we obtained the segmentation result of matching anatomical structure information. In addition, our method applied the clinical MR images, it was possible to obtain the effective and objective result for supporting the diagnosis of the brain atrophy by the doctor. (author)

  4. Hungarian contribution to the Global Soil Organic Carbon Map (GSOC17) using advanced machine learning algorithms and geostatistics

    Science.gov (United States)

    Szatmári, Gábor; Laborczi, Annamária; Takács, Katalin; Pásztor, László

    2017-04-01

    The knowledge about soil organic carbon (SOC) baselines and changes, and the detection of vulnerable hot spots for SOC losses and gains under climate change and changed land management is still fairly limited. Thus Global Soil Partnership (GSP) has been requested to develop a global SOC mapping campaign by 2017. GSPs concept builds on official national data sets, therefore, a bottom-up (country-driven) approach is pursued. The elaborated Hungarian methodology suits the general specifications of GSOC17 provided by GSP. The input data for GSOC17@HU mapping approach has involved legacy soil data bases, as well as proper environmental covariates related to the main soil forming factors, such as climate, organisms, relief and parent material. Nowadays, digital soil mapping (DSM) highly relies on the assumption that soil properties of interest can be modelled as a sum of a deterministic and stochastic component, which can be treated and modelled separately. We also adopted this assumption in our methodology. In practice, multiple regression techniques are commonly used to model the deterministic part. However, this global (and usually linear) models commonly oversimplify the often complex and non-linear relationship, which has a crucial effect on the resulted soil maps. Thus, we integrated machine learning algorithms (namely random forest and quantile regression forest) in the elaborated methodology, supposing then to be more suitable for the problem in hand. This approach has enable us to model the GSOC17 soil properties in that complex and non-linear forms as the soil itself. Furthermore, it has enable us to model and assess the uncertainty of the results, which is highly relevant in decision making. The applied methodology has used geostatistical approach to model the stochastic part of the spatial variability of the soil properties of interest. We created GSOC17@HU map with 1 km grid resolution according to the GSPs specifications. The map contributes to the GSPs

  5. Accuracy of vaginal symptom self-diagnosis algorithms for deployed military women.

    Science.gov (United States)

    Ryan-Wenger, Nancy A; Neal, Jeremy L; Jones, Ashley S; Lowe, Nancy K

    2010-01-01

    Deployed military women have an increased risk for development of vaginitis due to extreme temperatures, primitive sanitation, hygiene and laundry facilities, and unavailable or unacceptable healthcare resources. The Women in the Military Self-Diagnosis (WMSD) and treatment kit was developed as a field-expedient solution to this problem. The primary study aims were to evaluate the accuracy of women's self-diagnosis of vaginal symptoms and eight diagnostic algorithms and to predict potential self-medication omission and commission error rates. Participants included 546 active duty, deployable Army (43.3%) and Navy (53.6%) women with vaginal symptoms who sought healthcare at troop medical clinics on base.In the clinic lavatory, women conducted a self-diagnosis using a sterile cotton swab to obtain vaginal fluid, a FemExam card to measure positive or negative pH and amines, and the investigator-developed WMSD Decision-Making Guide. Potential self-diagnoses were "bacterial infection" (bacterial vaginosis [BV] and/or trichomonas vaginitis [TV]), "yeast infection" (candida vaginitis [CV]), "no infection/normal," or "unclear." The Affirm VPIII laboratory reference standard was used to detect clinically significant amounts of vaginal fluid DNA for organisms associated with BV, TV, and CV. Women's self-diagnostic accuracy was 56% for BV/TV and 69.2% for CV. False-positives would have led to a self-medication commission error rate of 20.3% for BV/TV and 8% for CV. Potential self-medication omission error rates due to false-negatives were 23.7% for BV/TV and 24.8% for CV. The positive predictive value of diagnostic algorithms ranged from 0% to 78.1% for BV/TV and 41.7% for CV. The algorithms were based on clinical diagnostic standards. The nonspecific nature of vaginal symptoms, mixed infections, and a faulty device intended to measure vaginal pH and amines explain why none of the algorithms reached the goal of 95% accuracy. The next prototype of the WMSD kit will not include

  6. Algorithmic Self

    DEFF Research Database (Denmark)

    Markham, Annette

    This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....

  7. ReactionMap: an efficient atom-mapping algorithm for chemical reactions.

    Science.gov (United States)

    Fooshee, David; Andronico, Alessio; Baldi, Pierre

    2013-11-25

    Large databases of chemical reactions provide new data-mining opportunities and challenges. Key challenges result from the imperfect quality of the data and the fact that many of these reactions are not properly balanced or atom-mapped. Here, we describe ReactionMap, an efficient atom-mapping algorithm. Our approach uses a combination of maximum common chemical subgraph search and minimization of an assignment cost function derived empirically from training data. We use a set of over 259,000 balanced atom-mapped reactions from the SPRESI commercial database to train the system, and we validate it on random sets of 1000 and 17,996 reactions sampled from this pool. These large test sets represent a broad range of chemical reaction types, and ReactionMap correctly maps about 99% of the atoms and about 96% of the reactions, with a mean time per mapping of 2 s. Most correctly mapped reactions are mapped with high confidence. Mapping accuracy compares favorably with ChemAxon's AutoMapper, versions 5 and 6.1, and the DREAM Web tool. These approaches correctly map 60.7%, 86.5%, and 90.3% of the reactions, respectively, on the same data set. A ReactionMap server is available on the ChemDB Web portal at http://cdb.ics.uci.edu .

  8. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

    Science.gov (United States)

    Kamimura, Ryotaro

    2014-01-01

    We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

  9. Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Ryotaro Kamimura

    2014-01-01

    Full Text Available We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

  10. Tensor SOM and tensor GTM: Nonlinear tensor analysis by topographic mappings.

    Science.gov (United States)

    Iwasaki, Tohru; Furukawa, Tetsuo

    2016-05-01

    In this paper, we propose nonlinear tensor analysis methods: the tensor self-organizing map (TSOM) and the tensor generative topographic mapping (TGTM). TSOM is a straightforward extension of the self-organizing map from high-dimensional data to tensorial data, and TGTM is an extension of the generative topographic map, which provides a theoretical background for TSOM using a probabilistic generative model. These methods are useful tools for analyzing and visualizing tensorial data, especially multimodal relational data. For given n-mode relational data, TSOM and TGTM can simultaneously organize a set of n-topographic maps. Furthermore, they can be used to explore the tensorial data space by interactively visualizing the relationships between modes. We present the TSOM algorithm and a theoretical description from the viewpoint of TGTM. Various TSOM variations and visualization techniques are also described, along with some applications to real relational datasets. Additionally, we attempt to build a comprehensive description of the TSOM family by adapting various data structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Analyzing the effectiveness of flare dispensing programs against pulse width modulation seekers using self-organizing maps

    Science.gov (United States)

    Şahingil, Mehmet C.; Aslan, Murat Š.

    2013-10-01

    Infrared guided missile seekers utilizing pulse width modulation in target tracking is one of the threats against air platforms. To be able to achieve a "soft-kill" protection of own platform against these type of threats, one needs to examine carefully the seeker operating principle with its special electronic counter-counter measure (ECCM) capability. One of the cost-effective ways of soft kill protection is to use flare decoys in accordance with an optimized dispensing program. Such an optimization requires a good understanding of the threat seeker, capabilities of the air platform and engagement scenario information between them. Modeling and simulation is very powerful tool to achieve a valuable insight and understand the underlying phenomenology. A careful interpretation of simulation results is crucial to infer valuable conclusions from the data. In such an interpretation there are lots of factors (features) which affect the results. Therefore, powerful statistical tools and pattern recognition algorithms are of special interest in the analysis. In this paper, we show how self-organizing maps (SOMs), which is one of those powerful tools, can be used in analyzing the effectiveness of various flare dispensing programs against a PWM seeker. We perform several Monte Carlo runs for a typical engagement scenario in a MATLAB-based simulation environment. In each run, we randomly change the flare dispending program and obtain corresponding class: "successful" or "unsuccessful", depending on whether the corresponding flare dispensing program deceives the seeker or not, respectively. Then, in the analysis phase, we use SOMs to interpret and visualize the results.

  12. Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

    Directory of Open Access Journals (Sweden)

    Veldin A. Talorete Jr.

    2017-03-01

    Full Text Available This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifier is in charge of classifying the detected subject’s posture patterns from subject’s silhouette. Moreover, the Intruder Localization Classifier is in charge of classifying the detected pattern’s location classifier will estimate the location of the intruder with respect to the fence using geometric feature from images as inputs. The system is capable of activating the alarm, display the actual image and depict the location of the intruder when an intruder is detected. In detecting intruder posture, the system’s success rate of 88%. Overall system accuracy for day-time intruder localization is 83% and an accuracy of 88% for night-time intruder localization

  13. Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Francesco Di Maio

    2017-01-01

    Full Text Available Integrated Deterministic and Probabilistic Safety Analysis (IDPSA of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs, and Prime Implicants (PIs. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers, for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG.

  14. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment

    International Nuclear Information System (INIS)

    Jiang Chuanwen; Bompard, Etorre

    2005-01-01

    This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm

  15. Unsupervised learning via self-organization a dynamic approach

    CERN Document Server

    Kyan, Matthew; Jarrah, Kambiz; Guan, Ling

    2014-01-01

    To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

  16. Self-organizing maps applied to two-phase flow on natural circulation loop studies

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Leonardo F.; Cunha, Kelly de P.; Andrade, Delvonei A.; Sabundjian, Gaiane; Torres, Walmir M.; Macedo, Luiz A.; Rocha, Marcelo da S.; Masotti, Paulo H.F.; Mesquita, Roberto N. de, E-mail: rnavarro@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    Two-phase flow of liquid and gas is found in many closed circuits using natural circulation for cooling purposes. Natural circulation phenomenon is important on recent nuclear power plant projects for heat removal on 'loss of pump power' or 'plant shutdown' accidents. The accuracy of heat transfer estimation has been improved based on models that require precise prediction of pattern transitions of flow. Self-Organizing Maps are trained to digital images acquired on natural circulation flow instabilities. This technique will allow the selection of the more important characteristics associated with each flow pattern, enabling a better comprehension of each observed instability. This periodic flow oscillation behavior can be observed thoroughly in this facility due its glass-made tubes transparency. The Natural Circulation Facility (Circuito de Circulacao Natural - CCN) installed at Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN, is an experimental circuit designed to provide thermal hydraulic data related to one and two phase flow under natural circulation conditions. (author)

  17. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  18. Segmentation of Natural Gas Customers in Industrial Sector Using Self-Organizing Map (SOM) Method

    Science.gov (United States)

    Masbar Rus, A. M.; Pramudita, R.; Surjandari, I.

    2018-03-01

    The usage of the natural gas which is non-renewable energy, needs to be more efficient. Therefore, customer segmentation becomes necessary to set up a marketing strategy to be right on target or to determine an appropriate fee. This research was conducted at PT PGN using one of data mining method, i.e. Self-Organizing Map (SOM). The clustering process is based on the characteristic of its customers as a reference to create the customer segmentation of natural gas customers. The input variables of this research are variable of area, type of customer, the industrial sector, the average usage, standard deviation of the usage, and the total deviation. As a result, 37 cluster and 9 segment from 838 customer data are formed. These 9 segments then employed to illustrate the general characteristic of the natural gas customer of PT PGN.

  19. Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography

    International Nuclear Information System (INIS)

    Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M.

    2005-01-01

    Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

  20. Model-driven product line engineering for mapping parallel algorithms to parallel computing platforms

    NARCIS (Netherlands)

    Arkin, Ethem; Tekinerdogan, Bedir

    2016-01-01

    Mapping parallel algorithms to parallel computing platforms requires several activities such as the analysis of the parallel algorithm, the definition of the logical configuration of the platform, the mapping of the algorithm to the logical configuration platform and the implementation of the

  1. Logical NAND and NOR Operations Using Algorithmic Self-assembly of DNA Molecules

    Science.gov (United States)

    Wang, Yanfeng; Cui, Guangzhao; Zhang, Xuncai; Zheng, Yan

    DNA self-assembly is the most advanced and versatile system that has been experimentally demonstrated for programmable construction of patterned systems on the molecular scale. It has been demonstrated that the simple binary arithmetic and logical operations can be computed by the process of self assembly of DNA tiles. Here we report a one-dimensional algorithmic self-assembly of DNA triple-crossover molecules that can be used to execute five steps of a logical NAND and NOR operations on a string of binary bits. To achieve this, abstract tiles were translated into DNA tiles based on triple-crossover motifs. Serving as input for the computation, long single stranded DNA molecules were used to nucleate growth of tiles into algorithmic crystals. Our method shows that engineered DNA self-assembly can be treated as a bottom-up design techniques, and can be capable of designing DNA computer organization and architecture.

  2. Performance quantification of clustering algorithms for false positive removal in fMRI by ROC curves

    Directory of Open Access Journals (Sweden)

    André Salles Cunha Peres

    Full Text Available Abstract Introduction Functional magnetic resonance imaging (fMRI is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number; thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.

  3. Algorithms and Complexity Results for Genome Mapping Problems.

    Science.gov (United States)

    Rajaraman, Ashok; Zanetti, Joao Paulo Pereira; Manuch, Jan; Chauve, Cedric

    2017-01-01

    Genome mapping algorithms aim at computing an ordering of a set of genomic markers based on local ordering information such as adjacencies and intervals of markers. In most genome mapping models, markers are assumed to occur uniquely in the resulting map. We introduce algorithmic questions that consider repeats, i.e., markers that can have several occurrences in the resulting map. We show that, provided with an upper bound on the copy number of repeated markers and with intervals that span full repeat copies, called repeat spanning intervals, the problem of deciding if a set of adjacencies and repeat spanning intervals admits a genome representation is tractable if the target genome can contain linear and/or circular chromosomal fragments. We also show that extracting a maximum cardinality or weight subset of repeat spanning intervals given a set of adjacencies that admits a genome realization is NP-hard but fixed-parameter tractable in the maximum copy number and the number of adjacent repeats, and tractable if intervals contain a single repeated marker.

  4. Parallel pipeline algorithm of real time star map preprocessing

    Science.gov (United States)

    Wang, Hai-yong; Qin, Tian-mu; Liu, Jia-qi; Li, Zhi-feng; Li, Jian-hua

    2016-03-01

    To improve the preprocessing speed of star map and reduce the resource consumption of embedded system of star tracker, a parallel pipeline real-time preprocessing algorithm is presented. The two characteristics, the mean and the noise standard deviation of the background gray of a star map, are firstly obtained dynamically by the means that the intervene of the star image itself to the background is removed in advance. The criterion on whether or not the following noise filtering is needed is established, then the extraction threshold value is assigned according to the level of background noise, so that the centroiding accuracy is guaranteed. In the processing algorithm, as low as two lines of pixel data are buffered, and only 100 shift registers are used to record the connected domain label, by which the problems of resources wasting and connected domain overflow are solved. The simulating results show that the necessary data of the selected bright stars could be immediately accessed in a delay time as short as 10us after the pipeline processing of a 496×496 star map in 50Mb/s is finished, and the needed memory and registers resource total less than 80kb. To verify the accuracy performance of the algorithm proposed, different levels of background noise are added to the processed ideal star map, and the statistic centroiding error is smaller than 1/23 pixel under the condition that the signal to noise ratio is greater than 1. The parallel pipeline algorithm of real time star map preprocessing helps to increase the data output speed and the anti-dynamic performance of star tracker.

  5. Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.

    Science.gov (United States)

    Giridhar, K.

    The transfer of information through a communication medium invariably results in various kinds of distortion to the transmitted signal. In this dissertation, a feed -forward neural network-based equalizer, and a family of maximum a posteriori (MAP) symbol detectors are proposed for signal recovery in the presence of intersymbol interference (ISI) and additive white Gaussian noise. The proposed neural network-based equalizer employs a novel bit-mapping strategy to handle multilevel data signals in an equivalent bipolar representation. It uses a training procedure to learn the channel characteristics, and at the end of training, the multilevel symbols are recovered from the corresponding inverse bit-mapping. When the channel characteristics are unknown and no training sequences are available, blind estimation of the channel (or its inverse) and simultaneous data recovery is required. Convergence properties of several existing Bussgang-type blind equalization algorithms are studied through computer simulations, and a unique gain independent approach is used to obtain a fair comparison of their rates of convergence. Although simple to implement, the slow convergence of these Bussgang-type blind equalizers make them unsuitable for many high data-rate applications. Rapidly converging blind algorithms based on the principle of MAP symbol-by -symbol detection are proposed, which adaptively estimate the channel impulse response (CIR) and simultaneously decode the received data sequence. Assuming a linear and Gaussian measurement model, the near-optimal blind MAP symbol detector (MAPSD) consists of a parallel bank of conditional Kalman channel estimators, where the conditioning is done on each possible data subsequence that can convolve with the CIR. This algorithm is also extended to the recovery of convolutionally encoded waveforms in the presence of ISI. Since the complexity of the MAPSD algorithm increases exponentially with the length of the assumed CIR, a suboptimal

  6. An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps. Designing the outlier analysis software package for the next Gaia survey

    Science.gov (United States)

    Fustes, D.; Manteiga, M.; Dafonte, C.; Arcay, B.; Ulla, A.; Smith, K.; Borrachero, R.; Sordo, R.

    2013-11-01

    Aims: A new method applied to the segmentation and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia satellite Data Processing and Analysis Consortium (DPAC) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in final Gaia archive. Methods: Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. Self-organizing maps networks are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized. Results: We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as "UNKNOWN". First, our method can help distinguish between astrophysical objects and instrumental artifacts. Additionally, the application of our algorithm to SDSS objects of unknown nature has allowed us to identify classes of objects with similar astrophysical natures. In addition, the method allows for the potential discovery of hundreds of new objects, such as white dwarfs and quasars. Therefore, the proposed method is shown to be very promising for data exploration and knowledge discovery in very large astronomical databases, such as the archive from the upcoming Gaia mission.

  7. A novel image encryption algorithm based on a 3D chaotic map

    Science.gov (United States)

    Kanso, A.; Ghebleh, M.

    2012-07-01

    Recently [Solak E, Çokal C, Yildiz OT Biyikoǧlu T. Cryptanalysis of Fridrich's chaotic image encryption. Int J Bifur Chaos 2010;20:1405-1413] cryptanalyzed the chaotic image encryption algorithm of [Fridrich J. Symmetric ciphers based on two-dimensional chaotic maps. Int J Bifur Chaos 1998;8(6):1259-1284], which was considered a benchmark for measuring security of many image encryption algorithms. This attack can also be applied to other encryption algorithms that have a structure similar to Fridrich's algorithm, such as that of [Chen G, Mao Y, Chui, C. A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Soliton Fract 2004;21:749-761]. In this paper, we suggest a novel image encryption algorithm based on a three dimensional (3D) chaotic map that can defeat the aforementioned attack among other existing attacks. The design of the proposed algorithm is simple and efficient, and based on three phases which provide the necessary properties for a secure image encryption algorithm including the confusion and diffusion properties. In phase I, the image pixels are shuffled according to a search rule based on the 3D chaotic map. In phases II and III, 3D chaotic maps are used to scramble shuffled pixels through mixing and masking rules, respectively. Simulation results show that the suggested algorithm satisfies the required performance tests such as high level security, large key space and acceptable encryption speed. These characteristics make it a suitable candidate for use in cryptographic applications.

  8. A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    A. S. Raja

    2012-08-01

    Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

  9. The self-organizing map, a new approach to apprehend the Madden–Julian Oscillation influence on the intraseasonal variability of rainfall in the southern African region

    CSIR Research Space (South Africa)

    Oettli, P

    2013-11-01

    Full Text Available -linear classification method, the self-organizing map (SOM), a type of artificial neural network used to produce a low-dimensional representation of high-dimensional datasets, to capture more accurately the life cycle of the MJO and its global impacts...

  10. Optimization of Antennas using a Hybrid Genetic-Algorithm Space-Mapping Algorithm

    DEFF Research Database (Denmark)

    Pantoja, M.F.; Bretones, A.R.; Meincke, Peter

    2006-01-01

    A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a Genetic Algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...

  11. Reaction-Map of Organic Chemistry

    Science.gov (United States)

    Murov, Steven

    2007-01-01

    The Reaction-Map of Organic Chemistry lists all the most commonly studied reactions in organic chemistry on one page. The discussed Reaction-Map will act as another learning aide for the students, making the study of organic chemistry much easier.

  12. Redox potential distribution of an organic-rich contaminated site obtained by the inversion of self-potential data

    Science.gov (United States)

    Abbas, M.; Jardani, A.; Soueid Ahmed, A.; Revil, A.; Brigaud, L.; Bégassat, Ph.; Dupont, J. P.

    2017-11-01

    Mapping the redox potential of shallow aquifers impacted by hydrocarbon contaminant plumes is important for the characterization and remediation of such contaminated sites. The redox potential of groundwater is indicative of the biodegradation of hydrocarbons and is important in delineating the shapes of contaminant plumes. The self-potential method was used to reconstruct the redox potential of groundwater associated with an organic-rich contaminant plume in northern France. The self-potential technique is a passive technique consisting in recording the electrical potential distribution at the surface of the Earth. A self-potential map is essentially the sum of two contributions, one associated with groundwater flow referred to as the electrokinetic component, and one associated with redox potential anomalies referred to as the electroredox component (thermoelectric and diffusion potentials are generally negligible). A groundwater flow model was first used to remove the electrokinetic component from the observed self-potential data. Then, a residual self-potential map was obtained. The source current density generating the residual self-potential signals is assumed to be associated with the position of the water table, an interface characterized by a change in both the electrical conductivity and the redox potential. The source current density was obtained through an inverse problem by minimizing a cost function including a data misfit contribution and a regularizer. This inversion algorithm allows the determination of the vertical and horizontal components of the source current density taking into account the electrical conductivity distribution of the saturated and non-saturated zones obtained independently by electrical resistivity tomography. The redox potential distribution was finally determined from the inverted residual source current density. A redox map was successfully built and the estimated redox potential values correlated well with in

  13. An image-space parallel convolution filtering algorithm based on shadow map

    Science.gov (United States)

    Li, Hua; Yang, Huamin; Zhao, Jianping

    2017-07-01

    Shadow mapping is commonly used in real-time rendering. In this paper, we presented an accurate and efficient method of soft shadows generation from planar area lights. First this method generated a depth map from light's view, and analyzed the depth-discontinuities areas as well as shadow boundaries. Then these areas were described as binary values in the texture map called binary light-visibility map, and a parallel convolution filtering algorithm based on GPU was enforced to smooth out the boundaries with a box filter. Experiments show that our algorithm is an effective shadow map based method that produces perceptually accurate soft shadows in real time with more details of shadow boundaries compared with the previous works.

  14. Transient classification for the IRIS reactor using self-organized maps built in free platform

    International Nuclear Information System (INIS)

    Doraskevicius Junior, Waldemar

    2005-01-01

    Kohonen's Self Organized Maps (SOM) were tested with data from several operational conditions of the nuclear reactor IRIS (International Reactor Innovative and Secure) to develop an effective tool in the classification and transient identification in nuclear reactors. The data were derived from 56 simulations of the operation of IRIS, from steady-state conditions to accidents. The digital system built for the tests was based on the JAVA platform for the portability and scalability, and for being one of the free development platforms. Satisfactory results of operation classification were obtained with reasonable processing time in personal computers; about two to five minutes were spent for ordination and convergence of the learning on the data base. The methodology of this work was extended to the supervision of logistics of natural gas for Brazilian pipelines, showing satisfactory results for the classification of deliveries for simultaneous measurement in several points. (author)

  15. Self-Organizing Robots

    CERN Document Server

    Murata, Satoshi

    2012-01-01

    It is man’s ongoing hope that a machine could somehow adapt to its environment by reorganizing itself. This is what the notion of self-organizing robots is based on. The theme of this book is to examine the feasibility of creating such robots within the limitations of current mechanical engineering. The topics comprise the following aspects of such a pursuit: the philosophy of design of self-organizing mechanical systems; self-organization in biological systems; the history of self-organizing mechanical systems; a case study of a self-assembling/self-repairing system as an autonomous distributed system; a self-organizing robot that can create its own shape and robotic motion; implementation and instrumentation of self-organizing robots; and the future of self-organizing robots. All topics are illustrated with many up-to-date examples, including those from the authors’ own work. The book does not require advanced knowledge of mathematics to be understood, and will be of great benefit to students in the rob...

  16. Hierarchical organization versus self-organization

    OpenAIRE

    Busseniers, Evo

    2014-01-01

    In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...

  17. Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

    Science.gov (United States)

    Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

    2005-07-01

    The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

  18. Identification of Outlier Loci Responding to Anthropogenic and Natural Selection Pressure in Stream Insects Based on a Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    Bin Li

    2016-05-01

    Full Text Available Water quality maintenance should be considered from an ecological perspective since water is a substrate ingredient in the biogeochemical cycle and is closely linked with ecosystem functioning and services. Addressing the status of live organisms in aquatic ecosystems is a critical issue for appropriate prediction and water quality management. Recently, genetic changes in biological organisms have garnered more attention due to their in-depth expression of environmental stress on aquatic ecosystems in an integrative manner. We demonstrate that genetic diversity would adaptively respond to environmental constraints in this study. We applied a self-organizing map (SOM to characterize complex Amplified Fragment Length Polymorphisms (AFLP of aquatic insects in six streams in Japan with natural and anthropogenic variability. After SOM training, the loci compositions of aquatic insects effectively responded to environmental selection pressure. To measure how important the role of loci compositions was in the population division, we altered the AFLP data by flipping the existence of given loci individual by individual. Subsequently we recognized the cluster change of the individuals with altered data using the trained SOM. Based on SOM recognition of these altered data, we determined the outlier loci (over 90th percentile that showed drastic changes in their belonging clusters (D. Subsequently environmental responsiveness (Ek’ was also calculated to address relationships with outliers in different species. Outlier loci were sensitive to slightly polluted conditions including Chl-a, NH4-N, NOX-N, PO4-P, and SS, and the food material, epilithon. Natural environmental factors such as altitude and sediment additionally showed relationships with outliers in somewhat lower levels. Poly-loci like responsiveness was detected in adapting to environmental constraints. SOM training followed by recognition shed light on developing algorithms de novo to

  19. A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

    Science.gov (United States)

    Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

    2016-06-01

    There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

  20. Self-prior strategy for organ reconstruction in fluorescence molecular tomography.

    Science.gov (United States)

    Zhou, Yuan; Chen, Maomao; Su, Han; Luo, Jianwen

    2017-10-01

    The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

  1. Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Krink, Thiemo; Thomsen, Rene

    2001-01-01

    The gaps in the fossil record gave rise to the hypothesis that evolution proceeded in long periods of stasis, which alternated with occasional, rapid changes that yielded evolutionary progress. One mechanism that could cause these punctuated bursts is the re-colonbation of changing and deserted...... at a critical state between chaos and order, known as self-organized criticality (SOC). Based on this background, we used SOC to control the size of spatial extinction zones in a diffusion model. The SOC selection process was easy to implement and implied only negligible computational costs. Our results show...

  2. Evaluation of algorithms used to order markers on genetic maps.

    Science.gov (United States)

    Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F

    2009-12-01

    When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results.

  3. A novel image encryption algorithm based on chaos maps with Markov properties

    Science.gov (United States)

    Liu, Quan; Li, Pei-yue; Zhang, Ming-chao; Sui, Yong-xin; Yang, Huai-jiang

    2015-02-01

    In order to construct high complexity, secure and low cost image encryption algorithm, a class of chaos with Markov properties was researched and such algorithm was also proposed. The kind of chaos has higher complexity than the Logistic map and Tent map, which keeps the uniformity and low autocorrelation. An improved couple map lattice based on the chaos with Markov properties is also employed to cover the phase space of the chaos and enlarge the key space, which has better performance than the original one. A novel image encryption algorithm is constructed on the new couple map lattice, which is used as a key stream generator. A true random number is used to disturb the key which can dynamically change the permutation matrix and the key stream. From the experiments, it is known that the key stream can pass SP800-22 test. The novel image encryption can resist CPA and CCA attack and differential attack. The algorithm is sensitive to the initial key and can change the distribution the pixel values of the image. The correlation of the adjacent pixels can also be eliminated. When compared with the algorithm based on Logistic map, it has higher complexity and better uniformity, which is nearer to the true random number. It is also efficient to realize which showed its value in common use.

  4. Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants

    International Nuclear Information System (INIS)

    Mesquita, Roberto Navarro de

    2002-01-01

    This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

  5. Parallel image encryption algorithm based on discretized chaotic map

    International Nuclear Information System (INIS)

    Zhou Qing; Wong Kwokwo; Liao Xiaofeng; Xiang Tao; Hu Yue

    2008-01-01

    Recently, a variety of chaos-based algorithms were proposed for image encryption. Nevertheless, none of them works efficiently in parallel computing environment. In this paper, we propose a framework for parallel image encryption. Based on this framework, a new algorithm is designed using the discretized Kolmogorov flow map. It fulfills all the requirements for a parallel image encryption algorithm. Moreover, it is secure and fast. These properties make it a good choice for image encryption on parallel computing platforms

  6. Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

    Directory of Open Access Journals (Sweden)

    An Luo

    2017-10-01

    Full Text Available Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

  7. How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?

    Directory of Open Access Journals (Sweden)

    Warren B. Cohen

    2017-03-01

    Full Text Available Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates, with respect to the disturbance targets of interest.

  8. Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

    Science.gov (United States)

    Hoomod, Haider K.; Kareem Jebur, Tuka

    2018-05-01

    Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

  9. PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

    Energy Technology Data Exchange (ETDEWEB)

    Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan [Tomas Bata University in Zlín, Faculty of Applied Informatics Department of Informatics and Artificial Intelligence nám. T.G. Masaryka 5555, 760 01 Zlín (Czech Republic)

    2015-03-10

    In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated.

  10. PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

    International Nuclear Information System (INIS)

    Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan

    2015-01-01

    In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated

  11. A Self Consistent Multiprocessor Space Charge Algorithm that is Almost Embarrassingly Parallel

    International Nuclear Information System (INIS)

    Nissen, Edward; Erdelyi, B.; Manikonda, S.L.

    2012-01-01

    We present a space charge code that is self consistent, massively parallelizeable, and requires very little communication between computer nodes; making the calculation almost embarrassingly parallel. This method is implemented in the code COSY Infinity where the differential algebras used in this code are important to the algorithm's proper functioning. The method works by calculating the self consistent space charge distribution using the statistical moments of the test particles, and converting them into polynomial series coefficients. These coefficients are combined with differential algebraic integrals to form the potential, and electric fields. The result is a map which contains the effects of space charge. This method allows for massive parallelization since its statistics based solver doesn't require any binning of particles, and only requires a vector containing the partial sums of the statistical moments for the different nodes to be passed. All other calculations are done independently. The resulting maps can be used to analyze the system using normal form analysis, as well as advance particles in numbers and at speeds that were previously impossible.

  12. Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps

    Science.gov (United States)

    Chen, I.-Ting; Chang, Li-Chiu; Chang, Fi-John

    2018-01-01

    In this study, we propose a soft-computing methodology to visibly explore the spatio-temporal groundwater variations of the Kuoping River basin in southern Taiwan. The self-organizing map (SOM) is implemented to investigate the interactive mechanism between surface water and groundwater over the river basin based on large high-dimensional data sets coupled with their occurrence times. We find that extracting the occurrence time from each 30-day moving average data set in the clustered neurons of the SOM is a crucial step to learn the spatio-temporal interaction between surface water and groundwater. We design 2-D Topological Bubble Map to summarize all the groundwater values of four aquifers in a neuron, which can visibly explore the major features of the groundwater in the vertical direction. The constructed SOM topological maps nicely display that: (1) the groundwater movement, in general, extends from the eastern area to the western, where groundwater in the eastern area can be easily recharged from precipitation in wet seasons and discharged into streams during dry seasons due to the high permeability in this area; (2) the water movements in the four aquifers of the study area are quite different, and the seasonal variations of groundwater in the second and third aquifers are larger than those of the others; and (3) the spatial distribution and seasonal variations of groundwater and surface water are comprehensively linked together over the constructed maps to present groundwater characteristics and the interrelation between groundwater and surface water. The proposed modeling methodology not only can classify the large complex high-dimensional data sets into visible topological maps to effectively facilitate the quantitative status of regional groundwater resources but can also provide useful elaboration for future groundwater management.

  13. Chemotaxonomy of three genera of the Annonaceae family using self-organizing maps and 13C NMR data of diterpenes

    International Nuclear Information System (INIS)

    Scotti, Luciana; Tavares, Josean Fechine; Silva, Marcelo Sobral da; Falcao, Emanuela Viana; Silva, Luana de Morais e; Soares, Gabriela Cristina da Silva; Scotti, Marcus Tullius

    2012-01-01

    The Annonaceae family is distributed throughout Neotropical regions of the world. In Brazil, it covers nearly all natural formations particularly Annona, Xylopia and Polyalthia and is characterized chemically by the production of sources of terpenoids (mainly diterpenes), alkaloids, steroids, polyphenols and, flavonoids. Studies from 13 C NMR data of diterpenes related with their botanical occurrence were used to generate self-organizing maps. Results corroborate those in the literature obtained from morphological and molecular data for three genera and the model can be used to project other diterpenes. Therefore, the model produced can predict which genera are likely to contain a compound. (author)

  14. Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

    Science.gov (United States)

    Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

    2000-01-01

    Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

  15. A novel algorithm for image encryption based on mixture of chaotic maps

    International Nuclear Information System (INIS)

    Behnia, S.; Akhshani, A.; Mahmodi, H.; Akhavan, A.

    2008-01-01

    Chaos-based encryption appeared recently in the early 1990s as an original application of nonlinear dynamics in the chaotic regime. In this paper, an implementation of digital image encryption scheme based on the mixture of chaotic systems is reported. The chaotic cryptography technique used in this paper is a symmetric key cryptography. In this algorithm, a typical coupled map was mixed with a one-dimensional chaotic map and used for high degree security image encryption while its speed is acceptable. The proposed algorithm is described in detail, along with its security analysis and implementation. The experimental results based on mixture of chaotic maps approves the effectiveness of the proposed method and the implementation of the algorithm. This mixture application of chaotic maps shows advantages of large key space and high-level security. The ciphertext generated by this method is the same size as the plaintext and is suitable for practical use in the secure transmission of confidential information over the Internet

  16. Content-based image retrieval using a signature graph and a self-organizing map

    Directory of Open Access Journals (Sweden)

    Van Thanh The

    2016-06-01

    Full Text Available In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

  17. A Trust Region Aggressive Space Mapping Algorithm for EM

    DEFF Research Database (Denmark)

    Bakr., M.; Bandler, J. W.; Biernacki, R.

    1998-01-01

    A robust new algorithm for electromagnetic (EM) optimization of microwave circuits is presented. The algorithm (TRASM) integrates a trust region methodology with the aggressive space mapping (ASM). The trust region ensures that each iteration results in improved alignment between the coarse....... This suggested step exploits all the available EM simulations for improving the uniqueness of parameter extraction. The new algorithm was successfully used to design a number of microwave circuits. Examples include the EM optimization of a double-folded stub filter and of a high-temperature superconducting (HTS...

  18. A novel self-organizing E-Learner community model with award and exchange mechanisms.

    Science.gov (United States)

    Yang, Fan; Shen, Rui-min; Han, Peng

    2004-11-01

    How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.

  19. A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases.

    Science.gov (United States)

    Jain, Chirag; Dilthey, Alexander; Koren, Sergey; Aluru, Srinivas; Phillippy, Adam M

    2018-04-30

    Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long-read mapping algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this article, we combine a fast approximate read mapping algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290 × faster than Burrows-Wheeler Aligner-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each ≥5 kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and >60,000 genomes.

  20. Characterization of robotics parallel algorithms and mapping onto a reconfigurable SIMD machine

    Science.gov (United States)

    Lee, C. S. G.; Lin, C. T.

    1989-01-01

    The kinematics, dynamics, Jacobian, and their corresponding inverse computations are six essential problems in the control of robot manipulators. Efficient parallel algorithms for these computations are discussed and analyzed. Their characteristics are identified and a scheme on the mapping of these algorithms to a reconfigurable parallel architecture is presented. Based on the characteristics including type of parallelism, degree of parallelism, uniformity of the operations, fundamental operations, data dependencies, and communication requirement, it is shown that most of the algorithms for robotic computations possess highly regular properties and some common structures, especially the linear recursive structure. Moreover, they are well-suited to be implemented on a single-instruction-stream multiple-data-stream (SIMD) computer with reconfigurable interconnection network. The model of a reconfigurable dual network SIMD machine with internal direct feedback is introduced. A systematic procedure internal direct feedback is introduced. A systematic procedure to map these computations to the proposed machine is presented. A new scheduling problem for SIMD machines is investigated and a heuristic algorithm, called neighborhood scheduling, that reorders the processing sequence of subtasks to reduce the communication time is described. Mapping results of a benchmark algorithm are illustrated and discussed.

  1. An effective self-assessment based on concept map extraction from test-sheet for personalized learning

    Science.gov (United States)

    Liew, Keng-Hou; Lin, Yu-Shih; Chang, Yi-Chun; Chu, Chih-Ping

    2013-12-01

    Examination is a traditional way to assess learners' learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

  2. IDENTIFYING LOCAL SCALE CLIMATE ZONES OF URBAN HEAT ISLAND FROM HJ-1B SATELLITE DATA USING SELF-ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    C. Z. Wei

    2016-10-01

    Full Text Available With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM. The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95% in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.

  3. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

    Science.gov (United States)

    Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

    2017-06-14

    The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

  4. Planimetric Features Generalization for the Production of Small-Scale Map by Using Base Maps and the Existing Algorithms

    Directory of Open Access Journals (Sweden)

    M. Modiri

    2014-10-01

    Full Text Available Cartographic maps are representations of the Earth upon a flat surface in the smaller scale than it’s true. Large scale maps cover relatively small regions in great detail and small scale maps cover large regions such as nations, continents and the whole globe. Logical connection between the features and scale map must be maintained by changing the scale and it is important to recognize that even the most accurate maps sacrifice a certain amount of accuracy in scale to deliver a greater visual usefulness to its user. Cartographic generalization, or map generalization, is the method whereby information is selected and represented on a map in a way that adapts to the scale of the display medium of the map, not necessarily preserving all intricate geographical or other cartographic details. Due to the problems facing small-scale map production process and the need to spend time and money for surveying, today’s generalization is used as executive approach. The software is proposed in this paper that converted various data and information to certain Data Model. This software can produce generalization map according to base map using the existing algorithm. Planimetric generalization algorithms and roles are described in this article. Finally small-scale maps with 1:100,000, 1:250,000 and 1:500,000 scale are produced automatically and they are shown at the end.

  5. Chemotaxonomy of three genera of the Annonaceae family using self-organizing maps and {sup 13}C NMR data of diterpenes

    Energy Technology Data Exchange (ETDEWEB)

    Scotti, Luciana; Tavares, Josean Fechine; Silva, Marcelo Sobral da [Universidade Federal da Paraiba (UFPB), Joao Pessoa, PB (Brazil). Dept. de Ciencias Farmaceuticas; Falcao, Emanuela Viana; Silva, Luana de Morais e; Soares, Gabriela Cristina da Silva; Scotti, Marcus Tullius, E-mail: mtscotti@ccae.ufpb.br [Universidade Federal da Paraiba (UFPB), Rio Tinto, PB (Brazil). Dept. de Engenharia e Meio Ambiente

    2012-07-01

    The Annonaceae family is distributed throughout Neotropical regions of the world. In Brazil, it covers nearly all natural formations particularly Annona, Xylopia and Polyalthia and is characterized chemically by the production of sources of terpenoids (mainly diterpenes), alkaloids, steroids, polyphenols and, flavonoids. Studies from {sup 13}C NMR data of diterpenes related with their botanical occurrence were used to generate self-organizing maps. Results corroborate those in the literature obtained from morphological and molecular data for three genera and the model can be used to project other diterpenes. Therefore, the model produced can predict which genera are likely to contain a compound. (author)

  6. Self-organizing plasmas

    International Nuclear Information System (INIS)

    Hayashi, T.; Sato, T.

    1999-01-01

    The primary purpose of this paper is to extract a grand view of self-organization through an extensive computer simulation of plasmas. The assertion is made that self-organization is governed by three key processes, i.e. the existence of an open complex system, the existence of information (energy) sources and the existence of entropy generation and expulsion processes. We find that self-organization takes place in an intermittent fashion when energy is supplied continuously from outside. In contrast, when the system state is suddenly changed into a non-equilibrium state externally, the system evolves stepwise and reaches a minimum energy state. We also find that the entropy production rate is maximized whenever a new ordered structure is created and that if the entropy generated during the self-organizing process is expelled from the system, then the self-organized structure becomes more prominent and clear. (author)

  7. Patterns identification in supervisory systems of nuclear reactors installations and gas pipelines systems using self-organizing maps

    International Nuclear Information System (INIS)

    Doraskevicius Junior, Waldemar

    2005-01-01

    Self-Organizing Maps, SOM, of Kohonen were studied, implemented and tested with the aim of developing, for the energy branch, an effective tool especially for transient identification in nuclear reactors and for gas pipelines networks logistic supervision, by classifying operations and identifying transients or abnormalities. The digital system for the test was developed in Java platform, for the portability and scalability, and for belonging to free development platforms. The system, executed in personal computers, showed satisfactory results to aid in decision taking, by classifying IRIS (International Reactor Innovative and Secure) reactor operation conditions (data from simulator) and by classifying Southeast (owner: TRANSPETRO - Brazil) gas pipeline network. Various adaptations were needed for such business, as new topologies for the output layer of artificial neural network and particular preparation for the input data. (author)

  8. Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits

    Directory of Open Access Journals (Sweden)

    Kajsa Ljungberg

    2010-10-01

    Full Text Available Kajsa Ljungberg1, Kateryna Mishchenko2, Sverker Holmgren11Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden; 2Department of Mathematics and Physics, Mälardalen University College, Västerås, SwedenAbstract: We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called QTL, and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.Keywords: global optimization, QTL mapping, DIRECT 

  9. An algorithm for automated layout of process description maps drawn in SBGN.

    Science.gov (United States)

    Genc, Begum; Dogrusoz, Ugur

    2016-01-01

    Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  10. MRPack: Multi-Algorithm Execution Using Compute-Intensive Approach in MapReduce

    Science.gov (United States)

    2015-01-01

    Large quantities of data have been generated from multiple sources at exponential rates in the last few years. These data are generated at high velocity as real time and streaming data in variety of formats. These characteristics give rise to challenges in its modeling, computation, and processing. Hadoop MapReduce (MR) is a well known data-intensive distributed processing framework using the distributed file system (DFS) for Big Data. Current implementations of MR only support execution of a single algorithm in the entire Hadoop cluster. In this paper, we propose MapReducePack (MRPack), a variation of MR that supports execution of a set of related algorithms in a single MR job. We exploit the computational capability of a cluster by increasing the compute-intensiveness of MapReduce while maintaining its data-intensive approach. It uses the available computing resources by dynamically managing the task assignment and intermediate data. Intermediate data from multiple algorithms are managed using multi-key and skew mitigation strategies. The performance study of the proposed system shows that it is time, I/O, and memory efficient compared to the default MapReduce. The proposed approach reduces the execution time by 200% with an approximate 50% decrease in I/O cost. Complexity and qualitative results analysis shows significant performance improvement. PMID:26305223

  11. A Parallel Encryption Algorithm Based on Piecewise Linear Chaotic Map

    Directory of Open Access Journals (Sweden)

    Xizhong Wang

    2013-01-01

    Full Text Available We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM. The parallel algorithm is designed with a master/slave communication model with the Message Passing Interface (MPI. The algorithm is suitable not only for multicore processors but also for the single-processor architecture. The experimental results show that the chaos-based cryptosystem possesses good statistical properties. The parallel algorithm provides much better performance than the serial ones and would be useful to apply in encryption/decryption file with large size or multimedia.

  12. A Graphical, Self-Organizing Approach to Classifying Electronic Meeting Output.

    Science.gov (United States)

    Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F., Jr.

    1997-01-01

    Describes research using an artificial intelligence approach in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information…

  13. "Accelerated Perceptron": A Self-Learning Linear Decision Algorithm

    OpenAIRE

    Zuev, Yu. A.

    2003-01-01

    The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...

  14. Self-organized topology of recurrence-based complex networks

    International Nuclear Information System (INIS)

    Yang, Hui; Liu, Gang

    2013-01-01

    With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks

  15. Self-organizing maps applied to two-phase flow on natural circulation loop study

    International Nuclear Information System (INIS)

    Castro, Leonardo Ferreira

    2016-01-01

    Two-phase flow of liquid and gas is found in many closed circuits using natural circulation for cooling purposes. Natural circulation phenomenon is important on recent nuclear power plant projects for decay heat removal. The Natural Circulation Facility (Circuito de Circulacao Natural CCN) installed at Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN, is an experimental circuit designed to provide thermal hydraulic data related to single and two-phase flow under natural circulation conditions. This periodic flow oscillation behavior can be observed thoroughly in this facility due its glass-made tubes transparency. The heat transfer estimation has been improved based on models that require precise prediction of pattern transitions of flow. This work presents experiments realized at CCN to visualize natural circulation cycles in order to classify two-phase flow patterns associated with phase transients and static instabilities of flow. Images are compared and clustered using Kohonen Self-organizing Maps (SOM's) applied on different digital image features. The Full Frame Discret Cosine Transform (FFDCT) coefficients were used as input for the classification task, enabling good results. FFDCT prototypes obtained can be associated to each flow pattern, enabling a better comprehension of each observed instability. A systematic test methodology was used to verify classifier robustness.

  16. The Peak Pairs algorithm for strain mapping from HRTEM images

    Energy Technology Data Exchange (ETDEWEB)

    Galindo, Pedro L. [Departamento de Lenguajes y Sistemas Informaticos, CASEM, Universidad de Cadiz, Pol. Rio San Pedro s/n. 11510, Puerto Real, Cadiz (Spain)], E-mail: pedro.galindo@uca.es; Kret, Slawomir [Institute of Physics, PAS, AL. Lotnikow 32/46, 02-668 Warsaw (Poland); Sanchez, Ana M. [Departamento de Ciencia de los Materiales e Ing. Metalurgica y Q. Inorganica, Facultad de Ciencias, Universidad de Cadiz, Pol. Rio San Pedro s/n. 11510, Puerto Real, Cadiz (Spain); Laval, Jean-Yves [Laboratoire de Physique du Solide, UPR5 CNRS-ESPCI, Paris (France); Yanez, Andres; Pizarro, Joaquin; Guerrero, Elisa [Departamento de Lenguajes y Sistemas Informaticos, CASEM, Universidad de Cadiz, Pol. Rio San Pedro s/n. 11510, Puerto Real, Cadiz (Spain); Ben, Teresa; Molina, Sergio I. [Departamento de Ciencia de los Materiales e Ing. Metalurgica y Q. Inorganica, Facultad de Ciencias, Universidad de Cadiz, Pol. Rio San Pedro s/n. 11510, Puerto Real, Cadiz (Spain)

    2007-11-15

    Strain mapping is defined as a numerical image-processing technique that measures the local shifts of image details around a crystal defect with respect to the ideal, defect-free, positions in the bulk. Algorithms to map elastic strains from high-resolution transmission electron microscopy (HRTEM) images may be classified into two categories: those based on the detection of peaks of intensity in real space and the Geometric Phase approach, calculated in Fourier space. In this paper, we discuss both categories and propose an alternative real space algorithm (Peak Pairs) based on the detection of pairs of intensity maxima in an affine transformed space dependent on the reference area. In spite of the fact that it is a real space approach, the Peak Pairs algorithm exhibits good behaviour at heavily distorted defect cores, e.g. interfaces and dislocations. Quantitative results are reported from experiments to determine local strain in different types of semiconductor heterostructures.

  17. A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism

    International Nuclear Information System (INIS)

    Fu-Lai, Wang

    2010-01-01

    A specific uniform map is constructed as a homeomorphism mapping chaotic time series into [0,1] to obtain sequences of standard uniform distribution. With the uniform map, a chaotic orbit and a sequence orbit obtained are topologically equivalent to each other so the map can preserve the most dynamic properties of chaotic systems such as permutation entropy. Based on the uniform map, a universal algorithm to generate pseudo random numbers is proposed and the pseudo random series is tested to follow the standard 0–1 random distribution both theoretically and experimentally. The algorithm is not complex, which does not impose high requirement on computer hard ware and thus computation speed is fast. The method not only extends the parameter spaces but also avoids the drawback of small function space caused by constraints on chaotic maps used to generate pseudo random numbers. The algorithm can be applied to any chaotic system and can produce pseudo random sequence of high quality, thus can be a good universal pseudo random number generator. (general)

  18. A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism

    Science.gov (United States)

    Wang, Fu-Lai

    2010-09-01

    A specific uniform map is constructed as a homeomorphism mapping chaotic time series into [0,1] to obtain sequences of standard uniform distribution. With the uniform map, a chaotic orbit and a sequence orbit obtained are topologically equivalent to each other so the map can preserve the most dynamic properties of chaotic systems such as permutation entropy. Based on the uniform map, a universal algorithm to generate pseudo random numbers is proposed and the pseudo random series is tested to follow the standard 0-1 random distribution both theoretically and experimentally. The algorithm is not complex, which does not impose high requirement on computer hard ware and thus computation speed is fast. The method not only extends the parameter spaces but also avoids the drawback of small function space caused by constraints on chaotic maps used to generate pseudo random numbers. The algorithm can be applied to any chaotic system and can produce pseudo random sequence of high quality, thus can be a good universal pseudo random number generator.

  19. Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping Algorithms with Robotic Mobile Mapping Systems

    Directory of Open Access Journals (Sweden)

    Bedkowski Janusz

    2017-09-01

    Full Text Available This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point, ICP (point to projection, ICP with semantic discrimination of points, LS3D (Least Square Surface Matching, NDT (Normal Distribution Transform can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs. The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.

  20. Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

    Science.gov (United States)

    Doble, Brett; Lorgelly, Paula

    2016-04-01

    To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

  1. Application of mapping crossover genetic algorithm in nuclear power equipment optimization design

    International Nuclear Information System (INIS)

    Li Guijiang; Yan Changqi; Wang Jianjun; Liu Chengyang

    2013-01-01

    Genetic algorithm (GA) has been widely applied in nuclear engineering. An improved method, named the mapping crossover genetic algorithm (MCGA), was developed aiming at improving the shortcomings of traditional genetic algorithm (TGA). The optimal results of benchmark problems show that MCGA has better optimizing performance than TGA. MCGA was applied to the reactor coolant pump optimization design. (authors)

  2. A MAP blind image deconvolution algorithm with bandwidth over-constrained

    Science.gov (United States)

    Ren, Zhilei; Liu, Jin; Liang, Yonghui; He, Yulong

    2018-03-01

    We demonstrate a maximum a posteriori (MAP) blind image deconvolution algorithm with bandwidth over-constrained and total variation (TV) regularization to recover a clear image from the AO corrected images. The point spread functions (PSFs) are estimated by bandwidth limited less than the cutoff frequency of the optical system. Our algorithm performs well in avoiding noise magnification. The performance is demonstrated on simulated data.

  3. Strength Pareto Evolutionary Algorithm using Self-Organizing Data Analysis Techniques

    Directory of Open Access Journals (Sweden)

    Ionut Balan

    2015-03-01

    Full Text Available Multiobjective optimization is widely used in problems solving from a variety of areas. To solve such problems there was developed a set of algorithms, most of them based on evolutionary techniques. One of the algorithms from this class, which gives quite good results is SPEA2, method which is the basis of the proposed algorithm in this paper. Results from this paper are obtained by running these two algorithms on a flow-shop problem.

  4. Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sabrina Sicari

    2017-01-01

    Full Text Available Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks, which frequently affect Wireless Sensor Networks (WSNs. Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe, which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

  5. Self-organizing map analysis using multivariate data from theophylline powders predicted by a thin-plate spline interpolation.

    Science.gov (United States)

    Yasuda, Akihito; Onuki, Yoshinori; Kikuchi, Shingo; Takayama, Kozo

    2010-11-01

    The quality by design concept in pharmaceutical formulation development requires establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline powders were prepared based on the standard formulation. The angle of repose, compressibility, cohesion, and dispersibility were measured as the response variables. These responses were predicted quantitatively on the basis of a nonlinear TPS. A large amount of data on these powders was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the powders could be classified into several distinctive clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and powder characteristics. For instance, the quantities of microcrystalline cellulose (MCC) and magnesium stearate (Mg-St) were classified distinctly into each cluster, indicating that the quantities of MCC and Mg-St were crucial for determining the powder characteristics. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline powder formulations. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

  6. Self-Mapping in Treating Suicide Ideation: A Case Study

    Science.gov (United States)

    Robertson, Lloyd Hawkeye

    2011-01-01

    This case study traces the development and use of a self-mapping exercise in the treatment of a youth who had been at risk for re-attempting suicide. A life skills exercise was modified to identify units of culture called "memes" from which a map of the youth's self was prepared. A successful treatment plan followed the mapping exercise. The…

  7. Strong Convergence of Hybrid Algorithm for Asymptotically Nonexpansive Mappings in Hilbert Spaces

    Directory of Open Access Journals (Sweden)

    Juguo Su

    2012-01-01

    Full Text Available The hybrid algorithms for constructing fixed points of nonlinear mappings have been studied extensively in recent years. The advantage of this methods is that one can prove strong convergence theorems while the traditional iteration methods just have weak convergence. In this paper, we propose two types of hybrid algorithm to find a common fixed point of a finite family of asymptotically nonexpansive mappings in Hilbert spaces. One is cyclic Mann's iteration scheme, and the other is cyclic Halpern's iteration scheme. We prove the strong convergence theorems for both iteration schemes.

  8. 75 FR 63878 - Self-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate...

    Science.gov (United States)

    2010-10-18

    ...-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate Effectiveness of...(b)(1). \\2\\ 17 CFR 240.19b-4. I. Self-Regulatory Organization's Statement of the Terms of Substance... Public Reference Room. II. Self-Regulatory Organization's Statement of the Purpose of, and Statutory...

  9. A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects

    International Nuclear Information System (INIS)

    Secui, Dinu Calin

    2016-01-01

    This paper proposes a new metaheuristic algorithm, called Modified Symbiotic Organisms Search (MSOS) algorithm, to solve the economic dispatch problem considering the valve-point effects, the prohibited operating zones (POZ), the transmission line losses, multi-fuel sources, as well as other operating constraints of the generating units and power system. The MSOS algorithm introduces, in all of its phases, new relations to update the solutions to improve its capacity of identifying stable and of high-quality solutions in a reasonable time. Furthermore, to increase the capacity of exploring the MSOS algorithm in finding the most promising zones, it is endowed with a chaotic component generated by the Logistic map. The performance of the modified algorithm and of the original algorithm Symbiotic Organisms Search (SOS) is tested on five systems of different characteristics, constraints and dimensions (13-unit, 40-unit, 80-unit, 160-unit and 320-unit). The results obtained by applying the proposed algorithm (MSOS) show that this has a better performance than other techniques of optimization recently used in solving the economic dispatch problem with valve-point effects. - Highlights: • A new modified SOS algorithm (MSOS) is proposed to solve the EcD problem. • Valve-point effects, ramp-rate limits, POZ, multi-fuel sources, transmission losses were considered. • The algorithm is tested on five systems having 13, 40, 80, 160 and 320 thermal units. • MSOS algorithm outperforms many other optimization techniques.

  10. Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma.

    Science.gov (United States)

    Arnold, David T; Rowen, Donna; Versteegh, Matthijs M; Morley, Anna; Hooper, Clare E; Maskell, Nicholas A

    2015-01-23

    In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published. Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed. The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset. This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

  11. Application Self-organizing Map Type in a Study of the Profile of Gasoline C Commercialized in the Eastern and Northern Parana Regions

    Directory of Open Access Journals (Sweden)

    Lívia Ramazzoti Silva

    2015-06-01

    Full Text Available Artificial neural networks self-organizing map type (SOM was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel. DOI: http://dx.doi.org/10.17807/orbital.v7i2.732 

  12. Alteration mineral mapping in inaccessible regions using target detection algorithms to ASTER data

    International Nuclear Information System (INIS)

    Pour, A B; Hashim, M; Park, Y

    2017-01-01

    In this study, the applications of target detection algorithms such as Constrained Energy Minimization (CEM), Orthogonal Subspace Projection (OSP) and Adaptive Coherence Estimator (ACE) to shortwave infrared bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was investigated to extract geological information for alteration mineral mapping in poorly exposed lithologies in inaccessible domains. The Oscar II coast area north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. It is an inaccessible region due to the remoteness of many rock exposures and the necessity to travel over sever mountainous and glacier-cover terrains for geological field mapping and sample collection. Fractional abundance of alteration minerals such as muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite were identified in alteration zones using CEM, OSP and ACE algorithms in poorly mapped and unmapped zones at district scale for the Oscar II coast area. The results of this investigation demonstrated the applicability of ASTER shortwave infrared spectral data for lithological and alteration mineral mapping in poorly exposed lithologies and inaccessible regions, particularly using the image processing algorithms that are capable to detect sub-pixel targets in the remotely sensed images, where no prior information is available. (paper)

  13. Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish

    Science.gov (United States)

    Russo, Tommaso; Scardi, Michele; Cataudella, Stefano

    2014-01-01

    We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods. PMID:24466185

  14. The optimal algorithm for Multi-source RS image fusion.

    Science.gov (United States)

    Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

    2016-01-01

    In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

  15. A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2017-01-01

    Full Text Available Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps. Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.

  16. Mobility Model for Self-Organizing and Cooperative MSN and MANET Systems

    Directory of Open Access Journals (Sweden)

    Andrzej Sikora

    2012-03-01

    Full Text Available Self-organization mechanisms are used for building scalable systems consisting of a huge number of subsystems. In computer networks, self-organizing is especially important in ad hoc networking. A self-organizing ad hoc network is a collection of wireless devices that collaborate with each other to form a network system that adapts to achieve a goal or goals. Such network is often built from mobile devices that may spontaneously create a network and dynamically adapted to changes in an unknown environment. Mobility pattern is a critical element that influences the performance characteristics of mobile sensor networks (MSN and mobile ad hoc networks (MANET. In this paper, we survey main directions to mobility modeling. We describe a novel algorithm for calculating mobility patterns for mobile devices that is based on a cluster formation and an artificial potential function. Finally, we present the simulation results of its application to a rescue mission planning.

  17. Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)

    International Nuclear Information System (INIS)

    Liukkonen, M.; Hiltunen, T.

    2014-01-01

    Improvement of energy efficiency, reduction of operating costs, and reduction of harmful emissions released into the atmosphere are issues of major concern in modern energy plants. While air emissions have to be restricted due to tightening environmental legislation, at the same time it is ever more important to be able to respond quickly to any changes in the load demand or fuel quality. As unpredictability increases with changing fuel quality and more complex operational strategies, undesired phenomena such as increased emission release rates may become more likely. Therefore, it is crucial that emission monitoring systems are able to adapt to varying conditions, and advanced methodologies are needed for monitoring and decision-support. In this paper a novel approach for advanced monitoring of emissions in CFB (circulating fluidized bed) boilers is described. In this approach a model based on SOM (self-organizing maps) is updated regularly to respond to the prevailing condition of the boiler. After creating each model a new set of measurements is input to the system, and the current state of the process is determined using vector distance calculation. Finally, the system evaluates the current condition and may alert if a preset limit defined for each emission component is exceeded. - Highlights: • An adaptive monitoring approach based on self-organizing maps is presented. • The system can monitor the current state of a combustion process and its emissions. • The system is designed to alert when the preset limits defined for emissions are exceeded. • Due to regular updating routine the system is able to adapt to changing conditions. • The application is demonstrated using data from a biomass-fired energy boiler

  18. DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

    Science.gov (United States)

    Dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

    2015-06-01

    Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time. We define a complete and original framework based on formal heuristics that drives the adaptation of KOS mappings. Our approach takes into account the definition of established mappings, the evolution of KOS and the possible changes that can be applied to mappings. This study experimentally evaluates the proposed heuristics and the entire framework on realistic case studies borrowed from the biomedical domain, using official mappings between several biomedical KOSs. We demonstrate the overall performance of the approach over biomedical datasets of different characteristics and sizes. Our findings reveal the effectiveness in terms of precision, recall and F-measure of the suggested heuristics and methods defining the framework to adapt mappings affected by KOS evolution. The obtained results contribute and improve the quality of mappings over time. The proposed framework can adapt mappings largely automatically, facilitating thus the maintenance task. The implemented algorithms and tools support and minimize the work of users in charge of KOS mapping maintenance. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. The Pointing Self-calibration Algorithm for Aperture Synthesis Radio Telescopes

    Energy Technology Data Exchange (ETDEWEB)

    Bhatnagar, S.; Cornwell, T. J., E-mail: sbhatnag@nrao.edu [National Radio Astronomy Observatory, 1003 Lopezville Road, Socorro, NM 87801 (United States)

    2017-11-01

    This paper is concerned with algorithms for calibration of direction-dependent effects (DDE) in aperture synthesis radio telescopes (ASRT). After correction of direction-independent effects (DIE) using self-calibration, imaging performance can be limited by the imprecise knowledge of the forward gain of the elements in the array. In general, the forward gain pattern is directionally dependent and varies with time due to a number of reasons. Some factors, such as rotation of the primary beam with Parallactic Angle for Azimuth–Elevation mount antennas are known a priori. Some, such as antenna pointing errors and structural deformation/projection effects for aperture-array elements cannot be measured a priori. Thus, in addition to algorithms to correct for DD effects known a priori, algorithms to solve for DD gains are required for high dynamic range imaging. Here, we discuss a mathematical framework for antenna-based DDE calibration algorithms and show that this framework leads to computationally efficient optimal algorithms that scale well in a parallel computing environment. As an example of an antenna-based DD calibration algorithm, we demonstrate the Pointing SelfCal (PSC) algorithm to solve for the antenna pointing errors. Our analysis show that the sensitivity of modern ASRT is sufficient to solve for antenna pointing errors and other DD effects. We also discuss the use of the PSC algorithm in real-time calibration systems and extensions for antenna Shape SelfCal algorithm for real-time tracking and corrections for pointing offsets and changes in antenna shape.

  20. The Pointing Self-calibration Algorithm for Aperture Synthesis Radio Telescopes

    Science.gov (United States)

    Bhatnagar, S.; Cornwell, T. J.

    2017-11-01

    This paper is concerned with algorithms for calibration of direction-dependent effects (DDE) in aperture synthesis radio telescopes (ASRT). After correction of direction-independent effects (DIE) using self-calibration, imaging performance can be limited by the imprecise knowledge of the forward gain of the elements in the array. In general, the forward gain pattern is directionally dependent and varies with time due to a number of reasons. Some factors, such as rotation of the primary beam with Parallactic Angle for Azimuth-Elevation mount antennas are known a priori. Some, such as antenna pointing errors and structural deformation/projection effects for aperture-array elements cannot be measured a priori. Thus, in addition to algorithms to correct for DD effects known a priori, algorithms to solve for DD gains are required for high dynamic range imaging. Here, we discuss a mathematical framework for antenna-based DDE calibration algorithms and show that this framework leads to computationally efficient optimal algorithms that scale well in a parallel computing environment. As an example of an antenna-based DD calibration algorithm, we demonstrate the Pointing SelfCal (PSC) algorithm to solve for the antenna pointing errors. Our analysis show that the sensitivity of modern ASRT is sufficient to solve for antenna pointing errors and other DD effects. We also discuss the use of the PSC algorithm in real-time calibration systems and extensions for antenna Shape SelfCal algorithm for real-time tracking and corrections for pointing offsets and changes in antenna shape.

  1. The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis

    Directory of Open Access Journals (Sweden)

    Dong Lu

    2015-06-01

    Full Text Available Smart cities link the city services, citizens, resource and infrastructures together and form the heart of the modern society. As a “smart” ecosystem, smart cities focus on sustainable growth, efficiency, productivity and environmentally friendly development. By comparing with the European Union, North America and other countries, smart cities in China are still in the preliminary stage. This study offers a comparative analysis of ten smart cities in China on the basis of an extensive database covering two time periods: 2005–2007 and 2008–2010. The unsupervised computational neural network self-organizing map (SOM analysis is adopted to map out the various cities based on their performance. The demonstration effect and mutual influences between these ten smart cities are also discussed by using social network analysis. Based on the smart city performance and cluster network, current problems for smart city development in China were pointed out. Future research directions for smart city research are discussed at the end this paper.

  2. A novel gridding algorithm to create regional trace gas maps from satellite observations

    Science.gov (United States)

    Kuhlmann, G.; Hartl, A.; Cheung, H. M.; Lam, Y. F.; Wenig, M. O.

    2014-02-01

    The recent increase in spatial resolution for satellite instruments has made it feasible to study distributions of trace gas column densities on a regional scale. For this application a new gridding algorithm was developed to map measurements from the instrument's frame of reference (level 2) onto a longitude-latitude grid (level 3). The algorithm is designed for the Ozone Monitoring Instrument (OMI) and can easily be employed for similar instruments - for example, the upcoming TROPOspheric Monitoring Instrument (TROPOMI). Trace gas distributions are reconstructed by a continuous parabolic spline surface. The algorithm explicitly considers the spatially varying sensitivity of the sensor resulting from the instrument function. At the swath edge, the inverse problem of computing the spline coefficients is very sensitive to measurement errors and is regularised by a second-order difference matrix. Since this regularisation corresponds to the penalty term for smoothing splines, it similarly attenuates the effect of measurement noise over the entire swath width. Monte Carlo simulations are conducted to study the performance of the algorithm for different distributions of trace gas column densities. The optimal weight of the penalty term is found to be proportional to the measurement uncertainty and the width of the instrument function. A comparison with an established gridding algorithm shows improved performance for small to moderate measurement errors due to better parametrisation of the distribution. The resulting maps are smoother and extreme values are more accurately reconstructed. The performance improvement is further illustrated with high-resolution distributions obtained from a regional chemistry model. The new algorithm is applied to tropospheric NO2 column densities measured by OMI. Examples of regional NO2 maps are shown for densely populated areas in China, Europe and the United States of America. This work demonstrates that the newly developed gridding

  3. A novel gridding algorithm to create regional trace gas maps from satellite observations

    Directory of Open Access Journals (Sweden)

    G. Kuhlmann

    2014-02-01

    Full Text Available The recent increase in spatial resolution for satellite instruments has made it feasible to study distributions of trace gas column densities on a regional scale. For this application a new gridding algorithm was developed to map measurements from the instrument's frame of reference (level 2 onto a longitude–latitude grid (level 3. The algorithm is designed for the Ozone Monitoring Instrument (OMI and can easily be employed for similar instruments – for example, the upcoming TROPOspheric Monitoring Instrument (TROPOMI. Trace gas distributions are reconstructed by a continuous parabolic spline surface. The algorithm explicitly considers the spatially varying sensitivity of the sensor resulting from the instrument function. At the swath edge, the inverse problem of computing the spline coefficients is very sensitive to measurement errors and is regularised by a second-order difference matrix. Since this regularisation corresponds to the penalty term for smoothing splines, it similarly attenuates the effect of measurement noise over the entire swath width. Monte Carlo simulations are conducted to study the performance of the algorithm for different distributions of trace gas column densities. The optimal weight of the penalty term is found to be proportional to the measurement uncertainty and the width of the instrument function. A comparison with an established gridding algorithm shows improved performance for small to moderate measurement errors due to better parametrisation of the distribution. The resulting maps are smoother and extreme values are more accurately reconstructed. The performance improvement is further illustrated with high-resolution distributions obtained from a regional chemistry model. The new algorithm is applied to tropospheric NO2 column densities measured by OMI. Examples of regional NO2 maps are shown for densely populated areas in China, Europe and the United States of America. This work demonstrates that the newly

  4. Performance and energy efficiency in wireless self-organized networks

    Energy Technology Data Exchange (ETDEWEB)

    Gao, C.

    2009-07-01

    Self-organized packet radio networks (ad-hoc networks) and wireless sensor networks have got massive attention recently. One of critical problems in such networks is the energy efficiency, because wireless nodes are usually powered by battery. Energy efficiency design can dramatically increase the survivability and stability of wireless ad-hoc/sensor networks. In this thesis the energy efficiency has been considered at different protocol layers for wireless ad-hoc/sensor networks. The energy consumption of wireless nodes is inspected at the physical layer and MAC layer. At the network layer, some current routing protocols are compared and special attention has been paid to reactive routing protocols. A minimum hop analysis is given and according to the analysis result, a modification of AODV routing is proposed. A variation of transmit power can be also applied to clustering algorithm, which is believed to be able to control the scalability of network. Clustering a network can also improve the energy efficiency. We offer a clustering scheme based on the link state measurement and variation of transmit power of intra-cluster and inter-cluster transmission. Simulation shows that it can achieve both targets. In association with the clustering algorithm, a global synchronization scheme is proposed to increase the efficiency of clustering algorithm. The research attention has been also paid to self-organization for multi-hop cellular networks. A 2-hop 2-slot uplink proposal to infrastructure-based cellular networks. The proposed solution can significantly increase the throughput of uplink communication and reduce the energy consumption of wireless terminals. (orig.)

  5. Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

    International Nuclear Information System (INIS)

    Rao, R.V.; More, K.C.

    2017-01-01

    Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.

  6. Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline interpolation.

    Science.gov (United States)

    Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Yamamoto, Rie; Takayama, Kozo

    2013-01-01

    The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared based on a standard formulation. The tensile strength, disintegration time, and stability of these variables were measured as response variables. These responses were predicted quantitatively based on nonlinear TPS. A large amount of data on these tablets was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the tablets were classified into several distinct clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and tablet characteristics. The results of this study suggest that increasing the proportion of microcrystalline cellulose (MCC) improved the tensile strength and the stability of tensile strength of these theophylline tablets. In addition, the proportion of MCC has an optimum value for disintegration time and stability of disintegration. Increasing the proportion of magnesium stearate extended disintegration time. Increasing the compression force improved tensile strength, but degraded the stability of disintegration. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulations.

  7. A review of feature detection and match algorithms for localization and mapping

    Science.gov (United States)

    Li, Shimiao

    2017-09-01

    Localization and mapping is an essential ability of a robot to keep track of its own location in an unknown environment. Among existing methods for this purpose, vision-based methods are more effective solutions for being accurate, inexpensive and versatile. Vision-based methods can generally be categorized as feature-based approaches and appearance-based approaches. The feature-based approaches prove higher performance in textured scenarios. However, their performance depend highly on the applied feature-detection algorithms. In this paper, we surveyed algorithms for feature detection, which is an essential step in achieving vision-based localization and mapping. In this pater, we present mathematical models of the algorithms one after another. To compare the performances of the algorithms, we conducted a series of experiments on their accuracy, speed, scale invariance and rotation invariance. The results of the experiments showed that ORB is the fastest algorithm in detecting and matching features, the speed of which is more than 10 times that of SURF and approximately 40 times that of SIFT. And SIFT, although with no advantage in terms of speed, shows the most correct matching pairs and proves its accuracy.

  8. Ordination of self-organizing feature map neural networks and its application to the study of plant communities

    Institute of Scientific and Technical Information of China (English)

    Jintun ZHANG; Dongping MENG; Yuexiang XI

    2009-01-01

    A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

  9. Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

    Science.gov (United States)

    Oyama, Katsunori; Sakatani, Kaoru

    2016-01-01

    Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

  10. Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015 Using Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Adeoluwa Akande

    2017-01-01

    Full Text Available The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

  11. Application of self-organizing competition artificial neural network to logging data explanation of sandstone-hosted uranium deposits

    International Nuclear Information System (INIS)

    Xu Jianguo; Xu Xianli; Wang Weiguo

    2008-01-01

    The article describes the model construction of self-organizing competition artificial neural network, its principle and automatic recognition process of borehole lithology in detail, and then proves the efficiency of the neural network model for automatically recognizing the borehole lithology with some cases. The self-organizing competition artificial neural network has the ability of self- organization, self-adjustment and high permitting errors. Compared with the BP algorithm, it takes less calculation quantity and more rapidly converges. Furthermore, it can automatically confirm the category without the known sample information. Trial results based on contrasting the identification results of the borehole lithology with geological documentations, indicate that self-organizing artificial neural network can be well applied to automatically performing the category of borehole lithology, during the logging data explanation of sandstone-hosted uranium deposits. (authors)

  12. A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.

    Science.gov (United States)

    Zhao, Yongan; Wang, Xiaofeng; Tang, Haixu

    2018-05-09

    The elastic and inexpensive computing resources such as clouds have been recognized as a useful solution to analyzing massive human genomic data (e.g., acquired by using next-generation sequencers) in biomedical researches. However, outsourcing human genome computation to public or commercial clouds was hindered due to privacy concerns: even a small number of human genome sequences contain sufficient information for identifying the donor of the genomic data. This issue cannot be directly addressed by existing security and cryptographic techniques (such as homomorphic encryption), because they are too heavyweight to carry out practical genome computation tasks on massive data. In this article, we present a secure algorithm to accomplish the read mapping, one of the most basic tasks in human genomic data analysis based on a hybrid cloud computing model. Comparing with the existing approaches, our algorithm delegates most computation to the public cloud, while only performing encryption and decryption on the private cloud, and thus makes the maximum use of the computing resource of the public cloud. Furthermore, our algorithm reports similar results as the nonsecure read mapping algorithms, including the alignment between reads and the reference genome, which can be directly used in the downstream analysis such as the inference of genomic variations. We implemented the algorithm in C++ and Python on a hybrid cloud system, in which the public cloud uses an Apache Spark system.

  13. Spatial self-organization in a multi-strain host–pathogen system

    International Nuclear Information System (INIS)

    Liu, Quan-Xing; Van de Koppel, Johan; Wang, Rong-Hua; Jin, Zhen; Alonso, David

    2010-01-01

    We develop stochastic spatial epidemic models with the competition of two pathogenic strains. The dynamics resulting from different approaches are examined using both non-spatial and spatially explicit models. Our results show that pair approximation, well-mixed ordinary differential equations (ODEs), Gillespie-algorithm-based simulations and spatially explicit models give similar qualitative results. In particular, the temporal evolution of the spatial model can be successfully approximated by pair equations. Simulation results obtained from the spatially explicit model show that, first, mutation plays a major role in multi-strain coexistence, second, mild virulence remarkably decreases the coexistence domain of the parameter space and, third, large-scale self-organized spatial patterns emerge for a wide range of transmission and virulence parameter values, where spatial self-organized clusters reveal a power law behavior within the coexistence domain

  14. Numerical Algorithms for Personalized Search in Self-organizing Information Networks

    CERN Document Server

    Kamvar, Sep

    2010-01-01

    This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quad

  15. Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma

    NARCIS (Netherlands)

    D.T. Arnold (David); D. Rowen (Donna); M.M. Versteegh (Matthijs); A. Morley (Anna); C.E. Hooper (Clare); N.A. Maskell (Nicholas)

    2015-01-01

    markdownabstract__Background:__ In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in

  16. FODA: a novel efficient multiple access protocol for highly dynamic self-organizing networks

    Science.gov (United States)

    Li, Hantao; Liu, Kai; Zhang, Jun

    2005-11-01

    Based on the concept of contention reservation for polling transmission and collision prevention strategy for collision resolution, a fair on-demand access (FODA) protocol for supporting node mobility and multihop architecture in highly dynamic self-organizing networks is proposed. In the protocol, a distributed clustering network architecture formed by self-organizing algorithm and a main idea of reserving channel resources to get polling service are adopted, so that the hidden terminal (HT) and exposed terminal (ET) problems existed in traffic transmission due to multihop architecture and wireless transmission can be eliminated completely. In addition, an improved collision prevention scheme based on binary countdown algorithm (BCA), called fair collision prevention (FCP) algorithm, is proposed to greatly eliminate unfair phenomena existed in contention access of newly active ordinary nodes and completely resolve access collisions. Finally, the performance comparison of the FODA protocol with carrier sense multiple access with collision avoidance (CSMA/CA) and polling protocols by OPNET simulation are presented. Simulation results show that the FODA protocol can overcome the disadvantages of CSMA/CA and polling protocols, and achieve higher throughput, lower average message delay and less average message dropping rate.

  17. Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

    Directory of Open Access Journals (Sweden)

    Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

    2016-06-01

    Full Text Available Amino acids are the dominant organic components of processed animal proteins, however there has been limited investigation of differences in their composition between various protein sources. Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods. In this study, self-organizing feature maps (SOFM were used to visualize amino acid composition of fish meal, and meat and bone meal (MBM produced from poultry, ruminants and swine. SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency. Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine, lysine and proline. However, the amino acid composition of the three MBMs was quite similar. The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward. SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

  18. A self-organizing algorithm for modeling protein loops.

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    Pu Liu

    2009-08-01

    Full Text Available Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.

  19. Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

    Science.gov (United States)

    Chan, Chon-Kit Kenneth; Hsu, Arthur L.; Tang, Sen-Lin; Halgamuge, Saman K.

    2008-01-01

    Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%–15% speed improvement. PMID:18288261

  20. Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases.

    Science.gov (United States)

    Chavez-Alvarez, Rocio; Chavoya, Arturo; Mendez-Vazquez, Andres

    2014-01-01

    DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

  1. Brain Basis of Self: Self-Organization and Lessons from Dreaming

    Directory of Open Access Journals (Sweden)

    David eKahn

    2013-07-01

    Full Text Available Through dreaming a different facet of the self is created as a result of a self-organizing process in the brain. Self-organization in biological systems often happens as an answer to an environmental change for which the existing system cannot cope; self-organization creates a system that can cope in the newly changed environment. In dreaming, self-organization serves the function of organizing disparate memories into a dream since the dreamer herself is not able to control how individual memories become weaved into a dream. The self-organized dream provides, thereby, a wide repertoire of experiences; this expanded repertoire of experience results in an expansion of the self beyond that obtainable when awake. Since expression of the self is associated with activity in specific areas of the brain, the article also discusses the brain basis of the self by reviewing studies of brain injured patients, discussing brain imaging studies in normal brain functioning when focused, when daydreaming and when asleep and dreaming.

  2. A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

    OpenAIRE

    Zhang, Wei; Zhang, Xueying; Sun, Ying

    2017-01-01

    Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between e...

  3. A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

    Directory of Open Access Journals (Sweden)

    Quanli Xu

    2018-03-01

    Full Text Available Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

  4. Performance of non-parametric algorithms for spatial mapping of tropical forest structure

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    Liang Xu

    2016-08-01

    Full Text Available Abstract Background Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results By using airborne lidar data as the “truth” and focusing on the mean canopy height (MCH as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME and random forest (RF for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.

  5. A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

    International Nuclear Information System (INIS)

    Xiao Di; Cai Hong-Kun; Zheng Hong-Ying

    2015-01-01

    In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. (paper)

  6. Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

    Science.gov (United States)

    Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

    2017-07-01

    One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

  7. Effects of supervised Self Organising Maps parameters on classification performance.

    Science.gov (United States)

    Ballabio, Davide; Vasighi, Mahdi; Filzmoser, Peter

    2013-02-26

    Self Organising Maps (SOMs) are one of the most powerful learning strategies among neural networks algorithms. SOMs have several adaptable parameters and the selection of appropriate network architectures is required in order to make accurate predictions. The major disadvantage of SOMs is probably due to the network optimisation, since this procedure can be often time-expensive. Effects of network size, training epochs and learning rate on the classification performance of SOMs are known, whereas the effect of other parameters (type of SOMs, weights initialisation, training algorithm, topology and boundary conditions) are not so obvious. This study was addressed to analyse the effect of SOMs parameters on the network classification performance, as well as on their computational times, taking into consideration a significant number of real datasets, in order to achieve a comprehensive statistical comparison. Parameters were contemporaneously evaluated by means of an approach based on the design of experiments, which enabled the investigation of their interaction effects. Results highlighted the most important parameters which influence the classification performance and enabled the identification of the optimal settings, as well as the optimal architectures to reduce the computational time of SOMs. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells.

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    Praveen K Pilly

    Full Text Available Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous

  9. Contractive type non-self mappings on metric spaces of hyperbolic type

    Science.gov (United States)

    Ciric, Ljubomir B.

    2006-05-01

    Let (X,d) be a metric space of hyperbolic type and K a nonempty closed subset of X. In this paper we study a class of mappings from K into X (not necessarily self-mappings on K), which are defined by the contractive condition (2.1) below, and a class of pairs of mappings from K into X which satisfy the condition (2.28) below. We present fixed point and common fixed point theorems which are generalizations of the corresponding fixed point theorems of Ciric [L.B. Ciric, Quasi-contraction non-self mappings on Banach spaces, Bull. Acad. Serbe Sci. Arts 23 (1998) 25-31; L.B. Ciric, J.S. Ume, M.S. Khan, H.K.T. Pathak, On some non-self mappings, Math. Nachr. 251 (2003) 28-33], Rhoades [B.E. Rhoades, A fixed point theorem for some non-self mappings, Math. Japon. 23 (1978) 457-459] and many other authors. Some examples are presented to show that our results are genuine generalizations of known results from this area.

  10. DESYNC: Self-Organizing Desynchronization and TDMA on Wireless Sensor Networks

    OpenAIRE

    Degesys, Julius; Rose, Ian; Patel, Ankit; Nagpal, Radhika

    2006-01-01

    Desynchronization is a novel primitive for sensor networks: it implies that nodes perfectly interleave periodic events to occur in a round-robin schedule. This primitive can be used to evenly distribute sampling burden in a group of nodes, schedule sleep cycles, or organize a collision-free TDMA schedule for transmitting wireless messages. Here we present Desync, a biologically-inspired self-maintaining algorithm for desynchronization in a single-hop network. We present (1) theoretical result...

  11. Atmospheric Convective Organization: Self-Organized Criticality or Homeostasis?

    Science.gov (United States)

    Yano, Jun-Ichi

    2015-04-01

    Atmospheric convection has a tendency organized on a hierarchy of scales ranging from the mesoscale to the planetary scales, with the latter especially manifested by the Madden-Julian oscillation. The present talk examines two major possible mechanisms of self-organization identified in wider literature from a phenomenological thermodynamic point of view by analysing a planetary-scale cloud-resolving model simulation. The first mechanism is self-organized criticality. A saturation tendency of precipitation rate with the increasing column-integrated water, reminiscence of critical phenomena, indicates self-organized criticality. The second is a self-regulation mechanism that is known as homeostasis in biology. A thermodynamic argument suggests that such self-regulation maintains the column-integrated water below a threshold by increasing the precipitation rate. Previous analyses of both observational data as well as cloud-resolving model (CRM) experiments give mixed results. A satellite data analysis suggests self-organized criticality. Some observational data as well as CRM experiments support homeostasis. Other analyses point to a combination of these two interpretations. In this study, a CRM experiment over a planetary-scale domain with a constant sea-surface temperature is analyzed. This analysis shows that the relation between the column-integrated total water and precipitation suggests self-organized criticality, whereas the one between the column-integrated water vapor and precipitation suggests homeostasis. The concurrent presence of these two mechanisms are further elaborated by detailed statistical and budget analyses. These statistics are scale invariant, reflecting a spatial scaling of precipitation processes. These self-organization mechanisms are most likely be best theoretically understood by the energy cycle of the convective systems consisting of the kinetic energy and the cloud-work function. The author has already investigated the behavior of this

  12. Document Organization Using Kohonen's Algorithm.

    Science.gov (United States)

    Guerrero Bote, Vicente P.; Moya Anegon, Felix de; Herrero Solana, Victor

    2002-01-01

    Discussion of the classification of documents from bibliographic databases focuses on a method of vectorizing reference documents from LISA (Library and Information Science Abstracts) which permits their topological organization using Kohonen's algorithm. Analyzes possibilities of this type of neural network with respect to the development of…

  13. Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

    Science.gov (United States)

    Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

    2016-01-01

    A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

  14. A chaos wolf optimization algorithm with self-adaptive variable step-size

    Science.gov (United States)

    Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

    2017-10-01

    To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

  15. Assessment of habitat conditions using Self-Organizing Feature Maps for reintroduction/introduction of Aldrovanda vesiculosa L. in Poland

    Directory of Open Access Journals (Sweden)

    Piotr Kosiba

    2011-07-01

    Full Text Available The study objects were Aldrovanda vesiculosa L., an endangered species and fifty five water sites in Poland. The aim of the present work was to test the Self-Organizing Feature Map in order to examine and predict water properties and type of trophicity for restoration of the rare plant. Descriptive statistical parameters have been calculated, analysis of variance and cluster analysis were carried out and SOFM model has been constructed for analysed sites. The results of SOFM model and cluster analysis were compared. The study revealed that the ordination of individuals and groups of neurons in topological map of sites are similar in relation to dendrogram of cluster analysis, but not identical. The constructed SOFM model is related with significantly different contents of chemical water properties and type of trophicity. It appeared that sites with A. vesiculosa are predominantly distrophic and eutrophic waters shifted to distrophicity. The elevated model showed the sites with chemical properties favourable for restoration the species. Determined was the range of ecological tolerance of the species in relation to habitat conditions as stenotopic or relatively stenotopic in respect of the earlier accepted eutrophic status. The SOFM appeared to be a useful technique for ordination of ecological data and provides a novel framework for the discovery and forecasting of ecosystem properties constituting a validation of the SOFM method in this type of studies.

  16. Classification of sediments by means of Self-Organizing Maps and sediment quality guidelines in sites of the southern Spanish coastline

    Directory of Open Access Journals (Sweden)

    O. VESES

    2013-08-01

    Full Text Available This study was carried out to classify 112 marine and estuarine sites of the southern Spanish coastline (about 918 km long according to similar sediment characteristics by means of artificial neural networks (ANNs such as Self-Organizing Maps (SOM and sediment quality guidelines from a dataset consisted of 16 physical and chemical parameters including sediment granulometry, trace and major elements, total N and P and organic carbon content. The use of ANNs such as SOM made possible the classification of the sampling sites according to their similar chemical characteristics. Visual correlations between geochemical parameters were extracted due to the powerful visual characteristics (component planes of the SOM revealing that ANNs are an excellent tool to be incorporated in sediment quality assessments. Besides, almost 20% of the sites were classified as medium-high or high priority sites in order to take future remediation actions due to their high mean Effects Range-Median Quotient (m-ERMQ value. Priority sites included the estuaries of the major rivers (Tinto, Odiel, Palmones, etc. and several locations along the eastern coastline.

  17. Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets.

    Science.gov (United States)

    Lamu, Admassu N; Chen, Gang; Gamst-Klaussen, Thor; Olsen, Jan Abel

    2018-03-22

    To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets. Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r 2 . Cross-validation with fivefold approach was conducted to test the generalizability of each model. The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r 2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models. The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

  18. From self-organization to self-assembly: a new materialism?

    Science.gov (United States)

    Vincent, Bernadette Bensaude

    2016-09-01

    While self-organization has been an integral part of academic discussions about the distinctive features of living organisms, at least since Immanuel Kant's Critique of Judgement, the term 'self-assembly' has only been used for a few decades as it became a hot research topic with the emergence of nanotechnology. Could it be considered as an attempt at reducing vital organization to a sort of assembly line of molecules? Considering the context of research on self-assembly I argue that the shift of attention from self-organization to self-assembly does not really challenge the boundary between chemistry and biology. Self-assembly was first and foremost investigated in an engineering context as a strategy for manufacturing without human intervention and did not raise new perspectives on the emergence of vital organization itself. However self-assembly implies metaphysical assumptions that this paper tries to disentangle. It first describes the emergence of self-assembly as a research field in the context of materials science and nanotechnology. The second section outlines the metaphysical implications and will emphasize a sharp contrast between the ontology underlying two practices of self-assembly developed under the umbrella of synthetic biology. And unexpectedly, we shall see that chemists are less on the reductionist side than most synthetic biologists. Finally, the third section ventures some reflections on the kind of design involved in self-assembly practices.

  19. A Novel Self-Adaptive Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    Kaiping Luo

    2013-01-01

    Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self-adaptive variants for large-scale optimization problems and constrained problems.

  20. Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

    Directory of Open Access Journals (Sweden)

    Xu Wang

    2013-01-01

    Full Text Available A differential evolution (DE algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

  1. Innovative Mechanism of Rural Organization Based on Self-Organization

    OpenAIRE

    Wang, Xing jin; Gao, Bing

    2011-01-01

    The paper analyzes the basic situation for the formation of innovative rural organizations with the form of self-organization; revels the features of self-organization, including the four aspects of openness of rural organization, innovation of rural organization is far away from equilibrium, the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation. The evolution mechanism of rural organization innovation is reveled accor...

  2. Proposed hybrid-classifier ensemble algorithm to map snow cover area

    Science.gov (United States)

    Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

    2018-01-01

    Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

  3. Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Aleman, Dionne M [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, ON M5S 3G8 (Canada); Glaser, Daniel [Division of Optimization and Systems Theory, Department of Mathematics, Royal Institute of Technology, Stockholm (Sweden); Romeijn, H Edwin [Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117 (United States); Dempsey, James F, E-mail: aleman@mie.utoronto.c, E-mail: romeijn@umich.ed, E-mail: jfdempsey@viewray.co [ViewRay, Inc. 2 Thermo Fisher Way, Village of Oakwood, OH 44146 (United States)

    2010-09-21

    One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

  4. Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

    International Nuclear Information System (INIS)

    Aleman, Dionne M; Glaser, Daniel; Romeijn, H Edwin; Dempsey, James F

    2010-01-01

    One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

  5. Walkable self-overlapping virtual reality maze and map visualization demo

    DEFF Research Database (Denmark)

    Serubugo, Sule; Skantarova, Denisa; Evers, Nicolaj

    2017-01-01

    This paper describes our demonstration of a walkable self-overlapping maze and its corresponding map to facilitate asymmetric collaboration for room-scale virtual reality setups in public places.......This paper describes our demonstration of a walkable self-overlapping maze and its corresponding map to facilitate asymmetric collaboration for room-scale virtual reality setups in public places....

  6. A chaos wolf optimization algorithm with self-adaptive variable step-size

    Directory of Open Access Journals (Sweden)

    Yong Zhu

    2017-10-01

    Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

  7. Practical Constraint K-Segment Principal Curve Algorithms for Generating Railway GPS Digital Map

    Directory of Open Access Journals (Sweden)

    Dewang Chen

    2013-01-01

    Full Text Available In order to obtain a decent trade-off between the low-cost, low-accuracy Global Positioning System (GPS receivers and the requirements of high-precision digital maps for modern railways, using the concept of constraint K-segment principal curves (CKPCS and the expert knowledge on railways, we propose three practical CKPCS generation algorithms with reduced computational complexity, and thereafter more suitable for engineering applications. The three algorithms are named ALLopt, MPMopt, and DCopt, in which ALLopt exploits global optimization and MPMopt and DCopt apply local optimization with different initial solutions. We compare the three practical algorithms according to their performance on average projection error, stability, and the fitness for simple and complex simulated trajectories with noise data. It is found that ALLopt only works well for simple curves and small data sets. The other two algorithms can work better for complex curves and large data sets. Moreover, MPMopt runs faster than DCopt, but DCopt can work better for some curves with cross points. The three algorithms are also applied in generating GPS digital maps for two railway GPS data sets measured in Qinghai-Tibet Railway (QTR. Similar results like the ones in synthetic data are obtained. Because the trajectory of a railway is relatively simple and straight, we conclude that MPMopt works best according to the comprehensive considerations on the speed of computation and the quality of generated CKPCS. MPMopt can be used to obtain some key points to represent a large amount of GPS data. Hence, it can greatly reduce the data storage requirements and increase the positioning speed for real-time digital map applications.

  8. Innovative Mechanism of Rural Organization Based on Self-Organization

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The paper analyzes the basic situation of the formation of innovative rural organizations with the form of self-organization;reveals the features of self-organization,including the four aspects of openness of rural organization,innovation of rural organization far away from equilibrium,the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation.The evolution mechanism of rural organization innovation is revealed according to the growth stage,the ideal stage,the decline and the fall stage.The paper probes into the basic restriction mechanism of the self-organization evaluation of rural organization from three aspects,including target recognition,path dependence and knowledge sharing.The basic measures on cultivating the innovative mechanism of rural organization are put forward.Firstly,constructing the dissipative structure of rural organization innovation;secondly,cultivating the dynamic study capability of rural organization innovation system;thirdly,selecting the step-by-step evolution strategy of rural organization innovation system.

  9. A Self Adaptive Differential Evolution Algorithm for Global Optimization

    Science.gov (United States)

    Kumar, Pravesh; Pant, Millie

    This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

  10. Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

    Directory of Open Access Journals (Sweden)

    Nicholas Corso

    2013-12-01

    Full Text Available Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

  11. Dataflow-Based Mapping of Computer Vision Algorithms onto FPGAs

    Directory of Open Access Journals (Sweden)

    Ivan Corretjer

    2007-01-01

    Full Text Available We develop a design methodology for mapping computer vision algorithms onto an FPGA through the use of coarse-grain reconfigurable dataflow graphs as a representation to guide the designer. We first describe a new dataflow modeling technique called homogeneous parameterized dataflow (HPDF, which effectively captures the structure of an important class of computer vision applications. This form of dynamic dataflow takes advantage of the property that in a large number of image processing applications, data production and consumption rates can vary, but are equal across dataflow graph edges for any particular application iteration. After motivating and defining the HPDF model of computation, we develop an HPDF-based design methodology that offers useful properties in terms of verifying correctness and exposing performance-enhancing transformations; we discuss and address various challenges in efficiently mapping an HPDF-based application representation into target-specific HDL code; and we present experimental results pertaining to the mapping of a gesture recognition application onto the Xilinx Virtex II FPGA.

  12. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    Directory of Open Access Journals (Sweden)

    Haizhou Wu

    2016-01-01

    Full Text Available Symbiotic organisms search (SOS is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs. In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

  13. Self-organization is a dynamic and lineage-intrinsic property of mammary epithelial cells

    Energy Technology Data Exchange (ETDEWEB)

    Chanson, L. [Ecole Polytechnique Federale de Lausanne (Switzerland). Inst. of Bioengineering; Brownfield, D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.; Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Garbe, J. C. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.; Kuhn, I. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.; Stampfer, M. R. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.; Bissell, M. J. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.; LaBarge, M. A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Div.

    2011-02-07

    Loss of organization is a principle feature of cancers; therefore it is important to understand how normal adult multilineage tissues, such as bilayered secretory epithelia, establish and maintain their architectures. The self-organization process that drives heterogeneous mixtures of cells to form organized tissues is well studied in embryology and with mammalian cell lines that were abnormal or engineered. Here we used a micropatterning approach that confined cells to a cylindrical geometry combined with an algorithm to quantify changes of cellular distribution over time to measure the ability of different cell types to self-organize relative to each other. Using normal human mammary epithelial cells enriched into pools of the two principal lineages, luminal and myoepithelial cells, we demonstrated that bilayered organization in mammary epithelium was driven mainly by lineage-specific differential E-cadherin expression, but that P-cadherin contributed specifically to organization of the myoepithelial layer. Disruption of the actomyosin network or of adherens junction proteins resulted in either prevention of bilayer formation or loss of preformed bilayers, consistent with continual sampling of the local microenvironment by cadherins. Together these data show that self-organization is an innate and reversible property of communities of normal adult human mammary epithelial cells.

  14. Road network selection for small-scale maps using an improved centrality-based algorithm

    Directory of Open Access Journals (Sweden)

    Roy Weiss

    2014-12-01

    Full Text Available The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000 to a small-scale database (1:200,000. The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.

  15. Self-Organization and the Self-Assembling Process in Tissue Engineering

    Science.gov (United States)

    Eswaramoorthy, Rajalakshmanan; Hadidi, Pasha; Hu, Jerry C.

    2015-01-01

    In recent years, the tissue engineering paradigm has shifted to include a new and growing subfield of scaffoldless techniques which generate self-organizing and self-assembling tissues. This review aims to provide a cogent description of this relatively new research area, with special emphasis on applications toward clinical use and research models. Particular emphasis is placed on providing clear definitions of self-organization and the self-assembling process, as delineated from other scaffoldless techniques in tissue engineering and regenerative medicine. Significantly, during formation, self-organizing and self-assembling tissues display biological processes similar to those that occur in vivo. These help lead to the recapitulation of native tissue morphological structure and organization. Notably, functional properties of these tissues also approach native tissue values; some of these engineered tissues are already in clinical trials. This review aims to provide a cohesive summary of work in this field, and to highlight the potential of self-organization and the self-assembling process to provide cogent solutions to current intractable problems in tissue engineering. PMID:23701238

  16. Your space or mine? Mapping self in time.

    Directory of Open Access Journals (Sweden)

    Brittany M Christian

    Full Text Available While humans are capable of mentally transcending the here and now, this faculty for mental time travel (MTT is dependent upon an underlying cognitive representation of time. To this end, linguistic, cognitive and behavioral evidence has revealed that people understand abstract temporal constructs by mapping them to concrete spatial domains (e.g. past=backward, future=forward. However, very little research has investigated factors that may determine the topographical characteristics of these spatiotemporal maps. Guided by the imperative role of episodic content for retrospective and prospective thought (i.e., MTT, here we explored the possibility that the spatialization of time is influenced by the amount of episodic detail a temporal unit contains. In two experiments, participants mapped temporal events along mediolateral (Experiment 1 and anterioposterior (Experiment 2 spatial planes. Importantly, the temporal units varied in self-relevance as they pertained to temporally proximal or distal events in the participant's own life, the life of a best friend or the life of an unfamiliar other. Converging evidence from both experiments revealed that the amount of space used to represent time varied as a function of target (self, best friend or unfamiliar other and temporal distance. Specifically, self-time was represented as occupying more space than time pertaining to other targets, but only for temporally proximal events. These results demonstrate the malleability of space-time mapping and suggest that there is a self-specific conceptualization of time that may influence MTT as well as other temporally relevant cognitive phenomena.

  17. REMap: Operon Map of M. tuberculosis

    Science.gov (United States)

    Xia, Fang Fang; Stevens, Rick L.; Bishai, William R.; Lamichhane, Gyanu

    2016-01-01

    A map of the transcriptional organization of genes of an organism is a basic tool that is necessary to understand and facilitate a more accurate genetic manipulation of the organism. Operon maps are largely generated by computational prediction programs that rely on gene conservation and genome architecture and may not be physiologically relevant. With the widespread use of RNA sequencing (RNAseq), the prediction of operons based on actual transcriptome sequencing rather than computational genomics alone is much needed. Here, we report a validated operon map of Mycobacterium tuberculosis, developed using RNAseq data from both the exponential and stationary phases of growth. At least 58.4% of M. tuberculosis genes are organized into 749 operons. Our prediction algorithm, REMap (RNA Expression Mapping of operons), considers the many cases of transcription coverage of intergenic regions, and avoids dependencies on functional annotation and arbitrary assumptions about gene structure. As a result, we demonstrate that REMap is able to more accurately predict operons, especially those that contain long intergenic regions or functionally unrelated genes, than previous operon prediction programs. The REMap algorithm is publicly available as a user-friendly tool that can be readily modified to predict operons in other bacteria. PMID:27450008

  18. Self-organized Learning Environments

    DEFF Research Database (Denmark)

    Dalsgaard, Christian; Mathiasen, Helle

    2007-01-01

    system actively. The two groups used the system in their own way to support their specific activities and ways of working. The paper concludes that self-organized learning environments can strengthen the development of students’ academic as well as social qualifications. Further, the paper identifies......The purpose of the paper is to discuss the potentials of using a conference system in support of a project based university course. We use the concept of a self-organized learning environment to describe the shape of the course. In the paper we argue that educational technology, such as conference...... systems, has a potential to support students’ development of self-organized learning environments and facilitate self-governed activities in higher education. The paper is based on an empirical study of two project groups’ use of a conference system. The study showed that the students used the conference...

  19. Self-Adaptive Step Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Shuhao Yu

    2013-01-01

    Full Text Available In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.

  20. Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Nicholas S. Flann

    2013-09-01

    Full Text Available The Quantitative Trait Loci (QTL mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

  1. PREFACE: Self-organized nanostructures

    Science.gov (United States)

    Rousset, Sylvie; Ortega, Enrique

    2006-04-01

    In order to fabricate ordered arrays of nanostructures, two different strategies might be considered. The `top-down' approach consists of pushing the limit of lithography techniques down to the nanometre scale. However, beyond 10 nm lithography techniques will inevitably face major intrinsic limitations. An alternative method for elaborating ultimate-size nanostructures is based on the reverse `bottom-up' approach, i.e. building up nanostructures (and eventually assemble them to form functional circuits) from individual atoms or molecules. Scanning probe microscopies, including scanning tunnelling microscopy (STM) invented in 1982, have made it possible to create (and visualize) individual structures atom by atom. However, such individual atomic manipulation is not suitable for industrial applications. Self-assembly or self-organization of nanostructures on solid surfaces is a bottom-up approach that allows one to fabricate and assemble nanostructure arrays in a one-step process. For applications, such as high density magnetic storage, self-assembly appears to be the simplest alternative to lithography for massive, parallel fabrication of nanostructure arrays with regular sizes and spacings. These are also necessary for investigating the physical properties of individual nanostructures by means of averaging techniques, i.e. all those using light or particle beams. The state-of-the-art and the current developments in the field of self-organization and physical properties of assembled nanostructures are reviewed in this issue of Journal of Physics: Condensed Matter. The papers have been selected from among the invited and oral presentations of the recent summer workshop held in Cargese (Corsica, France, 17-23 July 2005). All authors are world-renowned in the field. The workshop has been funded by the Marie Curie Actions: Marie Curie Conferences and Training Courses series named `NanosciencesTech' supported by the VI Framework Programme of the European Community, by

  2. Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization

    Science.gov (United States)

    Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei

    2014-04-01

    Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

  3. The (1+λ) evolutionary algorithm with self-adjusting mutation rate

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Witt, Carsten; Gießen, Christian

    2017-01-01

    We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then upd......We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate...... is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the (1 + A) evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the (1 + A) EA finds the optimum in an expected optimization...... time (number of fitness evaluations) of O(nA/log A + n log n). This time is asymptotically smaller than the optimization time of the classic (1 + A) EA. Previous work shows that this performance is best-possible among all A-parallel mutation-based unbiased black-box algorithms. This result shows...

  4. Self-Organization in Embedded Real-Time Systems

    CERN Document Server

    Brinkschulte, Uwe; Rettberg, Achim

    2013-01-01

    This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems.  Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...

  5. NOAA Office of Exploration and Research > About OER > Organization > Map of

    Science.gov (United States)

    About OER Overview Organization Guiding Documents Organizational Structure Map of Staff and Affiliate of Staff and Affiliate Locations About OER Organization Map of Staff and Affiliate Locations Home About OER Overview Organization Guiding Documents Organizational Structure Map of Staff and Affiliate

  6. Constructing Ozone Profile Climatologies with Self-Organizing Maps: Illustrations with CONUS Ozonesonde Data

    Science.gov (United States)

    Thompson, A. M.; Stauffer, R. M.; Young, G. S.

    2015-12-01

    Ozone (O3) trends analysis is typically performed with monthly or seasonal averages. Although this approach works well for stratospheric or total O3, uncertainties in tropospheric O3 amounts may be large due to rapid meteorological changes near the tropopause and in the lower free troposphere (LFT) where pollution has a days-weeks lifetime. We use self-organizing maps (SOM), a clustering technique, as an alternative for creating tropospheric climatologies from O3 soundings. In a previous study of 900 tropical ozonesondes, clusters representing >40% of profiles deviated > 1-sigma from mean O­3. Here SOM are based on 15 years of data from four sites in the contiguous US (CONUS; Boulder, CO; Huntsville, AL; Trinidad Head, CA; Wallops Island, VA). Ozone profiles from 2 - 12 km are used to evaluate the impact of tropopause variability on climatology; 2 - 6 km O3 profile segments are used for the LFT. Near-tropopause O­3 is twice the mean O­3 mixing ratio in three clusters of 2 - 12 km O3, representing > 15% of profiles at each site. Large mid and lower-tropospheric O3 deviations from monthly means are found in clusters of both 2 - 12 and 2 - 6 km O3. Positive offsets result from pollution and stratosphere-to-troposphere exchange. In the LFT the lowest tropospheric O3 is associated with subtropical air. Some clusters include profiles with common seasonality but other factors, e.g., tropopause height or LFT column amount, characterize other SOM nodes. Thus, as for tropical profiles, CONUS O­3 averages can be a poor choice for a climatology.

  7. Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms.

    Science.gov (United States)

    N'Diaye, Amidou; Haile, Jemanesh K; Fowler, D Brian; Ammar, Karim; Pozniak, Curtis J

    2017-01-01

    Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP) markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called 'large p, small n' problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers). While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat) and Norstar × Cappelle Desprez (bread wheat). The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF), we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez). Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase making map expansion

  8. Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Amidou N’Diaye

    2017-08-01

    Full Text Available Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called ‘large p, small n’ problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers. While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat and Norstar × Cappelle Desprez (bread wheat. The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF, we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez. Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase

  9. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling

    International Nuclear Information System (INIS)

    He Yaoyao; Zhou Jianzhong; Xiang Xiuqiao; Chen Heng; Qin Hui

    2009-01-01

    The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydroelectric system scheduling. The introduced chaotic maps improve the global optimal capability of CPSO algorithm. Moreover, a piecewise linear interpolation function is employed to transform all constraints into restrict upriver water level for implementing the maximum of objective function. Numerical results and comparisons demonstrate the effect and speed of different algorithms on a practical hydro-system.

  10. Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario

    Directory of Open Access Journals (Sweden)

    Huiyan Chen

    2013-07-01

    Full Text Available To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC areas, such as roads with lanes, and loose constraint (LC areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

  11. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  12. Hardware Implementation of a Modified Delay-Coordinate Mapping-Based QRS Complex Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Andrej Zemva

    2007-01-01

    Full Text Available We present a modified delay-coordinate mapping-based QRS complex detection algorithm, suitable for hardware implementation. In the original algorithm, the phase-space portrait of an electrocardiogram signal is reconstructed in a two-dimensional plane using the method of delays. Geometrical properties of the obtained phase-space portrait are exploited for QRS complex detection. In our solution, a bandpass filter is used for ECG signal prefiltering and an improved method for detection threshold-level calculation is utilized. We developed the algorithm on the MIT-BIH Arrhythmia Database (sensitivity of 99.82% and positive predictivity of 99.82% and tested it on the long-term ST database (sensitivity of 99.72% and positive predictivity of 99.37%. Our algorithm outperforms several well-known QRS complex detection algorithms, including the original algorithm.

  13. A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Apisit Eiumnoh

    2013-10-01

    Full Text Available Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF model to simultaneously align two or more images and obtain a land cover map (LCM of the scene. The expectation maximization (EM algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

  14. A concept of vehicle-based collision prevention in self-organized railway systems (SOG); Konzept zur fahrzeuggestuetzten Kollisionsvermeidung im selbstorganisierenden Gueterverkehr (SOG)

    Energy Technology Data Exchange (ETDEWEB)

    Vallee, H.

    2002-07-01

    Self-organizing vehicles are a revolution in railway transport: They find their own way in the available railway network, and they detect and solve conflicts encountered on the way. - The report presents the algorithms for conflict identification and solution. Conflicts are mapped according to the number of conflict sites, the travelling direction of the vehicles in the conflict sites, and particularities of the programmed destinations. Solution schemes are presented for each type of conflict. The applicability of the algorithms is illustrated by a simulation and in trial operation of real vehicles. [German] Selbstorganisierend verkehrende Fahrzeuge revolutionieren den Schienengueterverkehr. Sie suchen ihren Fahrweg durch das ihnen zur Verfuegung stehende Gleisnetz selbst. Sie erkennen und loesen Konflikte innerhalb der von ihnen gewaehlten Fahrwege. - In dieser Arbeit werden die Algorithmen fuer die Konflikterkennung und Konfliktloesung beschrieben. Konflikte werden in Konfliktarten eingeteilt. Kriterien dafuer sind die Anzahl der Konfliktorte, die Fahrtrichtungen der Fahrzeuge innerhalb der Konfliktorte sowie Besonderheiten der programmierten Zielorte. Fuer jede Konfliktart werden schematisierte Loesungen angegeben. Die Tauglichkeit der Algorithmen wurde in einer Simulation und einem Testbetrieb mit realen Fahrzeugen gezeigt. (orig.)

  15. Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

    Science.gov (United States)

    Peiró-Velert, Carmen; Valencia-Peris, Alexandra; González, Luis M; García-Massó, Xavier; Serra-Añó, Pilar; Devís-Devís, José

    2014-01-01

    Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and to improve

  16. Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

    Directory of Open Access Journals (Sweden)

    Carmen Peiró-Velert

    Full Text Available Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and

  17. Structural characterization of self-assembled semiconductor islands by three-dimensional X-ray diffraction mapping in reciprocal space

    International Nuclear Information System (INIS)

    Holy, V.; Mundboth, K.; Mokuta, C.; Metzger, T.H.; Stangl, J.; Bauer, G.; Boeck, T.; Schmidbauer, M.

    2008-01-01

    For the first time self-organized epitaxially grown semiconductor islands were investigated by a full three-dimensional mapping of the scattered X-ray intensity in reciprocal space. Intensity distributions were measured in a coplanar diffraction geometry around symmetric and asymmetric Bragg reflections. The 3D intensity maps were compared with theoretical simulations based on continuum-elasticity simulations of internal strains in the islands and on kinematical scattering theory whereby local chemical composition and strain profiles of the islands were retrieved

  18. Empirical study of self-configuring genetic programming algorithm performance and behaviour

    International Nuclear Information System (INIS)

    KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkin, E; KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkina, M

    2015-01-01

    The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems

  19. Self-organizing sensing and actuation for automatic control

    Science.gov (United States)

    Cheng, George Shu-Xing

    2017-07-04

    A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

  20. Using parallel factor analysis modeling (PARAFAC) and self-organizing maps to track senescence-induced patterns in leaf litter leachate

    Science.gov (United States)

    Wheeler, K. I.; Levia, D. F., Jr.; Hudson, J. E.

    2017-12-01

    As trees undergo autumnal processes such as resorption, senescence, and leaf abscission, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams changes. However, little research has investigated how the fluorescent DOM (FDOM) changes throughout the autumn and how this differs inter- and intraspecifically. Two of the major impacts of global climate change on forested ecosystems include altering phenology and causing forest community species and subspecies composition restructuring. We examined changes in FDOM in leachate from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and yellow poplar (Liriodendron tulipifera L.) leaves from Maryland throughout three different phenophases: green, senescing, and freshly abscissed. Beech leaves from Maryland and Rhode Island have previously been identified as belonging to the same distinct genetic cluster and beech trees from Vermont and the study site in North Carolina from the other. FDOM in samples was characterized using excitation-emission matrices (EEMs) and a six-component parallel factor analysis (PARAFAC) model was created to identify components. Self-organizing maps (SOMs) were used to visualize variation and patterns in the PARAFAC component proportions of the leachate samples. Phenophase and species had the greatest influence on determining where a sample mapped on the SOM when compared to genetic clusters and geographic origin. Throughout senescence, FDOM from all the trees transitioned from more protein-like components to more humic-like ones. Percent greenness of the sampled leaves and the proportion of the tyrosine-like component 1 were found to significantly differ between the two genetic beech clusters. This suggests possible differences in photosynthesis and resorption between the two genetic clusters of beech. The use of SOMs to visualize differences in patterns of senescence between the different species and genetic

  1. Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains

    Directory of Open Access Journals (Sweden)

    Volker Turau

    2018-05-01

    Full Text Available The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.

  2. Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

    Science.gov (United States)

    Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

    2014-03-01

    The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  3. Microscope self-calibration based on micro laser line imaging and soft computing algorithms

    Science.gov (United States)

    Apolinar Muñoz Rodríguez, J.

    2018-06-01

    A technique to perform microscope self-calibration via micro laser line and soft computing algorithms is presented. In this technique, the microscope vision parameters are computed by means of soft computing algorithms based on laser line projection. To implement the self-calibration, a microscope vision system is constructed by means of a CCD camera and a 38 μm laser line. From this arrangement, the microscope vision parameters are represented via Bezier approximation networks, which are accomplished through the laser line position. In this procedure, a genetic algorithm determines the microscope vision parameters by means of laser line imaging. Also, the approximation networks compute the three-dimensional vision by means of the laser line position. Additionally, the soft computing algorithms re-calibrate the vision parameters when the microscope vision system is modified during the vision task. The proposed self-calibration improves accuracy of the traditional microscope calibration, which is accomplished via external references to the microscope system. The capability of the self-calibration based on soft computing algorithms is determined by means of the calibration accuracy and the micro-scale measurement error. This contribution is corroborated by an evaluation based on the accuracy of the traditional microscope calibration.

  4. An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

    Science.gov (United States)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

  5. System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

    Science.gov (United States)

    Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

    2014-06-17

    A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

  6. An Improved Piecewise Linear Chaotic Map Based Image Encryption Algorithm

    Directory of Open Access Journals (Sweden)

    Yuping Hu

    2014-01-01

    Full Text Available An image encryption algorithm based on improved piecewise linear chaotic map (MPWLCM model was proposed. The algorithm uses the MPWLCM to permute and diffuse plain image simultaneously. Due to the sensitivity to initial key values, system parameters, and ergodicity in chaotic system, two pseudorandom sequences are designed and used in the processes of permutation and diffusion. The order of processing pixels is not in accordance with the index of pixels, but it is from beginning or end alternately. The cipher feedback was introduced in diffusion process. Test results and security analysis show that not only the scheme can achieve good encryption results but also its key space is large enough to resist against brute attack.

  7. ROAD DETECTION BY NEURAL AND GENETIC ALGORITHM IN URBAN ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    A. Barsi

    2012-07-01

    Full Text Available In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.

  8. A diagnostic algorithm to optimize data collection and interpretation of Ripple Maps in atrial tachycardias.

    Science.gov (United States)

    Koa-Wing, Michael; Nakagawa, Hiroshi; Luther, Vishal; Jamil-Copley, Shahnaz; Linton, Nick; Sandler, Belinda; Qureshi, Norman; Peters, Nicholas S; Davies, D Wyn; Francis, Darrel P; Jackman, Warren; Kanagaratnam, Prapa

    2015-11-15

    Ripple Mapping (RM) is designed to overcome the limitations of existing isochronal 3D mapping systems by representing the intracardiac electrogram as a dynamic bar on a surface bipolar voltage map that changes in height according to the electrogram voltage-time relationship, relative to a fiduciary point. We tested the hypothesis that standard approaches to atrial tachycardia CARTO™ activation maps were inadequate for RM creation and interpretation. From the results, we aimed to develop an algorithm to optimize RMs for future prospective testing on a clinical RM platform. CARTO-XP™ activation maps from atrial tachycardia ablations were reviewed by two blinded assessors on an off-line RM workstation. Ripple Maps were graded according to a diagnostic confidence scale (Grade I - high confidence with clear pattern of activation through to Grade IV - non-diagnostic). The RM-based diagnoses were corroborated against the clinical diagnoses. 43 RMs from 14 patients were classified as Grade I (5 [11.5%]); Grade II (17 [39.5%]); Grade III (9 [21%]) and Grade IV (12 [28%]). Causes of low gradings/errors included the following: insufficient chamber point density; window-of-interestRipple Maps in atrial tachycardias. This algorithm requires prospective testing on a real-time clinical platform. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  9. Self-Organizing Maps: A Data Mining Tool for the Analysis of Airborne Geophysical Data Collected over the Brazilian Amazon

    Science.gov (United States)

    Carneiro, C.; Fraser, S. J.; Crosta, A. P.; Silva, A.; Barros, C.

    2011-12-01

    Regional airborne geophysical data sets are being collected worldwide to promote mineral exploration and resource development. These data sets often are collected over highly prospective terranes, where access is limited or there are environmental concerns. Such regional surveys typically consist of two or more sensor packages being flown in an aircraft over the survey area and vast amounts of near-continuous data can be acquired in a relatively short time. Increasingly, there is also a need to process such data in a timely fashion to demonstrate the data's value and indicate the potential return or value of the survey to the funding agency. To assist in the timely analysis of such regional data sets, we have used an exploratory data mining approach: the Self Organizing Map (SOM). Because SOM is based on vector quantization and measures of vector similarity, it is an ideal tool to analyze a data set consisting of disparate geophysical input parameters to look for relationships and trends. We report on our use of SOM to analyze part of a regional airborne geophysical survey collected over the prospective Anapu-Tuere region of the Brazilian Amazon. Magnetic and spectrometric gamma ray data were used as input to our SOM analysis, and the results used to discriminate and identify various rock types and produce a "pseudo" geological map over the study area. The ability of SOM to define discrete domains of rock-types with similar properties allowed us to expand upon existing geological knowledge of the area for mapping purposes; and, often it was the combination of the magnetic and radiometric responses that identified a lithology's unique response. One particular unit was identified that had an association with known gold mineralization, which consequently highlighted the prospectivity of that unit elsewhere in the survey area. Our results indicate that SOM can be used for the semi-automatic analysis of regional airborne geophysical data to assist in geological mapping

  10. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  11. A Hierarchical and Distributed Approach for Mapping Large Applications to Heterogeneous Grids using Genetic Algorithms

    Science.gov (United States)

    Sanyal, Soumya; Jain, Amit; Das, Sajal K.; Biswas, Rupak

    2003-01-01

    In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.

  12. Spatial Mapping of Organic Carbon in Returned Samples from Mars

    Science.gov (United States)

    Siljeström, S.; Fornaro, T.; Greenwalt, D.; Steele, A.

    2018-04-01

    To map organic material spatially to minerals present in the sample will be essential for the understanding of the origin of any organics in returned samples from Mars. It will be shown how ToF-SIMS may be used to map organics in samples from Mars.

  13. Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm

    Science.gov (United States)

    Bian, Haiyi; Wang, Peng; Wang, Jun; Yin, Huancai; Tian, Yubing; Bai, Pengli; Wu, Xiaodong; Wang, Ning; Tang, Yuguo; Gao, Jing

    2017-09-01

    We report a self-reference algorithm to discriminate human and nonhuman blood by calculating the ratios of identification Raman peaks to reference Raman peaks and choosing appropriate threshold values. The influence of using different reference peaks and identification peaks was analyzed in detail. The Raman peak at 1003 cm-1 was proved to be a stable reference peak to avoid the influencing factors, such as the incident laser intensity and the amount of sample. The Raman peak at 1341 cm-1 was found to be an efficient identification peak, which indicates that the difference between human and nonhuman blood results from the C-H bend in tryptophan. The comparison between self-reference algorithm and partial least square method was made. It was found that the self-reference algorithm not only obtained the discrimination results with the same accuracy, but also provided information on the difference of chemical composition. In addition, the performance of self-reference algorithm whose true positive rate is 100% is significant for customs inspection to avoid genetic disclosure and forensic science.

  14. New algorithm improves fine structure of the barley consensus SNP map

    Directory of Open Access Journals (Sweden)

    Endelman Jeffrey B

    2011-08-01

    Full Text Available Abstract Background The need to integrate information from multiple linkage maps is a long-standing problem in genetics. One way to visualize the complex ordinal relationships is with a directed graph, where each vertex in the graph is a bin of markers. When there are no ordering conflicts between the linkage maps, the result is a directed acyclic graph, or DAG, which can then be linearized to produce a consensus map. Results New algorithms for the simplification and linearization of consensus graphs have been implemented as a package for the R computing environment called DAGGER. The simplified consensus graphs produced by DAGGER exactly capture the ordinal relationships present in a series of linkage maps. Using either linear or quadratic programming, DAGGER generates a consensus map with minimum error relative to the linkage maps while remaining ordinally consistent with them. Both linearization methods produce consensus maps that are compressed relative to the mean of the linkage maps. After rescaling, however, the consensus maps had higher accuracy (and higher marker density than the individual linkage maps in genetic simulations. When applied to four barley linkage maps genotyped at nearly 3000 SNP markers, DAGGER produced a consensus map with improved fine structure compared to the existing barley consensus SNP map. The root-mean-squared error between the linkage maps and the DAGGER map was 0.82 cM per marker interval compared to 2.28 cM for the existing consensus map. Examination of the barley hardness locus at the 5HS telomere, for which there is a physical map, confirmed that the DAGGER output was more accurate for fine structure analysis. Conclusions The R package DAGGER is an effective, freely available resource for integrating the information from a set of consistent linkage maps.

  15. Synthesis Road Map Problems in Organic Chemistry

    Science.gov (United States)

    Schaller, Chris P.; Graham, Kate J.; Jones, T. Nicholas

    2014-01-01

    Road map problems ask students to integrate their knowledge of organic reactions with pattern recognition skills to "fill in the blanks" in the synthesis of an organic compound. Students are asked to identify familiar organic reactions in unfamiliar contexts. A practical context, such as a medicinally useful target compound, helps…

  16. Chaoticity of interval self-maps with positive entropy

    International Nuclear Information System (INIS)

    Xiong Jincheng.

    1988-12-01

    Li and Yorke originally introduced the notion of chaos for continuous self-map of the interval I = (0,1). In the present paper we show that an interval self-map with positive topological entropy has a chaoticity more complicated than the chaoticity in the sense of Li and Yorke. The main result is that if f:I → I is continuous and has a periodic point with odd period > 1 then there exists a closed subset K of I invariant with respect to f such that the periodic points are dense in K, the periods of periodic points in K form an infinite set and f|K is topologically mixing. (author). 9 refs

  17. Black hole algorithm for determining model parameter in self-potential data

    Science.gov (United States)

    Sungkono; Warnana, Dwa Desa

    2018-01-01

    Analysis of self-potential (SP) data is increasingly popular in geophysical method due to its relevance in many cases. However, the inversion of SP data is often highly nonlinear. Consequently, local search algorithms commonly based on gradient approaches have often failed to find the global optimum solution in nonlinear problems. Black hole algorithm (BHA) was proposed as a solution to such problems. As the name suggests, the algorithm was constructed based on the black hole phenomena. This paper investigates the application of BHA to solve inversions of field and synthetic self-potential (SP) data. The inversion results show that BHA accurately determines model parameters and model uncertainty. This indicates that BHA is highly potential as an innovative approach for SP data inversion.

  18. The Self-Organized Archive: SPASE, PDS and Archive Cooperatives

    Science.gov (United States)

    King, T. A.; Hughes, J. S.; Roberts, D. A.; Walker, R. J.; Joy, S. P.

    2005-05-01

    Information systems with high quality metadata enable uses and services which often go beyond the original purpose. There are two types of metadata: annotations which are items that comment on or describe the content of a resource and identification attributes which describe the external properties of the resource itself. For example, annotations may indicate which columns are present in a table of data, whereas an identification attribute would indicate source of the table, such as the observatory, instrument, organization, and data type. When the identification attributes are collected and used as the basis of a search engine, a user can constrain on an attribute, the archive can then self-organize around the constraint, presenting the user with a particular view of the archive. In an archive cooperative where each participating data system or archive may have its own metadata standards, providing a multi-system search engine requires that individual archive metadata be mapped to a broad based standard. To explore how cooperative archives can form a larger self-organized archive we will show how the Space Physics Archive Search and Extract (SPASE) data model will allow different systems to create a cooperative and will use Planetary Data System (PDS) plus existing space physics activities as a demonstration.

  19. Optimization algorithms intended for self-tuning feedwater heater model

    International Nuclear Information System (INIS)

    Czop, P; Barszcz, T; Bednarz, J

    2013-01-01

    This work presents a self-tuning feedwater heater model. This work continues the work on first-principle gray-box methodology applied to diagnostics and condition assessment of power plant components. The objective of this work is to review and benchmark the optimization algorithms regarding the time required to achieve the best model fit to operational power plant data. The paper recommends the most effective algorithm to be used in the model adjustment process.

  20. Self-organization phenomena in plasma physics

    International Nuclear Information System (INIS)

    Sanduloviciu, M.; Popescu, S.

    2001-01-01

    The self-assembling in nature and laboratory of structures in systems away from thermodynamic equilibrium is one of the problems that mostly fascinates the scientists working in all branches of science. In this context a substantial progress has been obtained by investigating the appearance of spatial and spatiotemporal patterns in plasma. These experiments revealed the presence of a scenario of self-organization able to suggest an answer to the central problem of the 'Science of Complexity', why matter transits spontaneously from a disordered into an ordered state? Based on this scenario of self-organization we present arguments proving the possibility to explain the challenging problems of nonequilibrium physics in general. These problems refer to: (i) genuine origin of phase transitions observed in gaseous conductors and semiconductors; (ii) the elucidation of the role played by self-organization in the simulation of oscillations; (iii) the physical basis of anomalous transport of matter and energy with special reference to the possibilities of improving the economical performance of fusion devices; (iv) the possibility to use self-confined gaseous space charged configurations as an alternative to the magnetically confined plasma used at present in fusion devices. In other branches of sciences, as for instance in Biology, the self-organization scenario reveals a new insight into a mechanism able to explain the appearance of the simplest possible space charge configuration able to evolve, under suitable conditions, into prebiotic structures. Referring to phenomena observed in nature, the same self-organization scenario suggests plausible answers to the appearance of ball lightening but also to the origin of the flickering phenomena observed in the light emission of the Sun and stars. For theory the described self-organization scenario offers a new physical basis for many problems of nonlinear science not solved yet and also a new model for the so-called 'self

  1. Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach

    Science.gov (United States)

    Friedel, Michael J.

    2011-01-01

    Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.

  2. Self-organizing map network-based precipitation regionalization for the Tibetan Plateau and regional precipitation variability

    Science.gov (United States)

    Wang, Nini; Yin, Jianchuan

    2017-12-01

    A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the period 1900-2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ˜12 months, except for the representative grid of the northwestern region.

  3. MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization

    Directory of Open Access Journals (Sweden)

    Kellermann Walter

    2007-01-01

    Full Text Available We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.

  4. A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System

    Science.gov (United States)

    Wang, Jian; Hu, Andong; Liu, Chunyan; Li, Xin

    2015-01-01

    This paper proposes a scheme for indoor positioning by fusing floor map, WiFi and smartphone sensor data to provide meter-level positioning without additional infrastructure. A topology-constrained K nearest neighbor (KNN) algorithm based on a floor map layout provides the coordinates required to integrate WiFi data with pseudo-odometry (P-O) measurements simulated using a pedestrian dead reckoning (PDR) approach. One method of further improving the positioning accuracy is to use a more effective multi-threshold step detection algorithm, as proposed by the authors. The “go and back” phenomenon caused by incorrect matching of the reference points (RPs) of a WiFi algorithm is eliminated using an adaptive fading-factor-based extended Kalman filter (EKF), taking WiFi positioning coordinates, P-O measurements and fused heading angles as observations. The “cross-wall” problem is solved based on the development of a floor-map-aided particle filter algorithm by weighting the particles, thereby also eliminating the gross-error effects originating from WiFi or P-O measurements. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that the proposed scheme can reliably achieve meter-level positioning. PMID:25811224

  5. Surface stress and large-scale self-organization at organic-metal interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Pollinger, Florian

    2009-01-22

    The role of elastic interactions, particularly for the self-organized formation of periodically faceted interfaces, was investigated in this thesis for archetype organic-metal interfaces. The cantilever bending technique was applied to study the change of surface stress upon formation of the interface between 3,4,9,10-perylene-tetracarboxylic-dianhydride (PTCDA) and Ag(111). The main focus of this work was on the investigation of the formation of the long-range ordered, self-organized faceted PTCDA/Ag(10 8 7) interface. Reciprocal space maps of this interface were recorded both by spot profile analysis low energy electron diffraction (SPA-LEED) and low energy electron microscopy (LEEM) in selected area LEED mode. Complementary to the reciprocal data, also microscopic real-space LEEM data were used to characterize the morphology of this interface. Six different facet faces ((111), (532), (743), (954), (13 9 5), and (542)) were observed for the preparation path of molecular adsorption on the substrate kept at 550 K. Facet-sensitive dark-field LEEM localized these facets to grow in homogeneous areas of microscopic extensions. The temperature-dependence of the interface formation was studied in a range between 418 K and 612 K in order to learn more about the kinetics of the process. Additional steeper facets of 27 inclination with respect to the (111) surface were observed in the low temperature regime. Furthermore, using facet-sensitive dark-field LEEM, spatial and size distributions of specific facets were studied for the different temperatures. Moreover, the facet dimensions were statistically analyzed. The total island size of the facets follows an exponential distribution, indicating a random growth mode in absence of any mutual facet interactions. While the length distribution of the facets also follows an exponential distribution, the width distribution is peaked, reflecting the high degree of lateral order. This anisotropy is temperature-dependent and occurs

  6. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

    Science.gov (United States)

    Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

    2009-07-01

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

  7. A Hybrid Genetic-Algorithm Space-Mapping Tool for the Optimization of Antennas

    DEFF Research Database (Denmark)

    Pantoja, Mario Fernández; Meincke, Peter; Bretones, Amelia Rubio

    2007-01-01

    A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a genetic algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...

  8. Non-Taylor magnetohydrodynamic self-organization

    International Nuclear Information System (INIS)

    Zhu, Shao-ping; Horiuchi, Ritoku; Sato, Tetsuya.

    1994-10-01

    A self-organization process in a plasma with a finite pressure is investigated by means of a three-dimensional magnetohydrodynamic simulation. It is demonstrated that a non-Taylor finite β self-organized state is realized in which a perpendicular component of the electric current is generated and the force-free(parallel) current decreases until they reach to almost the same level. The self-organized state is described by an MHD force-balance relation, namely, j perpendicular = B x ∇p/B·B and j parallel = μB where μ is not a constant, and the pressure structure resembles the structure of the toroidal magnetic field intensity. Unless an anomalous perpendicular thermal conduction arises, the plasma cannot relax to a Taylor state but to a non-Taylor (non-force-free) self-organized state. This state becomes more prominent for a weaker resistivity condition. The non-Taylor state has a rather universal property, for example, independence of the initial β value. Another remarkable finding is that the Taylor's conjecture of helicity conservation is, in a strict sense, not valid. The helicity dissipation occurs and its rate slows down critically in accordance with the stepwise relaxation of the magnetic energy. It is confirmed that the driven magnetic reconnection caused by the nonlinearly excited plasma kink flows plays the leading role in all of these key features of the non-Taylor self-organization. (author)

  9. The Resource Mapping Algorithm of Wireless Virtualized Networks for Saving Energy in Ultradense Small Cells

    Directory of Open Access Journals (Sweden)

    Sai Zou

    2015-01-01

    Full Text Available As the current network is designed for peak loads, it results in insufficient resource utilization and energy waste. Virtualized technology makes it possible that intelligent energy perception network could be deployed and resource sharing could become an effective energy saving technology. How to make more small cells into sleeping state for energy saving in ultradense small cell system has become a research hot spot. Based on the mapping feature of virtualized network, a new wireless resource mapping algorithm for saving energy in ultradense small cells has been put forward when wireless resource amount is satisfied in every small cell. First of all, the method divides the virtual cells. Again through the alternate updating between small cell mapping and wireless resource allocation, least amount of small cells is used and other small cells turn into sleeping state on the premise of guaranteeing users’ QoS. Next, the energy consumption of the wireless access system, wireless resource utilization, and the convergence of the proposed algorithm are analyzed in theory. Finally, the simulation results demonstrate that the algorithm can effectively reduce the system energy consumption and required wireless resource amount under the condition of satisfying users’ QoS.

  10. The concept of self-organizing systems. Why bother?

    Science.gov (United States)

    Elverfeldt, Kirsten v.; Embleton-Hamann, Christine; Slaymaker, Olav

    2016-04-01

    Complexity theory and the concept of self-organizing systems provide a rather challenging conceptual framework for explaining earth systems change. Self-organization - understood as the aggregate processes internal to an environmental system that lead to a distinctive spatial or temporal organization - reduces the possibility of implicating a specific process as being causal, and it poses some restrictions on the idea that external drivers cause a system to change. The concept of self-organizing systems suggests that many phenomena result from an orchestration of different mechanisms, so that no causal role can be assigned to an individual factor or process. The idea that system change can be due to system-internal processes of self-organization thus proves a huge challenge to earth system research, especially in the context of global environmental change. In order to understand the concept's implications for the Earth Sciences, we need to know the characteristics of self-organizing systems and how to discern self-organizing systems. Within the talk, we aim firstly at characterizing self-organizing systems, and secondly at highlighting the advantages and difficulties of the concept within earth system sciences. The presentation concludes that: - The concept of self-organizing systems proves especially fruitful for small-scale earth surface systems. Beach cusps and patterned ground are only two of several other prime examples of self-organizing earth surface systems. They display characteristics of self-organization like (i) system-wide order from local interactions, (ii) symmetry breaking, (iii) distributed control, (iv) robustness and resilience, (v) nonlinearity and feedbacks, (vi) organizational closure, (vii) adaptation, and (viii) variation and selection. - It is comparatively easy to discern self-organization in small-scale systems, but to adapt the concept to larger scale systems relevant to global environmental change research is more difficult: Self-organizing

  11. Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory.

    Science.gov (United States)

    Chen, Qiuwen; Rui, Han; Li, Weifeng; Zhang, Yanhui

    2014-06-01

    Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004-2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial-temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial-temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. An Algorithm Creating Thumbnail for Web Map Services Based on Information Entropy and Trans-scale Similarity

    Directory of Open Access Journals (Sweden)

    CHENG Xiaoqiang

    2017-11-01

    Full Text Available Thumbnail can greatly increase the efficiency of browsing pictures,videos and other image resources and improve the user experience prominently. Map service is a kind of graphic resource coupling spatial information and representation scale,its crafting,retrieval and management will not function well without the support of thumbnail. Sophisticated designed thumbnails bring users vivid first impressions and help users make efficient exploration. On the contrast,coarse thumbnail cause negative emotion and discourage users to explore the map service positively. Inspired by video summarization,key position and key scale of web map service were proposed. Meanwhile,corresponding quantitative measures and an automatic algorithm were drawn up and implemented. With the help of this algorithm,poor visual quality,lack of map information and low automation of current thumbnails was solved successfully. Information entropy was used to determine areas richer in content and tran-scale similarity was calculated to judge at which scale the appearance of the map service has changed drastically,and finally a series of static pictures were extracted which can represent the content of the map service. Experimental results show that this method produced medium-sized,content-rich and well-representative thumbnails which effectively reflect the content and appearance of map service.

  13. Color reproduction and processing algorithm based on real-time mapping for endoscopic images.

    Science.gov (United States)

    Khan, Tareq H; Mohammed, Shahed K; Imtiaz, Mohammad S; Wahid, Khan A

    2016-01-01

    In this paper, we present a real-time preprocessing algorithm for image enhancement for endoscopic images. A novel dictionary based color mapping algorithm is used for reproducing the color information from a theme image. The theme image is selected from a nearby anatomical location. A database of color endoscopy image for different location is prepared for this purpose. The color map is dynamic as its contents change with the change of the theme image. This method is used on low contrast grayscale white light images and raw narrow band images to highlight the vascular and mucosa structures and to colorize the images. It can also be applied to enhance the tone of color images. The statistic visual representation and universal image quality measures show that the proposed method can highlight the mucosa structure compared to other methods. The color similarity has been verified using Delta E color difference, structure similarity index, mean structure similarity index and structure and hue similarity. The color enhancement was measured using color enhancement factor that shows considerable improvements. The proposed algorithm has low and linear time complexity, which results in higher execution speed than other related works.

  14. A comparison of three self-tuning control algorithms developed for the Bristol-Babcock controller

    International Nuclear Information System (INIS)

    Tapp, P.A.

    1992-04-01

    A brief overview of adaptive control methods relating to the design of self-tuning proportional-integral-derivative (PID) controllers is given. The methods discussed include gain scheduling, self-tuning, auto-tuning, and model-reference adaptive control systems. Several process identification and parameter adjustment methods are discussed. Characteristics of the two most common types of self-tuning controllers implemented by industry (i.e., pattern recognition and process identification) are summarized. The substance of the work is a comparison of three self-tuning proportional-plus-integral (STPI) control algorithms developed to work in conjunction with the Bristol-Babcock PID control module. The STPI control algorithms are based on closed-loop cycling theory, pattern recognition theory, and model-based theory. A brief theory of operation of these three STPI control algorithms is given. Details of the process simulations developed to test the STPI algorithms are given, including an integrating process, a first-order system, a second-order system, a system with initial inverse response, and a system with variable time constant and delay. The STPI algorithms' performance with regard to both setpoint changes and load disturbances is evaluated, and their robustness is compared. The dynamic effects of process deadtime and noise are also considered. Finally, the limitations of each of the STPI algorithms is discussed, some conclusions are drawn from the performance comparisons, and a few recommendations are made. 6 refs

  15. Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography; Selbstorganisierende neuronale Netze zur automatischen Detektion und Klassifikation von Kontrast(mittel)-verstaerkten Laesionen in der dynamischen MR-Mammographie

    Energy Technology Data Exchange (ETDEWEB)

    Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M. [Klinik und Poliklinik fuer Radiologie, Klinikum der Univ. Mainz (Germany)

    2005-05-01

    Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

  16. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.)

    Energy Technology Data Exchange (ETDEWEB)

    Samecka-Cymerman, A., E-mail: sameckaa@biol.uni.wroc.p [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Stankiewicz, A.; Kolon, K. [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Kempers, A.J. [Department of Environmental Sciences, Radboud University of Nijmegen, Toernooiveld, 6525 ED Nijmegen (Netherlands)

    2009-07-15

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Olesnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wroclaw to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. - Once trained, SOFM could be used in the future to recognize types of pollution.

  17. Mapping Soil Organic Matter with Hyperspectral Imaging

    Science.gov (United States)

    Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

    2014-05-01

    Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

  18. Systematic approach for deriving feasible mappings of parallel algorithms to parallel computing platforms

    NARCIS (Netherlands)

    Arkin, Ethem; Tekinerdogan, Bedir; Imre, Kayhan M.

    2017-01-01

    The need for high-performance computing together with the increasing trend from single processor to parallel computer architectures has leveraged the adoption of parallel computing. To benefit from parallel computing power, usually parallel algorithms are defined that can be mapped and executed

  19. Self-organization in irregular landscapes: Detecting autogenic interactions from field data using descriptive statistics and dynamical systems theory

    Science.gov (United States)

    Larsen, L.; Watts, D.; Khurana, A.; Anderson, J. L.; Xu, C.; Merritts, D. J.

    2015-12-01

    The classic signal of self-organization in nature is pattern formation. However, the interactions and feedbacks that organize depositional landscapes do not always result in regular or fractal patterns. How might we detect their existence and effects in these "irregular" landscapes? Emergent landscapes such as newly forming deltaic marshes or some restoration sites provide opportunities to study the autogenic processes that organize landscapes and their physical signatures. Here we describe a quest to understand autogenic vs. allogenic controls on landscape evolution in Big Spring Run, PA, a landscape undergoing restoration from bare-soil conditions to a target wet meadow landscape. The contemporary motivation for asking questions about autogenic vs. allogenic controls is to evaluate how important initial conditions or environmental controls may be for the attainment of management objectives. However, these questions can also inform interpretation of the sedimentary record by enabling researchers to separate signals that may have arisen through self-organization processes from those resulting from environmental perturbations. Over three years at Big Spring Run, we mapped the dynamic evolution of floodplain vegetation communities and distributions of abiotic variables and topography. We used principal component analysis and transition probability analysis to detect associative interactions between vegetation and geomorphic variables and convergent cross-mapping on lidar data to detect causal interactions between biomass and topography. Exploratory statistics revealed that plant communities with distinct morphologies exerted control on landscape evolution through stress divergence (i.e., channel initiation) and promoting the accumulation of fine sediment in channels. Together, these communities participated in a negative feedback that maintains low energy and multiple channels. Because of the spatially explicit nature of this feedback, causal interactions could not

  20. Navigation and Self-Semantic Location of Drones in Indoor Environments by Combining the Visual Bug Algorithm and Entropy-Based Vision.

    Science.gov (United States)

    Maravall, Darío; de Lope, Javier; Fuentes, Juan P

    2017-01-01

    We introduce a hybrid algorithm for the self-semantic location and autonomous navigation of robots using entropy-based vision and visual topological maps. In visual topological maps the visual landmarks are considered as leave points for guiding the robot to reach a target point (robot homing) in indoor environments. These visual landmarks are defined from images of relevant objects or characteristic scenes in the environment. The entropy of an image is directly related to the presence of a unique object or the presence of several different objects inside it: the lower the entropy the higher the probability of containing a single object inside it and, conversely, the higher the entropy the higher the probability of containing several objects inside it. Consequently, we propose the use of the entropy of images captured by the robot not only for the landmark searching and detection but also for obstacle avoidance. If the detected object corresponds to a landmark, the robot uses the suggestions stored in the visual topological map to reach the next landmark or to finish the mission. Otherwise, the robot considers the object as an obstacle and starts a collision avoidance maneuver. In order to validate the proposal we have defined an experimental framework in which the visual bug algorithm is used by an Unmanned Aerial Vehicle (UAV) in typical indoor navigation tasks.

  1. Navigation and Self-Semantic Location of Drones in Indoor Environments by Combining the Visual Bug Algorithm and Entropy-Based Vision

    Directory of Open Access Journals (Sweden)

    Darío Maravall

    2017-08-01

    Full Text Available We introduce a hybrid algorithm for the self-semantic location and autonomous navigation of robots using entropy-based vision and visual topological maps. In visual topological maps the visual landmarks are considered as leave points for guiding the robot to reach a target point (robot homing in indoor environments. These visual landmarks are defined from images of relevant objects or characteristic scenes in the environment. The entropy of an image is directly related to the presence of a unique object or the presence of several different objects inside it: the lower the entropy the higher the probability of containing a single object inside it and, conversely, the higher the entropy the higher the probability of containing several objects inside it. Consequently, we propose the use of the entropy of images captured by the robot not only for the landmark searching and detection but also for obstacle avoidance. If the detected object corresponds to a landmark, the robot uses the suggestions stored in the visual topological map to reach the next landmark or to finish the mission. Otherwise, the robot considers the object as an obstacle and starts a collision avoidance maneuver. In order to validate the proposal we have defined an experimental framework in which the visual bug algorithm is used by an Unmanned Aerial Vehicle (UAV in typical indoor navigation tasks.

  2. Supramolecular chemistry: from molecular information towards self-organization and complex matter

    International Nuclear Information System (INIS)

    Lehn, Jean-Marie

    2004-01-01

    Molecular chemistry has developed a wide range of very powerful procedures for constructing ever more sophisticated molecules from atoms linked by covalent bonds. Beyond molecular chemistry lies supramolecular chemistry, which aims at developing highly complex chemical systems from components interacting via non-covalent intermolecular forces. By the appropriate manipulation of these interactions, supramolecular chemistry became progressively the chemistry of molecular information, involving the storage of information at the molecular level, in the structural features, and its retrieval, transfer, and processing at the supramolecular level, through molecular recognition processes operating via specific interactional algorithms. This has paved the way towards apprehending chemistry also as an information science. Numerous receptors capable of recognizing, i.e. selectively binding, specific substrates have been developed, based on the molecular information stored in the interacting species. Suitably functionalized receptors may perform supramolecular catalysis and selective transport processes. In combination with polymolecular organization, recognition opens ways towards the design of molecular and supramolecular devices based on functional (photoactive, electroactive, ionoactive, etc) components. A step beyond preorganization consists in the design of systems undergoing self-organization, i.e. systems capable of spontaneously generating well-defined supramolecular architectures by self-assembly from their components. Self-organization processes, directed by the molecular information stored in the components and read out at the supramolecular level through specific interactions, represent the operation of programmed chemical systems. They have been implemented for the generation of a variety of discrete functional architectures of either organic or inorganic nature. Self-organization processes also give access to advanced supramolecular materials, such as

  3. Fast mapping algorithm of lighting spectrum and GPS coordinates for a large area

    Science.gov (United States)

    Lin, Chih-Wei; Hsu, Ke-Fang; Hwang, Jung-Min

    2016-09-01

    In this study, we propose a fast rebuild technology for evaluating light quality in large areas. Outdoor light quality, which is measured by illuminance uniformity and the color rendering index, is difficult to conform after improvement. We develop an algorithm for a lighting quality mapping system and coordinates using a micro spectrometer and GPS tracker integrated with a quadcopter or unmanned aerial vehicle. After cruising at a constant altitude, lighting quality data is transmitted and immediately mapped to evaluate the light quality in a large area.

  4. Tracking senescence-induced patterns in leaf litter leachate using parallel factor analysis (PARAFAC) modeling and self-organizing maps

    Science.gov (United States)

    Wheeler, K. I.; Levia, D. F.; Hudson, J. E.

    2017-09-01

    In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.

  5. The Relationship between Self-Assembly and Conformal Mappings

    Science.gov (United States)

    Duque, Carlos; Santangelo, Christian

    The isotropic growth of a thin sheet has been used as a way to generate programmed shapes through controlled buckling. We discuss how conformal mappings, which are transformations that locally preserve angles, provide a way to quantify the area growth needed to produce a particular shape. A discrete version of the conformal map can be constructed from circle packings, which are maps between packings of circles whose contact network is preserved. This provides a link to the self-assembly of particles on curved surfaces. We performed simulations of attractive particles on a curved surface using molecular dynamics. The resulting particle configurations were used to generate the corresponding discrete conformal map, allowing us to quantify the degree of area distortion required to produce a particular shape by finding particle configurations that minimize the area distortion.

  6. An Isometric Mapping Based Co-Location Decision Tree Algorithm

    Science.gov (United States)

    Zhou, G.; Wei, J.; Zhou, X.; Zhang, R.; Huang, W.; Sha, H.; Chen, J.

    2018-05-01

    Decision tree (DT) induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information) as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT) method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT), which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1) The extraction method of exposed carbonate rocks is of high accuracy. (2) The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

  7. AN ISOMETRIC MAPPING BASED CO-LOCATION DECISION TREE ALGORITHM

    Directory of Open Access Journals (Sweden)

    G. Zhou

    2018-05-01

    Full Text Available Decision tree (DT induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT, which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1 The extraction method of exposed carbonate rocks is of high accuracy. (2 The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

  8. Performance of 3DOSEM and MAP algorithms for reconstructing low count SPECT acquisitions

    Energy Technology Data Exchange (ETDEWEB)

    Grootjans, Willem [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Leiden Univ. Medical Center (Netherlands). Dept. of Radiology; Meeuwis, Antoi P.W.; Gotthardt, Martin; Visser, Eric P. [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Slump, Cornelis H. [Univ. Twente, Enschede (Netherlands). MIRA Inst. for Biomedical Technology and Technical Medicine; Geus-Oei, Lioe-Fee de [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Univ. Twente, Enschede (Netherlands). MIRA Inst. for Biomedical Technology and Technical Medicine; Leiden Univ. Medical Center (Netherlands). Dept. of Radiology

    2016-07-01

    Low count single photon emission computed tomography (SPECT) is becoming more important in view of whole body SPECT and reduction of radiation dose. In this study, we investigated the performance of several 3D ordered subset expectation maximization (3DOSEM) and maximum a posteriori (MAP) algorithms for reconstructing low count SPECT images. Phantom experiments were conducted using the National Electrical Manufacturers Association (NEMA) NU2 image quality (IQ) phantom. The background compartment of the phantom was filled with varying concentrations of pertechnetate and indiumchloride, simulating various clinical imaging conditions. Images were acquired using a hybrid SPECT/CT scanner and reconstructed with 3DOSEM and MAP reconstruction algorithms implemented in Siemens Syngo MI.SPECT (Flash3D) and Hermes Hybrid Recon Oncology (Hyrid Recon 3DOSEM and MAP). Image analysis was performed by calculating the contrast recovery coefficient (CRC),percentage background variability (N%), and contrast-to-noise ratio (CNR), defined as the ratio between CRC and N%. Furthermore, image distortion is characterized by calculating the aspect ratio (AR) of ellipses fitted to the hot spheres. Additionally, the performance of these algorithms to reconstruct clinical images was investigated. Images reconstructed with 3DOSEM algorithms demonstrated superior image quality in terms of contrast and resolution recovery when compared to images reconstructed with filtered-back-projection (FBP), OSEM and 2DOSEM. However, occurrence of correlated noise patterns and image distortions significantly deteriorated the quality of 3DOSEM reconstructed images. The mean AR for the 37, 28, 22, and 17 mm spheres was 1.3, 1.3, 1.6, and 1.7 respectively. The mean N% increase in high and low count Flash3D and Hybrid Recon 3DOSEM from 5.9% and 4.0% to 11.1% and 9.0%, respectively. Similarly, the mean CNR decreased in high and low count Flash3D and Hybrid Recon 3DOSEM from 8.7 and 8.8 to 3.6 and 4

  9. MODIS 250m burned area mapping based on an algorithm using change point detection and Markov random fields.

    Science.gov (United States)

    Mota, Bernardo; Pereira, Jose; Campagnolo, Manuel; Killick, Rebeca

    2013-04-01

    Area burned in tropical savannas of Brazil was mapped using MODIS-AQUA daily 250m resolution imagery by adapting one of the European Space Agency fire_CCI project burned area algorithms, based on change point detection and Markov random fields. The study area covers 1,44 Mkm2 and was performed with data from 2005. The daily 1000 m image quality layer was used for cloud and cloud shadow screening. The algorithm addresses each pixel as a time series and detects changes in the statistical properties of NIR reflectance values, to identify potential burning dates. The first step of the algorithm is robust filtering, to exclude outlier observations, followed by application of the Pruned Exact Linear Time (PELT) change point detection technique. Near-infrared (NIR) spectral reflectance changes between time segments, and post change NIR reflectance values are combined into a fire likelihood score. Change points corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurring outside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps and detection date maps are converted to dichotomous (burned-unburned maps) using Markov random fields, which take into account both spatial and temporal relations in the potential burned area maps. A preliminary assessment of our results is performed by comparison with data from the MODIS 1km active fires and the 500m burned area products, taking into account differences in spatial resolution between the two sensors.

  10. Complexity of a kind of interval continuous self-map of finite type

    International Nuclear Information System (INIS)

    Wang Lidong; Chu Zhenyan; Liao Gongfu

    2011-01-01

    Highlights: → We find the Hausdorff dimension for an interval continuous self-map f of finite type is s element of (0,1) on a non-wandering set. → f| Ω(f) has positive topological entropy. → f| Ω(f) is chaotic such as Devaney chaos, Kato chaos, two point distributional chaos and so on. - Abstract: An interval map is called finitely typal, if the restriction of the map to non-wandering set is topologically conjugate with a subshift of finite type. In this paper, we prove that there exists an interval continuous self-map of finite type such that the Hausdorff dimension is an arbitrary number in the interval (0, 1), discuss various chaotic properties of the map and the relations between chaotic set and the set of recurrent points.

  11. Complexity of a kind of interval continuous self-map of finite type

    Energy Technology Data Exchange (ETDEWEB)

    Wang Lidong, E-mail: wld@dlnu.edu.cn [Institute of Mathematics, Dalian Nationalities University, Dalian 116600 (China); Institute of Mathematics, Jilin Normal University, Siping 136000 (China); Chu Zhenyan, E-mail: chuzhenyan8@163.com [Institute of Mathematics, Dalian Nationalities University, Dalian 116600 (China) and Institute of Mathematics, Jilin University, Changchun 130023 (China); Liao Gongfu, E-mail: liaogf@email.jlu.edu.cn [Institute of Mathematics, Jilin University, Changchun 130023 (China)

    2011-10-15

    Highlights: > We find the Hausdorff dimension for an interval continuous self-map f of finite type is s element of (0,1) on a non-wandering set. > f|{sub {Omega}(f)} has positive topological entropy. > f|{sub {Omega}(f)} is chaotic such as Devaney chaos, Kato chaos, two point distributional chaos and so on. - Abstract: An interval map is called finitely typal, if the restriction of the map to non-wandering set is topologically conjugate with a subshift of finite type. In this paper, we prove that there exists an interval continuous self-map of finite type such that the Hausdorff dimension is an arbitrary number in the interval (0, 1), discuss various chaotic properties of the map and the relations between chaotic set and the set of recurrent points.

  12. Radionuclide identification algorithm for organic scintillator-based radiation portal monitor

    Energy Technology Data Exchange (ETDEWEB)

    Paff, Marc Gerrit, E-mail: mpaff@umich.edu; Di Fulvio, Angela; Clarke, Shaun D.; Pozzi, Sara A.

    2017-03-21

    We have developed an algorithm for on-the-fly radionuclide identification for radiation portal monitors using organic scintillation detectors. The algorithm was demonstrated on experimental data acquired with our pedestrian portal monitor on moving special nuclear material and industrial sources at a purpose-built radiation portal monitor testing facility. The experimental data also included common medical isotopes. The algorithm takes the power spectral density of the cumulative distribution function of the measured pulse height distributions and matches these to reference spectra using a spectral angle mapper. F-score analysis showed that the new algorithm exhibited significant performance improvements over previously implemented radionuclide identification algorithms for organic scintillators. Reliable on-the-fly radionuclide identification would help portal monitor operators more effectively screen out the hundreds of thousands of nuisance alarms they encounter annually due to recent nuclear-medicine patients and cargo containing naturally occurring radioactive material. Portal monitor operators could instead focus on the rare but potentially high impact incidents of nuclear and radiological material smuggling detection for which portal monitors are intended.

  13. Radionuclide identification algorithm for organic scintillator-based radiation portal monitor

    Science.gov (United States)

    Paff, Marc Gerrit; Di Fulvio, Angela; Clarke, Shaun D.; Pozzi, Sara A.

    2017-03-01

    We have developed an algorithm for on-the-fly radionuclide identification for radiation portal monitors using organic scintillation detectors. The algorithm was demonstrated on experimental data acquired with our pedestrian portal monitor on moving special nuclear material and industrial sources at a purpose-built radiation portal monitor testing facility. The experimental data also included common medical isotopes. The algorithm takes the power spectral density of the cumulative distribution function of the measured pulse height distributions and matches these to reference spectra using a spectral angle mapper. F-score analysis showed that the new algorithm exhibited significant performance improvements over previously implemented radionuclide identification algorithms for organic scintillators. Reliable on-the-fly radionuclide identification would help portal monitor operators more effectively screen out the hundreds of thousands of nuisance alarms they encounter annually due to recent nuclear-medicine patients and cargo containing naturally occurring radioactive material. Portal monitor operators could instead focus on the rare but potentially high impact incidents of nuclear and radiological material smuggling detection for which portal monitors are intended.

  14. Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps.

    Science.gov (United States)

    Kriegel, Fabian L; Köhler, Ralf; Bayat-Sarmadi, Jannike; Bayerl, Simon; Hauser, Anja E; Niesner, Raluca; Luch, Andreas; Cseresnyes, Zoltan

    2018-03-01

    Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short- and long-term changes in their surroundings under physiological and pathological conditions. Intravital multi-photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track- and shape-describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D-to-2D projections of surface-rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self-Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not

  15. A novel method to design S-box based on chaotic map and genetic algorithm

    International Nuclear Information System (INIS)

    Wang, Yong; Wong, Kwok-Wo; Li, Changbing; Li, Yang

    2012-01-01

    The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

  16. A novel method to design S-box based on chaotic map and genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yong, E-mail: wangyong_cqupt@163.com [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China); Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Wong, Kwok-Wo [Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong (Hong Kong); Li, Changbing [Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Li, Yang [Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, S1 3DJ (United Kingdom)

    2012-01-30

    The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

  17. A novel symbiotic organisms search algorithm for congestion management in deregulated environment

    Science.gov (United States)

    Verma, Sumit; Saha, Subhodip; Mukherjee, V.

    2017-01-01

    In today's competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

  18. A buffer material optimal design in the radioactive wastes geological disposal using the satisficing trade-off method and the self-organizing map

    International Nuclear Information System (INIS)

    Okamoto, Takashi; Hanaoka, Yuya; Aiyoshi, Eitaro; Kobayashi, Yoko

    2012-01-01

    In this paper, we consider a multi-objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high-level radioactive wastes geological disposal. The buffer material optimal design problem is formulated as a constrained multi-objective optimization problem. Its Pareto optimal solutions are distributed evenly on whole bounds of the feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions which are distributed evenly and vastly. In the preferred solution search method, the visualization technique of a Pareto optimal solution set using the self-organizing map is introduced into the satisficing trade-off method which is the interactive method to obtain a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem. (author)

  19. Differentiating the Spatiotemporal Distribution of Natural and Anthropogenic Processes on River Water-Quality Variation Using a Self-Organizing Map With Factor Analysis.

    Science.gov (United States)

    Wang, Yeuh-Bin; Liu, Chen-Wuing; Lee, Jin-Jing

    2015-08-01

    To elucidate the historical improvement and advanced measure of river water quality in the Taipei metropolitan area, this study applied the self-organizing map (SOM) technique with factor analysis (FA) to differentiate the spatiotemporal distribution of natural and anthropogenic processes on river water-quality variation spanning two decades. The SOM clustered river water quality into five groups: very low pollution, low pollution, moderate pollution, high pollution, and very high pollution. FA was then used to extract four latent factors that dominated water quality from 1991 to 2011 including three anthropogenic process factors (organic, industrial, and copper pollution) and one natural process factor [suspended solids (SS) pollution]. The SOM revealed that the water quality improved substantially over time. However, the downstream river water quality was still classified as high pollution because of an increase in anthropogenic activity. FA showed the spatiotemporal pattern of each factor score decreasing over time, but the organic pollution factor downstream of the Tamsui River, as well as the SS factor scores in the upstream major tributary (the Dahan Stream), remained within the high pollution level. Therefore, we suggest that public sewage-treatment plants should be upgraded from their current secondary biological processing to advanced treatment processing. The conservation of water and soil must also be reinforced to decrease the SS loading of the Dahan Stream from natural erosion processes in the future.

  20. Guided self-organization inception

    CERN Document Server

    2014-01-01

    Is it possible to guide the process of self-organisation towards specific patterns and outcomes?  Wouldn’t this be self-contradictory?   After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control.  Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?  This book presents different approaches to resolving this paradox.  In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms.  A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field. Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided S...

  1. The genotype-phenotype map of an evolving digital organism

    OpenAIRE

    Fortuna, Miguel A.; Zaman, Luis; Ofria, Charles; Wagner, Andreas

    2017-01-01

    To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms fr...

  2. Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping

    Directory of Open Access Journals (Sweden)

    Antero Kukko

    2008-09-01

    Full Text Available Automated processing of the data provided by a laser-based mobile mapping system will be a necessity due to the huge amount of data produced. In the future, vehiclebased laser scanning, here called mobile mapping, should see considerable use for road environment modelling. Since the geometry of the scanning and point density is different from airborne laser scanning, new algorithms are needed for information extraction. In this paper, we propose automatic methods for classifying the road marking and kerbstone points and modelling the road surface as a triangulated irregular network. On the basis of experimental tests, the mean classification accuracies obtained using automatic method for lines, zebra crossings and kerbstones were 80.6%, 92.3% and 79.7%, respectively.

  3. Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.

    Science.gov (United States)

    Chattopadhyay, Ishanu; Ray, Asok

    2009-12-01

    This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

  4. A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia

    Science.gov (United States)

    Xiangming Xiao; Chandrashekhar M. Biradar; Christina Czarnecki; Tunrayo Alabi; Michael Keller

    2009-01-01

    The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile...

  5. A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

    Directory of Open Access Journals (Sweden)

    Dawen Xia

    2018-01-01

    Full Text Available Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR, CombineFileInputFormat (CFIF, and Sequence Files (SF, to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP algorithm in efficiency and scalability.

  6. An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.

    Directory of Open Access Journals (Sweden)

    Jinjun Tang

    Full Text Available An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

  7. Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

    Science.gov (United States)

    Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

    2017-07-01

    Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed

  8. Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

    Science.gov (United States)

    Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

    2016-08-03

    Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

  9. `VIS/NIR mapping of TOC and extent of organic soils in the Nørre Å valley

    Science.gov (United States)

    Knadel, M.; Greve, M. H.; Thomsen, A.

    2009-04-01

    Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The system also acquired soil electrical conductivity (EC) for two soil depths: shallow EC-SH (0- 31 cm) and deep conductivity EC-DP (0- 91 cm). The conductivity was recorded together with GPS coordinates and spectral data for further construction of the calibration models. Two maps of organic soils in the Nørre Å valley (Central Jutland) were generated: (i) based on a conventional 25 m grid with 162 sampling points and laboratory analysis of TOC, (ii) based on in-situ VIS/NIR measurements supported by chemometrics. Before regression analysis, spectral information was compressed by calculating principal components. The outliers were determined by a mahalanobis distance equation and removed. Clustering using a fuzzy c- means algorithm was conducted. Within each cluster a location with the minimal spatial variability was selected. A map of 15 representative sample locations was proposed. The interpolation of the spectra into a single spectrum was performed using a Gaussian kernel weighting

  10. Time synchronization algorithm of distributed system based on server time-revise and workstation self-adjust

    International Nuclear Information System (INIS)

    Zhou Shumin; Sun Yamin; Tang Bin

    2007-01-01

    In order to enhance the time synchronization quality of the distributed system, a time synchronization algorithm of distributed system based on server time-revise and workstation self-adjust is proposed. The time-revise cycle and self-adjust process is introduced in the paper. The algorithm reduces network flow effectively and enhances the quality of clock-synchronization. (authors)

  11. A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor

    Directory of Open Access Journals (Sweden)

    Liang Zhang

    2015-08-01

    Full Text Available Internet of Things (IoT is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN k-Nearest Neighbor (KNN algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

  12. A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor.

    Science.gov (United States)

    Zhang, Liang; Shen, Peiyi; Zhu, Guangming; Wei, Wei; Song, Houbing

    2015-08-14

    Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University's datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

  13. Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing

    Science.gov (United States)

    Liao, Zhiheng; Sun, Jiaren; Yao, Jialin; Liu, Li; Li, Haowen; Liu, Jian; Xie, Jielan; Wu, Dui; Fan, Shaojia

    2018-05-01

    Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013-2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM2.5, NO2 and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3 % (44.4 µg m-3) in January 2013, 46.4 % (22.2 µg m-3) in December 2015 and 73.3 % (34.6 µg m-3) in December 2016.

  14. ‘’Mapping Self- and Co-regulation Approaches in the EU Context’’ : Explorative Study for the European Commission, DG Connect

    NARCIS (Netherlands)

    Senden, L.A.J.; Kica, E.; Klinger, Kilian; Hiemstra, M.I.

    2015-01-01

    This report presents a first mapping or inventory of the different approaches to self- and co- regulation (SCR) that can be found within the EU context, in a number of Member States (MS) and international organizations. The report consists of four sections. Whereas the first section provides an

  15. Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM: A Case Study in Beijing, China

    Directory of Open Access Journals (Sweden)

    Binwu Wang

    2014-03-01

    Full Text Available The regional management of trace elements in soils requires understanding the interaction between the natural system and human socio-economic activities. In this study, a social-ecological patterns of heavy metals (SEPHM approach was proposed to identify the heavy metal concentration patterns and processes in different ecoregions of Beijing (China based on a self-organizing map (SOM. Potential ecological risk index (RI values of Cr, Ni, Zn, Hg, Cu, As, Cd and Pb were calculated for 1,018 surface soil samples. These data were averaged in accordance with 253 communities and/or towns, and compared with demographic, agriculture structure, geomorphology, climate, land use/cover, and soil-forming parent material to discover the SEPHM. Multivariate statistical techniques were further applied to interpret the control factors of each SEPHM. SOM application clustered the 253 towns into nine groups on the map size of 12 × 7 plane (quantization error 1.809; topographic error, 0.0079. The distribution characteristics and Spearman rank correlation coefficients of RIs were strongly associated with the population density, vegetation index, industrial and mining land percent and road density. The RIs were relatively high in which towns in a highly urbanized area with large human population density exist, while low RIs occurred in mountainous and high vegetation cover areas. The resulting dataset identifies the SEPHM of Beijing and links the apparent results of RIs to driving factors, thus serving as an excellent data source to inform policy makers for legislative and land management actions.

  16. Algorithms for necklace maps

    NARCIS (Netherlands)

    Speckmann, B.; Verbeek, K.A.B.

    2015-01-01

    Necklace maps visualize quantitative data associated with regions by placing scaled symbols, usually disks, without overlap on a closed curve (the necklace) surrounding the map regions. Each region is projected onto an interval on the necklace that contains its symbol. In this paper we address the

  17. The Effectiveness of Web-Based Asthma Self-Management System, My Asthma Portal (MAP): A Pilot Randomized Controlled Trial.

    Science.gov (United States)

    Ahmed, Sara; Ernst, Pierre; Bartlett, Susan J; Valois, Marie-France; Zaihra, Tasneem; Paré, Guy; Grad, Roland; Eilayyan, Owis; Perreault, Robert; Tamblyn, Robyn

    2016-12-01

    Whether Web-based technologies can improve disease self-management is uncertain. My Asthma Portal (MAP) is a Web-based self-management support system that couples evidence-based behavioral change components (self-monitoring of symptoms, physical activity, and medication adherence) with real-time monitoring, feedback, and support from a nurse case manager. The aim of this study was to compare the impact of access to a Web-based asthma self-management patient portal linked to a case-management system (MAP) over 6 months compared with usual care on asthma control and quality of life. A multicenter, parallel, 2-arm, pilot, randomized controlled trial was conducted with 100 adults with confirmed diagnosis of asthma from 2 specialty clinics. Asthma control was measured using an algorithm based on overuse of fast-acting bronchodilators and emergency department visits, and asthma-related quality of life was assessed using the Mini-Asthma Quality of Life Questionnaire (MAQLQ). Secondary mediating outcomes included asthma symptoms, depressive symptoms, self-efficacy, and beliefs about medication. Process evaluations were also included. A total of 49 individuals were randomized to MAP and 51 to usual care. Compared with usual care, participants in the intervention group reported significantly higher asthma quality of life (mean change 0.61, 95% CI 0.03 to 1.19), and the change in asthma quality of life for the intervention group between baseline and 3 months (mean change 0.66, 95% CI 0.35 to 0.98) was not seen in the control group. No significant differences in asthma quality of life were found between the intervention and control groups at 6 (mean change 0.46, 95% CI -0.12 to 1.05) and 9 months (mean change 0.39, 95% CI -0.2 to 0.98). For poor control status, there was no significant effect of group, time, or group by time. For all self-reported measures, the intervention group had a significantly higher proportion of individuals, demonstrating a minimal clinically

  18. Portraits of self-organization in fish schools interacting with robots

    Science.gov (United States)

    Aureli, M.; Fiorilli, F.; Porfiri, M.

    2012-05-01

    In this paper, we propose an enabling computational and theoretical framework for the analysis of experimental instances of collective behavior in response to external stimuli. In particular, this work addresses the characterization of aggregation and interaction phenomena in robot-animal groups through the exemplary analysis of fish schooling in the vicinity of a biomimetic robot. We adapt global observables from statistical mechanics to capture the main features of the shoal collective motion and its response to the robot from experimental observations. We investigate the shoal behavior by using a diffusion mapping analysis performed on these global observables that also informs the definition of relevant portraits of self-organization.

  19. A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders

    2015-01-01

    We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that 1) the proposed algorithm...

  20. Temporal high-pass non-uniformity correction algorithm based on grayscale mapping and hardware implementation

    Science.gov (United States)

    Jin, Minglei; Jin, Weiqi; Li, Yiyang; Li, Shuo

    2015-08-01

    In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms' poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.

  1. An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

    Directory of Open Access Journals (Sweden)

    Dawen Xia

    2015-01-01

    Full Text Available Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs. Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K-Means (Par3PKM algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K-Means and then employ a MapReduce paradigm to redesign the optimized K-Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K-Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.

  2. MAP SERVICES FOR MANAGEMENT OF HUNTING ORGANIZATIONS (THE CASE OF HUNTING ORGANIZATION “MEDVEDICA”

    Directory of Open Access Journals (Sweden)

    S. A. Zaichenko

    2013-01-01

    Full Text Available The current state map support of the system of hunting management requires updating an information database and the creation of new schemes of hunting organization. In this case the beneficial is using of satellite imagery data for the mapping and also for important environmental research. Presentation of the results in the form of Internet web services provides broad benefits to the paper version of the maps.

  3. Design and implementation of three-dimension texture mapping algorithm for panoramic system based on smart platform

    Science.gov (United States)

    Liu, Zhi; Zhou, Baotong; Zhang, Changnian

    2017-03-01

    Vehicle-mounted panoramic system is important safety assistant equipment for driving. However, traditional systems only render fixed top-down perspective view of limited view field, which may have potential safety hazard. In this paper, a texture mapping algorithm for 3D vehicle-mounted panoramic system is introduced, and an implementation of the algorithm utilizing OpenGL ES library based on Android smart platform is presented. Initial experiment results show that the proposed algorithm can render a good 3D panorama, and has the ability to change view point freely.

  4. Interfacial self-organization of bolaamphiphiles bearing mesogenic groups: relationships between the molecular structures and their self-organized morphologies.

    Science.gov (United States)

    Song, Bo; Liu, Guanqing; Xu, Rui; Yin, Shouchun; Wang, Zhiqiang; Zhang, Xi

    2008-04-15

    This article discusses the relationship between the molecular structure of bolaamphiphiles bearing mesogenic groups and their interfacial self-organized morphology. On the basis of the molecular structures of bolaamphiphiles, we designed and synthesized a series of molecules with different hydrophobic alkyl chain lengths, hydrophilic headgroups, mesogenic groups, and connectors between the alkyl chains and the mesogenic group. Through investigating their interfacial self-organization behavior, some experiential rules are summarized: (1) An appropriate alkyl chain length is necessary to form stable surface micelles; (2) different categories of headgroups have a great effect on the interfacial self-organized morphology; (3) different types of mesogenic groups have little effect on the structure of the interfacial assembly when it is changed from biphenyl to azobenzene or stilbene; (4) the orientation of the ester linker between the mesogenic group and alkyl chain can greatly influence the interfacial self-organization behavior. It is anticipated that this line of research may be helpful for the molecular engineering of bolaamphiphiles to form tailor-made morphologies.

  5. Using Hadoop MapReduce for Parallel Genetic Algorithms: A Comparison of the Global, Grid and Island Models.

    Science.gov (United States)

    Ferrucci, Filomena; Salza, Pasquale; Sarro, Federica

    2017-06-29

    The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store

  6. Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm.

    Science.gov (United States)

    Yang, Dan; Xu, Bin; Rao, Kaiyou; Sheng, Weihua

    2018-01-24

    Indoor occupants' positions are significant for smart home service systems, which usually consist of robot service(s), appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans' position in indoor environments based on passive infrared (PIR) sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization.

  7. Passive Infrared (PIR-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

    Directory of Open Access Journals (Sweden)

    Dan Yang

    2018-01-01

    Full Text Available Indoor occupants’ positions are significant for smart home service systems, which usually consist of robot service(s, appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans’ position in indoor environments based on passive infrared (PIR sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization.

  8. Segmentation and profiling consumers in a multi-channel environment using a combination of self-organizing maps (SOM method, and logistic regression

    Directory of Open Access Journals (Sweden)

    Seyed Ali Akbar Afjeh

    2014-05-01

    Full Text Available Market segmentation plays essential role on understanding the behavior of people’s interests in purchasing various products and services through various channels. This paper presents an empirical investigation to shed light on consumer’s purchasing attitude as well as gathering information in multi-channel environment. The proposed study of this paper designed a questionnaire and distributed it among 800 people who were at least 18 years of age and had some experiences on purchasing goods and services on internet, catalog or regular shopping centers. Self-organizing map, SOM, clustering technique was performed based on consumer’s interest in gathering information as well as purchasing products through internet, catalog and shopping centers and determined four segments. There were two types of questions for the proposed study of this paper. The first group considered participants’ personal characteristics such as age, gender, income, etc. The second group of questions was associated with participants’ psychographic characteristics including price consciousness, quality consciousness, time pressure, etc. Using multinominal logistic regression technique, the study determines consumers’ behaviors in each four segments.

  9. Self organized criticality

    International Nuclear Information System (INIS)

    Creutz, M.

    1993-03-01

    Self organized criticality refers to the tendency of highly dissipative systems to drive themselves to a critical state. This has been proposed to explain why observed physics often displays a wide disparity of length and time scales. The phenomenon can be studied in simple cellular automaton models

  10. Estimating health-state utility values for patients with recurrent ovarian cancer using Functional Assessment of Cancer Therapy – General mapping algorithms

    Directory of Open Access Journals (Sweden)

    Hettle R

    2015-11-01

    Full Text Available Robert Hettle,1 John Borrill,2 Gaurav Suri,1 Jerome Wulff1 1Parexel Consulting, London, 2AstraZeneca, Macclesfield, UK Objectives: In the absence of EuroQol 5D data, mapping algorithms can be used to predict health-state utility values (HSUVs for use in economic evaluation. In a placebo-controlled Phase II study of olaparib maintenance therapy (NCT00753545, health-related quality of life was measured using the Functional Assessment of Cancer Therapy – Ovarian (FACT-O questionnaire. Our objective was to generate HSUVs from the FACT-O data using published mapping algorithms. Materials and methods: Algorithms were identified from a review of the literature. Goodness-of-fit and patient characteristics were compared to select the best-performing algorithm, and this was used to generate base-case HSUVs for the intention-to-treat population of the olaparib study and for patients with breast cancer antigen mutations. Results: Four FACT – General (the core component of FACT-O mapping algorithms were identified and compared. Under the preferred algorithm, treatment-related adverse events had no statistically significant effect on HSU (P>0.05. Discontinuation of the study treatment and breast cancer antigen mutation status were both associated with a reduction in HSUVs (–0.06, P=0.0009; and –0.03, P=0.0511, respectively. The mean HSUV recorded at assessment visits was 0.786. Conclusion: FACT – General mapping generated credible HSUVs for an economic evaluation of olaparib. As reported in other studies, different algorithms may produce significantly different estimates of HSUV. For this reason, it is important to test whether the choice of a specific algorithm changes the conclusions of an economic evaluation. Keywords: platinum sensitive ovarian cancer, EQ 5D, maintenance therapy, olaparib

  11. Explaining the “how” of self-esteem development : The self-organizing self-esteem model

    NARCIS (Netherlands)

    de Ruiter, Naomi M.P.; van Geert, Paul L.C.; Kunnen, E. Saskia

    2017-01-01

    The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The

  12. Genetic algorithms for map labeling

    NARCIS (Netherlands)

    Dijk, Steven Ferdinand van

    2001-01-01

    Map labeling is the cartographic problem of placing the names of features (for example cities or rivers) on the map. A good labeling has no intersections between labels. Even basic versions of the problem are NP-hard. In addition, realistic map-labeling problems deal with many cartographic

  13. Self-organization in metal complexes

    International Nuclear Information System (INIS)

    Radecka-Paryzek, W.

    1999-01-01

    Inorganic self-organization involves the spontaneous generation of well-defined supramolecular architectures from metal ions and organic ligands. The basic concept of supramolecular chemistry is a molecular recognition. When the substrate are metal ions, recognition is expressed in the stability and selectivity of metal ion complexation by organic ligands and depends on the geometry of the ligand and on their binding sites that it contains. The combination of the geometric features of the ligand units and the coordination geometries of the metal ions provides very efficient tool for the synthesis of novel, intriguing and highly sophisticated species such as catenanes, box structures, double and triple helicates with a variety of interesting properties. The article will focus on the examples of inorganic self-organization involving the templating as a first step for the assembly of supramolecular structures of high complexity. (author)

  14. Self-organizing networks

    DEFF Research Database (Denmark)

    Marchetti, Nicola; Prasad, Neeli R.; Johansson, Johan

    2010-01-01

    In this paper, a general overview of Self-Organizing Networks (SON), and the rationale and state-of-the-art of wireless SON are first presented. The technical and business requirements are then briefly treated, and the research challenges within the field of SON are highlighted. Thereafter, the r...

  15. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L

    2017-02-07

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

  16. Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps.

    Science.gov (United States)

    Hadjisolomou, Ekaterini; Stefanidis, Konstantinos; Papatheodorou, George; Papastergiadou, Evanthia

    2018-03-19

    During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters' relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl - a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

  17. Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Ekaterini Hadjisolomou

    2018-03-01

    Full Text Available During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA, cluster analysis, and a self-organizing map (SOM were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters’ relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl-a, water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

  18. Self-organizing periodicity in development: organ positioning in plants.

    Science.gov (United States)

    Bhatia, Neha; Heisler, Marcus G

    2018-02-08

    Periodic patterns during development often occur spontaneously through a process of self-organization. While reaction-diffusion mechanisms are often invoked, other types of mechanisms that involve cell-cell interactions and mechanical buckling have also been identified. Phyllotaxis, or the positioning of plant organs, has emerged as an excellent model system to study the self-organization of periodic patterns. At the macro scale, the regular spacing of organs on the growing plant shoot gives rise to the typical spiral and whorled arrangements of plant organs found in nature. In turn, this spacing relies on complex patterns of cell polarity that involve feedback between a signaling molecule - the plant hormone auxin - and its polar, cell-to-cell transport. Here, we review recent progress in understanding phyllotaxis and plant cell polarity and highlight the development of new tools that can help address the remaining gaps in our understanding. © 2018. Published by The Company of Biologists Ltd.

  19. USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT

    NARCIS (Netherlands)

    SCHOMAKER, L

    Comparisons are made between a number of stroke-based and character-based recognizers of connected cursive script. In both approaches a Kohonen self-organizing neural network is used as a feature-vector quantizer. It is found that a ''best match only'' character-based recognizer performs better than

  20. Aid decision algorithms to estimate the risk in congenital heart surgery.

    Science.gov (United States)

    Ruiz-Fernández, Daniel; Monsalve Torra, Ana; Soriano-Payá, Antonio; Marín-Alonso, Oscar; Triana Palencia, Eddy

    2016-04-01

    In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  1. Studies on Manfred Eigen's model for the self-organization of information processing.

    Science.gov (United States)

    Ebeling, W; Feistel, R

    2018-05-01

    In 1971, Manfred Eigen extended the principles of Darwinian evolution to chemical processes, from catalytic networks to the emergence of information processing at the molecular level, leading to the emergence of life. In this paper, we investigate some very general characteristics of this scenario, such as the valuation process of phenotypic traits in a high-dimensional fitness landscape, the effect of spatial compartmentation on the valuation, and the self-organized transition from structural to symbolic genetic information of replicating chain molecules. In the first part, we perform an analysis of typical dynamical properties of continuous dynamical models of evolutionary processes. In particular, we study the mapping of genotype to continuous phenotype spaces following the ideas of Wright and Conrad. We investigate typical features of a Schrödinger-like dynamics, the consequences of the high dimensionality, the leading role of saddle points, and Conrad's extra-dimensional bypass. In the last part, we discuss in brief the valuation of compartment models and the self-organized emergence of molecular symbols at the beginning of life.

  2. A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.

    Directory of Open Access Journals (Sweden)

    Dimitrios V Vavoulis

    Full Text Available Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm, often in combination with a local search method (such as gradient descent in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a

  3. Self-karaoke patterns: an interactive audio-visual system for handsfree live algorithm performance

    OpenAIRE

    Eldridge, Alice

    2014-01-01

    Self-karaoke Patterns, is an audiovisual study for improvised cello and live algorithms. The work is motivated in part by addressing the practical needs of the performer in ‘handsfree’ live algorithm contexts and in part an aesthetic concern with resolving the tension between conceptual dedication to autonomous algorithms and musical dedication to coherent performance. The elected approach is inspired by recent work investing the role of ‘shape’ in musical performance.

  4. Modeling Directional Selectivity Using Self-Organizing Delay-Aadaptation Maps

    OpenAIRE

    Tversky, Mr. Tal; Miikkulainen, Dr. Risto

    2002-01-01

    Using a delay adaptation learning rule, we model the activity-dependent development of directionally selective cells in the primary visual cortex. Based on input stimuli, a learning rule shifts delays to create synchronous arrival of spikes at cortical cells. As a result, delays become tuned creating a smooth cortical map of direction selectivity. This result demonstrates how delay adaption can serve as a powerful abstraction for modeling temporal learning in the brain.

  5. Domination, self-determination and circular organizing

    NARCIS (Netherlands)

    Romme, A.G.L.

    2002-01-01

    The emergence of self-organizing forms of control, based on the idea of self-determination, have challenged traditional forms of control based on the concept of domination. As such, self-determination has been put forward as an alternative rather than as a complement to domination. This paper

  6. QoS mapping algorithm for ETE QoS provisioning

    Science.gov (United States)

    Wu, Jian J.; Foster, Gerry

    2002-08-01

    End-to-End (ETE) Quality of Service (QoS) is critical for next generation wireless multimedia communication systems. To meet the ETE QoS requirements, Universal Mobile Telecommunication System (UMTS) requires not only meeting the 3GPP QoS requirements [1-2] but also mapping external network QoS classes to UMTS QoS classes. There are four Quality of Services (QoS) classes in UMTS; they are Conversational, Streaming, Interactive and Background. There are eight QoS classes for LAN in IEEE 802.1 (one reserved). ATM has four QoS categories. They are Constant Bit Rate (CBR) - highest priority, short queue for strict Cell Delay Variation (CDV), Variable Bit Rate (VBR) - second highest priority, short queues for real time, longer queues for non-real time, Guaranteed Frame Rate (GFR)/ Unspecified Bit Rate (UBR) with Minimum Desired Cell Rate (MDCR) - intermediate priority, dependent on service provider UBR/ Available Bit Rate (ABR) - lowest priority, long queues, large delay variation. DiffServ (DS) has six-bit DS codepoint (DSCP) available to determine the datagram's priority relative to other datagrams and therefore, up to 64 QoS classes are available from the IPv4 and IPv6 DSCP. Different organisations have tried to solve the QoS issues from their own perspective. However, none of them has a full picture for end-to-end QoS classes and how to map them among all QoS classes. Therefore, a universal QoS needs to be created and a new set of QoS classes to enable end-to-end (ETE) QoS provisioning is required. In this paper, a new set of ETE QoS classes is proposed and a mappings algorithm for different QoS classes that are proposed by different organisations is given. With our proposal, ETE QoS mapping and control can be implemented.

  7. Profile of the biodiesel B100 commercialized in the region of Londrina: application of artificial neural networks of the type self organizing maps

    Directory of Open Access Journals (Sweden)

    Vilson Machado de Campos Filho

    2015-10-01

    Full Text Available The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33 B (25 C (24 and D (15. The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP, through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN Self Organizing MAP (SOM in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

  8. Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee - doi: 10.4025/actascitechnol.v34i1.10892

    Directory of Open Access Journals (Sweden)

    Evandro Bona

    2011-11-01

    Full Text Available The electronic nose (EN is an instrument very used for food flavor analysis. However, it is also necessary to integrate the equipment with a multivariable pattern recognition system, and to this end the principal component analysis (PCA is the first choice. Alternatively, self-organizing maps (SOM had been also suggested, since they are a nonlinear and reliable technique. In this study SOM were used to distinguish soluble coffee according to EN data. The proposed methodology had identified all of the seven coffees evaluated; in addition, the groups and relationships detected were similar to those obtained through PCA. Also, the analysis of network weights allowed gathering the e-nose sensors into 4 groups according to the behavior regarding the samples. Results confirm SOM as an efficient tool to EN data pos-processing, and have showed the methodology as a promising choice for the development of new products and quality control of soluble coffee.

  9. Self-Organization Activities of College Students: Challenges and Opportunities

    Science.gov (United States)

    Shmurygina, Natalia; Bazhenova, Natalia; Bazhenov, Ruslan; Nikolaeva, Natalia; Tcytcarev, Andrey

    2016-01-01

    The article provides the analysis of self-organization activities of college students related to their participation in youth associations activities. The purpose of research is to disclose a degree of students' activities demonstration based on self-organization processes, assessment of existing self-organization practices of the youth,…

  10. Development of a MELCOR self-initialization algorithm for boiling water reactors

    International Nuclear Information System (INIS)

    Chien, C.S.; Wang, S.J.; Cheng, S.K.

    1996-01-01

    The MELCOR code, developed by Sandia National Laboratories, is suitable for calculating source terms and simulating severe accident phenomena of nuclear power plants. Prior to simulating a severe accident transient with MELCOR, the initial steady-state conditions must be generated in advance. The current MELCOR users' manuals do not provide a self-initialization procedure; this is the reason users have to adjust the initial conditions by themselves through a trial-and-error approach. A MELCOR self-initialization algorithm for boiling water reactor plants has been developed, which eliminates the tedious trial-and-error procedures and improves the simulation accuracy. This algorithm adjusts the important plant variable such as the dome pressure, downcomer level, and core flow rate to the desired conditions automatically. It is implemented through input with control functions provided in MELCOR. The reactor power and feedwater temperature are fed as input data. The initialization work of full-power conditions of the Kuosheng nuclear power station is cited as an example. These initial conditions are generated successfully with the developed algorithm. The generated initial conditions can be stored in a restart file and used for transient analysis. The methodology in this study improves the accuracy and consistency of transient calculations. Meanwhile, the algorithm provides all MELCOR users an easy and correct method for establishing the initial conditions

  11. Genomic and proteomic analysis with dynamically growing self ...

    African Journals Online (AJOL)

    The system proposed here is a tree structure, a new hierarchical clustering algorithm called a dynamically growing self-organizing tree (DGSOT) algorithm, which overcomes drawbacks of traditional hierarchical clustering algorithms. The DGSOT algorithm combines horizontal and vertical growth to construct a mutlifurcating ...

  12. Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2012-01-01

    Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

  13. Self-organizing of critical state in granulated superconductors

    International Nuclear Information System (INIS)

    Ginzburg, S.L.; Savitskaya, N.E.

    2000-01-01

    Critical state in granulated superconductors was studied on the basis of two mathematical models - the system of differential equations for calibration and invariant difference of phases and a simplified model describing the system of associated images and equivalent to the standard models to study self-organizing criticality. The critical state of granulated superconductors in all studied cases was shown to be self-organized. Besides, it is shown that the applied models are practically equivalent ones, that is they both show similar critical behavior and lead to coincidence of noncritical phenomena. For the first time one showed that the occurrence of self-organized critically within the system of nonlinear differential equations and its equivalence to self-organized critically in the standard models [ru

  14. An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

    Directory of Open Access Journals (Sweden)

    Jiaye Li

    2018-04-01

    Full Text Available River discharge, which represents the accumulation of surface water flowing into rivers and ultimately into the ocean or other water bodies, may have great impacts on water quality and the living organisms in rivers. However, the global knowledge of river discharge is still poor and worth exploring. This study proposes an efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Using the existing global runoff map and digital elevation model (DEM data as inputs, this method consists of three steps. First, the pixels of the runoff map and the DEM data are resampled into the same resolution (i.e., 0.01-degree. Second, the flow direction of each pixel of the DEM data (identified by the optimal flow path method used in drainage network extraction is determined and then applied to the corresponding pixel of the runoff map. Third, the river discharge of each pixel of the runoff map is calculated by summing the runoffs of all the pixels in the upstream of this pixel, similar to the upslope area accumulation step in drainage network extraction. Finally, a 0.01-degree global map of the mean annual river discharge is obtained. Moreover, a 0.5-degree global map of the mean annual river discharge is produced to display the results with a more intuitive perception. Compared against the existing global river discharge databases, the 0.01-degree map is of a generally high accuracy for the selected river basins, especially for the Amazon River basin with the lowest relative error (RE of 0.3% and the Yangtze River basin within the RE range of ±6.0%. However, it is noted that the results of the Congo and Zambezi River basins are not satisfactory, with RE values over 90%, and it is inferred that there may be some accuracy problems with the runoff map in these river basins.

  15. Complex Systems and Self-organization Modelling

    CERN Document Server

    Bertelle, Cyrille; Kadri-Dahmani, Hakima

    2009-01-01

    The concern of this book is the use of emergent computing and self-organization modelling within various applications of complex systems. The authors focus their attention both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used efficiently. This book is the outcome of a workshop meeting within ESM 2006 (Eurosis), held in Toulouse, France in October 2006.

  16. Self-Organized Construction with Continuous Building Material

    DEFF Research Database (Denmark)

    Heinrich, Mary Katherine; Wahby, Mostafa; Divband Soorati, Mohammad

    2016-01-01

    Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example...... investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can...... be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament...

  17. Self organising maps for visualising and modelling.

    Science.gov (United States)

    Brereton, Richard G

    2012-05-02

    The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring.

  18. Self organising maps for visualising and modelling

    Science.gov (United States)

    2012-01-01

    The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. PMID:22594434

  19. Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

    Science.gov (United States)

    Huang, Ping-Tzan; Jong, Tai-Lang; Li, Chien-Ming; Chen, Wei-Ling; Lin, Chia-Hung

    2017-08-01

    Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

  20. An Odometry-free Approach for Simultaneous Localization and Online Hybrid Map Building

    Directory of Open Access Journals (Sweden)

    Wei Hong Chin

    2016-11-01

    Full Text Available In this paper, a new approach is proposed for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM which comprises two main components: 1 Steady state genetic algorithm (SSGA for self-localization and occupancy grid map building; 2 Bayesian Adaptive Resonance Associative Memory (ARAM for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid-based, and it is incrementally constructed during the exploration process. During occupancy map building, robot estimated self-position is updated by SSGA. At the same time, robot estimated self position is transmit to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment is to verify the proposed method can be implemented in real world.

  1. Modeling self-organization of novel organic materials

    Science.gov (United States)

    Sayar, Mehmet

    In this thesis, the structural organization of oligomeric multi-block molecules is analyzed by computational analysis of coarse-grained models. These molecules form nanostructures with different dimensionalities, and the nanostructured nature of these materials leads to novel structural properties at different length scales. Previously, a number of oligomeric triblock rodcoil molecules have been shown to self-organize into mushroom shaped noncentrosymmetric nanostructures. Interestingly, thin films of these molecules contain polar domains and a finite macroscopic polarization. However, the fully polarized state is not the equilibrium state. In the first chapter, by solving a model with dipolar and Ising-like short range interactions, we show that polar domains are stable in films composed of aggregates as opposed to isolated molecules. Unlike classical molecular systems, these nanoaggregates have large intralayer spacings (a ≈ 6 nm), leading to a reduction in the repulsive dipolar interactions that oppose polar order within layers. This enables the formation of a striped pattern with polar domains of alternating directions. The energies of the possible structures at zero temperature are computed exactly and results of Monte Carlo simulations are provided at non-zero temperatures. In the second chapter, the macroscopic polarization of such nanostructured films is analyzed in the presence of a short range surface interaction. The surface interaction leads to a periodic domain structure where the balance between the up and down domains is broken, and therefore films of finite thickness have a net macroscopic polarization. The polarization per unit volume is a function of film thickness and strength of the surface interaction. Finally, in chapter three, self-organization of organic molecules into a network of one dimensional objects is analyzed. Multi-block organic dendron rodcoil molecules were found to self-organize into supramolecular nanoribbons (threads) and

  2. Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

    Directory of Open Access Journals (Sweden)

    Lixiong Xu

    2017-01-01

    Full Text Available As one of the most effective function mining algorithms, Gene Expression Programming (GEP algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.

  3. A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2011-08-01

    Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

  4. A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

    Directory of Open Access Journals (Sweden)

    Gwo-Fong Lin

    2016-01-01

    Full Text Available This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs model, the self-organizing map (SOM is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression model because of its accuracy and robustness.

  5. Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

    Science.gov (United States)

    Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

    2017-12-01

    Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

  6. Quantum self-organization and nuclear collectivities

    Science.gov (United States)

    Otsuka, T.; Tsunoda, Y.; Togashi, T.; Shimizu, N.; Abe, T.

    2018-02-01

    The quantum self-organization is introduced as one of the major underlying mechanisms of the quantum many-body systems. In the case of atomic nuclei as an example, two types of the motion of nucleons, single-particle states and collective modes, dominate the structure of the nucleus. The outcome of the collective mode is determined basically by the balance between the effect of the mode-driving force (e.g., quadrupole force for the ellipsoidal deformation) and the resistance power against it. The single-particle energies are one of the sources to produce such resistance power: a coherent collective motion is more hindered by larger gaps between relevant single particle states. Thus, the single-particle state and the collective mode are “enemies” each other. However, the nuclear forces are demonstrated to be rich enough so as to enhance relevant collective mode by reducing the resistance power by changing singleparticle energies for each eigenstate through monopole interactions. This will be verified with the concrete example taken from Zr isotopes. Thus, when the quantum self-organization occurs, single-particle energies can be self-organized, being enhanced by (i) two quantum liquids, e.g., protons and neutrons, (ii) two major force components, e.g., quadrupole interaction (to drive collective mode) and monopole interaction (to control resistance). In other words, atomic nuclei are not necessarily like simple rigid vases containing almost free nucleons, in contrast to the naïve Fermi liquid picture. Type II shell evolution is considered to be a simple visible case involving excitations across a (sub)magic gap. The quantum self-organization becomes more important in heavier nuclei where the number of active orbits and the number of active nucleons are larger. The quantum self-organization is a general phenomenon, and is expected to be found in other quantum systems.

  7. High-resolution charge carrier mobility mapping of heterogeneous organic semiconductors

    Science.gov (United States)

    Button, Steven W.; Mativetsky, Jeffrey M.

    2017-08-01

    Organic electronic device performance is contingent on charge transport across a heterogeneous landscape of structural features. Methods are therefore needed to unravel the effects of local structure on overall electrical performance. Using conductive atomic force microscopy, we construct high-resolution out-of-plane hole mobility maps from arrays of 5000 to 16 000 current-voltage curves. To demonstrate the efficacy of this non-invasive approach for quantifying and mapping local differences in electrical performance due to structural heterogeneities, we investigate two thin film test systems, one bearing a heterogeneous crystal structure [solvent vapor annealed 5,11-Bis(triethylsilylethynyl)anthradithiophene (TES-ADT)—a small molecule organic semiconductor] and one bearing a heterogeneous chemical composition [p-DTS(FBTTh2)2:PC71BM—a high-performance organic photovoltaic active layer]. TES-ADT shows nearly an order of magnitude difference in hole mobility between semicrystalline and crystalline areas, along with a distinct boundary between the two regions, while p-DTS(FBTTh2)2:PC71BM exhibits subtle local variations in hole mobility and a nanoscale domain structure with features below 10 nm in size. We also demonstrate mapping of the built-in potential, which plays a significant role in organic light emitting diode and organic solar cell operation.

  8. Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

    Directory of Open Access Journals (Sweden)

    S. Nakaoka

    2013-09-01

    Full Text Available This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST, mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES. The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM to 20.2 μatm (for independent dataset. We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

  9. Relativistic fluid theories - Self organization

    International Nuclear Information System (INIS)

    Mahajan, S.M.; Hazeltine, R.D.; Yoshida, Z.

    2003-01-01

    Developments in two distinct but related subjects are reviewed: 1) Formulation and investigation of closed fluid theories which transcend the limitations of standard magnetohydrodynamics (MHD), in particular, theories which are valid in the long mean free path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently, and 2) Exploitation of the two-fluid theories to derive new plasma configurations in which the flow-field is a co-determinant of the overall dynamics; some of these states belong to the category of self-organized relaxed states. Physical processes which may provide a route to self-organization and complexity are also explored. (author)

  10. An Interactive Control Algorithm Used for Equilateral Triangle Formation with Robotic Sensors

    Science.gov (United States)

    Li, Xiang; Chen, Hongcai

    2014-01-01

    This paper describes an interactive control algorithm, called Triangle Formation Algorithm (TFA), used for three neighboring robotic sensors which are distributed randomly to self-organize into and equilateral triangle (E) formation. The algorithm is proposed based on the triangular geometry and considering the actual sensors used in robotics. In particular, the stability of the TFA, which can be executed by robotic sensors independently and asynchronously for E formation, is analyzed in details based on Lyapunov stability theory. Computer simulations are carried out for verifying the effectiveness of the TFA. The analytical results and simulation studies indicate that three neighboring robots employing conventional sensors can self-organize into E formations successfully regardless of their initial distribution using the same TFAs. PMID:24759118

  11. Image Encryption Algorithm Based on a Novel Improper Fractional-Order Attractor and a Wavelet Function Map

    Directory of Open Access Journals (Sweden)

    Jian-feng Zhao

    2017-01-01

    Full Text Available This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks.

  12. Self-Organized Transport System

    Science.gov (United States)

    2009-09-28

    This report presents the findings of the simulation model for a self-organized transport system where traffic lights communicate with neighboring traffic lights and make decisions locally to adapt to traffic conditions in real time. The model is insp...

  13. Optical electronics self-organized integration and applications

    CERN Document Server

    Yoshimura, Tetsuzo

    2012-01-01

    IntroductionFrom Electronics to Optical ElectronicsAnalysis Tools for Optical CircuitsSelf-Organized Optical Waveguides: Theoretical AnalysisSelf-Organized Optical Waveguides: Experimental DemonstrationsOptical Waveguide Films with Vertical Mirrors 3-D Optical Circuits with Stacked Waveguide Films Heterogeneous Thin-Film Device IntegrationOptical Switches OE Hardware Built by Optical ElectronicsIntegrated Solar Energy Conversion SystemsFuture Challenges.

  14. Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qiuwen, E-mail: qchen@rcees.ac.cn [RCEES, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 10085 (China); China Three Gorges University, Daxuelu 8, Yichang 443002 (China); CEER, Nanjing Hydraulics Research Institute, Guangzhoulu 223, Nanjing 210029 (China); Rui, Han; Li, Weifeng; Zhang, Yanhui [RCEES, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 10085 (China)

    2014-06-01

    Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004–2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial–temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial–temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. - Highlights: • An innovative method is developed to analyze algal bloom risks with uncertainties. • The algal blooms in Taihu Lake showed obvious spatial and temporal patterns. • The lake is mainly characterized as moderate bloom but with high uncertainty. • Severe bloom with low uncertainty appeared occasionally in the northwest part. • The results provide important information to bloom monitoring and management.

  15. Complexity in plasma: From self-organization to geodynamo

    International Nuclear Information System (INIS)

    Sato, T.

    1996-01-01

    A central theme of open-quote open-quote Complexity close-quote close-quote is the question of the creation of ordered structure in nature (self-organization). The assertion is made that self-organization is governed by three key processes, i.e., energy pumping, entropy expulsion and nonlinearity. Extensive efforts have been done to confirm this assertion through computer simulations of plasmas. A system exhibits markedly different features in self-organization, depending on whether the energy pumping is instantaneous or continuous, or whether the produced entropy is expulsed or reserved. The nonlinearity acts to bring a nonequilibrium state into a bifurcation, thus resulting in a new structure along with an anomalous entropy production. As a practical application of our grand view of self-organization a preferential generation of a dipole magnetic field is successfully demonstrated. copyright 1996 American Institute of Physics

  16. Adaptive symbiotic organisms search (SOS algorithm for structural design optimization

    Directory of Open Access Journals (Sweden)

    Ghanshyam G. Tejani

    2016-07-01

    Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.

  17. Evaluation of Changes in Effluent Quality from Industrial Complexes on the Korean Nationwide Scale Using a Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    Mi-Jung Bae

    2012-04-01

    Full Text Available One of the major issues related to the environment in the 21st century is sustainable development. The innovative economic growth policy has supported relatively successful economic development, but poor environmental conservation efforts, have consequently resulted in serious water quality pollution issues. Hence, assessments of water quality and health are fundamental processes towards conserving and restoring aquatic ecosystems. In this study, we characterized spatial and temporal changes in water quality (specifically physico-chemical variables plus priority and non-priority pollutants of discharges from industrial complexes on a national scale in Korea. The data were provided by the Water Quality Monitoring Program operated by the Ministry of Environment, Korea and were measured from 1989 to 2008 on a monthly basis at 61 effluent monitoring sites located at industrial complexes. Analysis of monthly and annual changes in water quality, using the seasonal Mann-Kendall test, indicated an improvement in water quality, which was inferred from a continuous increase in dissolved oxygen and decrease in other water quality factors. A Self-Organizing Map, which is an unsupervised artificial neural network, also indicated an improvement of effluent water quality, by showing spatial and temporal differences in the effluent water quality as well as in the occurrence of priority pollutants. Finally, our results suggested that continued long-term monitoring is necessary to establish plans and policies for wastewater management and health assessment.

  18. Evaluation of Changes in Effluent Quality from Industrial Complexes on the Korean Nationwide Scale Using a Self-Organizing Map

    Science.gov (United States)

    Bae, Mi-Jung; Kim, Jun-Su; Park, Young-Seuk

    2012-01-01

    One of the major issues related to the environment in the 21st century is sustainable development. The innovative economic growth policy has supported relatively successful economic development, but poor environmental conservation efforts, have consequently resulted in serious water quality pollution issues. Hence, assessments of water quality and health are fundamental processes towards conserving and restoring aquatic ecosystems. In this study, we characterized spatial and temporal changes in water quality (specifically physico-chemical variables plus priority and non-priority pollutants) of discharges from industrial complexes on a national scale in Korea. The data were provided by the Water Quality Monitoring Program operated by the Ministry of Environment, Korea and were measured from 1989 to 2008 on a monthly basis at 61 effluent monitoring sites located at industrial complexes. Analysis of monthly and annual changes in water quality, using the seasonal Mann-Kendall test, indicated an improvement in water quality, which was inferred from a continuous increase in dissolved oxygen and decrease in other water quality factors. A Self-Organizing Map, which is an unsupervised artificial neural network, also indicated an improvement of effluent water quality, by showing spatial and temporal differences in the effluent water quality as well as in the occurrence of priority pollutants. Finally, our results suggested that continued long-term monitoring is necessary to establish plans and policies for wastewater management and health assessment. PMID:22690190

  19. AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

    Directory of Open Access Journals (Sweden)

    Xin Yuan

    2017-05-01

    Full Text Available In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.

  20. The role of hierarchy in self-organizing systems

    NARCIS (Netherlands)

    Ollfen, van W.; Romme, A.G.L.

    1995-01-01

    This paper discusses the role of hierarchy in human systems. Two kinds of self-organizing processes are distinguished: conservative and dissipative self-organization. The former leads to rather stable, specialistic systems, whereas the latter leads to continuously changing generalistic systems. When

  1. Integrative mapping analysis of chicken microchromosome 16 organization

    Directory of Open Access Journals (Sweden)

    Bed'hom Bertrand

    2010-11-01

    Full Text Available Abstract Background The chicken karyotype is composed of 39 chromosome pairs, of which 9 still remain totally absent from the current genome sequence assembly, despite international efforts towards complete coverage. Some others are only very partially sequenced, amongst which microchromosome 16 (GGA16, particularly under-represented, with only 433 kb assembled for a full estimated size of 9 to 11 Mb. Besides the obvious need of full genome coverage with genetic markers for QTL (Quantitative Trait Loci mapping and major genes identification studies, there is a major interest in the detailed study of this chromosome because it carries the two genetically independent MHC complexes B and Y. In addition, GGA16 carries the ribosomal RNA (rRNA genes cluster, also known as the NOR (nucleolus organizer region. The purpose of the present study is to construct and present high resolution integrated maps of GGA16 to refine its organization and improve its coverage with genetic markers. Results We developed 79 STS (Sequence Tagged Site markers to build a physical RH (radiation hybrid map and 34 genetic markers to extend the genetic map of GGA16. We screened a BAC (Bacterial Artificial Chromosome library with markers for the MHC-B, MHC-Y and rRNA complexes. Selected clones were used to perform high resolution FISH (Fluorescent In Situ Hybridization mapping on giant meiotic lampbrush chromosomes, allowing meiotic mapping in addition to the confirmation of the order of the three clusters along the chromosome. A region with high recombination rates and containing PO41 repeated elements separates the two MHC complexes. Conclusions The three complementary mapping strategies used refine greatly our knowledge of chicken microchromosome 16 organisation. The characterisation of the recombination hotspots separating the two MHC complexes demonstrates the presence of PO41 repetitive sequences both in tandem and inverted orientation. However, this region still needs to

  2. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

    Science.gov (United States)

    Shen, Lili; Guo, Jiming; Wang, Lei

    2018-06-06

    The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  3. SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

    Directory of Open Access Journals (Sweden)

    Hossam Gabbar

    2016-03-01

    Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

  4. Estimation of austral summer net community production in the Amundsen Sea: Self-organizing map analysis approach

    Science.gov (United States)

    Park, K.; Hahm, D.; Lee, D. G.; Rhee, T. S.; Kim, H. C.

    2014-12-01

    The Amundsen Sea, Antarctica, has been known for one of the most susceptible region to the current climate change such as sea ice melting and sea surface temperature change. In the Southern Ocean, a predominant amount of primary production is occurring in the continental shelf region. Phytoplankton blooms take place during the austral summer due to the limited sunlit and sea ice cover. Thus, quantifying the variation of summer season net community production (NCP) in the Amundsen Sea is essential to analyze the influence of climate change to the variation of biogeochemical cycle in the Southern Ocean. During the past three years of 2011, 2012 and 2014 in austral summer, we have conducted underway observations of ΔO2/Ar and derived NCP of the Amundsen Sea. Despite the importance of NCP for understanding biological carbon cycle of the ocean, the observations are rather limited to see the spatio-temporal variation in the Amundsen Sea. Therefore, we applied self-organizing map (SOM) analysis to expand our observed data sets and estimate the NCP during the summer season. SOM analysis, a type of artificial neural network, has been proved to be a useful method for extracting and classifying features in geoscience. In oceanography, SOM has applied for the analysis of various properties of the seawater such as sea surface temperature, chlorophyll concentration, pCO2, and NCP. Especially it is useful to expand a spatial coverage of direct measurements or to estimate properties whose satellite observations are technically or spatially limited. In this study, we estimate summer season NCP and find a variables set which optimally delineates the NCP variation in the Amundsen Sea as well. Moreover, we attempt to analyze the interannual variation of the Amundsen Sea NCP by taking climatological factors into account for the SOM analysis.

  5. REMap: Operon map of M. tuberculosis based on RNA sequence data.

    Science.gov (United States)

    Pelly, Shaaretha; Winglee, Kathryn; Xia, Fang Fang; Stevens, Rick L; Bishai, William R; Lamichhane, Gyanu

    2016-07-01

    A map of the transcriptional organization of genes of an organism is a basic tool that is necessary to understand and facilitate a more accurate genetic manipulation of the organism. Operon maps are largely generated by computational prediction programs that rely on gene conservation and genome architecture and may not be physiologically relevant. With the widespread use of RNA sequencing (RNAseq), the prediction of operons based on actual transcriptome sequencing rather than computational genomics alone is much needed. Here, we report a validated operon map of Mycobacterium tuberculosis, developed using RNAseq data from both the exponential and stationary phases of growth. At least 58.4% of M. tuberculosis genes are organized into 749 operons. Our prediction algorithm, REMap (RNA Expression Mapping of operons), considers the many cases of transcription coverage of intergenic regions, and avoids dependencies on functional annotation and arbitrary assumptions about gene structure. As a result, we demonstrate that REMap is able to more accurately predict operons, especially those that contain long intergenic regions or functionally unrelated genes, than previous operon prediction programs. The REMap algorithm is publicly available as a user-friendly tool that can be readily modified to predict operons in other bacteria. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

    Science.gov (United States)

    Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

    2017-07-01

    Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

  7. Self-organized computation with unreliable, memristive nanodevices

    International Nuclear Information System (INIS)

    Snider, G S

    2007-01-01

    Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network-a self-organized spatial filter array-that demonstrate its performance as a function of defects and device variation

  8. Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

    Directory of Open Access Journals (Sweden)

    Jorge L. Zapotecatl

    2017-01-01

    Full Text Available Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave and a similar performance (close to optimal compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.

  9. Association mapping of main tomato fruit sugars and organic acids

    Directory of Open Access Journals (Sweden)

    Jiantao Zhao

    2016-08-01

    Full Text Available Association mapping has been widely used to map the significant associated loci responsible for natural variation in complex traits and are valuable for crop improvement. Sugars and organic acids are the most important metabolites in tomato fruits. We used a collection of 174 tomato accessions composed of S. lycopersicum (123 accessions and S. lycopersicum var cerasiforme (51 accessions to detect significantly associated loci controlling the variation of main sugars and organic acids. The accessions were genotyped with 182 SSRs spreading over the tomato genome. Association mapping was conducted on the main sugars and organic acids detected by gas chromatography-mass spectrometer (GC-MS over two years using the mixed linear model (MLM. We detected a total of 58 significantly associated loci (P<0.001 for the 17 sugars and organic acids, including fructose, glucose, sucrose, citric acid, malic acid. These results not only co-localized with several reported QTLs, including fru9.1/PV, suc9.1/PV, ca2.1/HS, ca3.1/PV, ca4.1/PV and ca8.1/PV, but also provided a list of candidate significantly associated loci to be functionally validated. These significantly associated loci could be used for deciphering the genetic architecture of tomato fruit sugars and organic acids and for tomato quality breeding.

  10. Mathematical model and coordination algorithms for ensuring complex security of an organization

    Science.gov (United States)

    Novoseltsev, V. I.; Orlova, D. E.; Dubrovin, A. S.; Irkhin, V. P.

    2018-03-01

    The mathematical model of coordination when ensuring complex security of the organization is considered. On the basis of use of a method of casual search three types of algorithms of effective coordination adequate to mismatch level concerning security are developed: a coordination algorithm at domination of instructions of the coordinator; a coordination algorithm at domination of decisions of performers; a coordination algorithm at parity of interests of the coordinator and performers. Assessment of convergence of the algorithms considered above it was made by carrying out a computing experiment. The described algorithms of coordination have property of convergence in the sense stated above. And, the following regularity is revealed: than more simply in the structural relation the algorithm, for the smaller number of iterations is provided to those its convergence.

  11. Singularity spectrum of self-organized criticality

    International Nuclear Information System (INIS)

    Canessa, E.

    1992-10-01

    I introduce a simple continuous probability theory based on the Ginzburg-Landau equation that provides for the first time a common analytical basis to relate and describe the main features of two seemingly different phenomena of condensed-matter physics, namely self-organized criticality and multifractality. Numerical support is given by a comparison with reported simulation data. Within the theory the origin of self-organized critical phenomena is analysed in terms of a nonlinear singularity spectrum different form the typical convex shape due to multifractal measures. (author). 29 refs, 5 figs

  12. A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map.

    Science.gov (United States)

    Tsai, Wen-Ping; Huang, Shih-Pin; Cheng, Su-Ting; Shao, Kwang-Tsao; Chang, Fi-John

    2017-02-01

    The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water

  13. Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization

    International Nuclear Information System (INIS)

    Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.

    2013-01-01

    Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems

  14. Information Driven Ecohydrologic Self-Organization

    Directory of Open Access Journals (Sweden)

    Benjamin L. Ruddell

    2010-09-01

    Full Text Available Variability plays an important role in the self-organized interaction between vegetation and its environment, yet the principles that characterize the role of the variability in these interactions remain elusive. To address this problem, we study the dependence between a number of variables measured at flux towers by quantifying the information flow between the different variables along with the associated time lag. By examining this network of feedback loops for seven ecosystems in different climate regions, we find that: (1 the feedback tends to maximize information production in the entire system, and the latter increases with increasing variability within the whole system; and (2 variables that participate in feedback exhibit moderated variability. Self-organization arises as a tradeoff where the ability of the total system to maximize information production through feedback is limited by moderate variability of the participating variables. This relationship between variability and information production leads to the emergence of ordered organization.

  15. Self-Organization during Friction of Slide Bearing Antifriction Materials

    Directory of Open Access Journals (Sweden)

    Iosif S. Gershman

    2015-12-01

    Full Text Available This article discusses the peculiarities of self-organization behavior and formation of dissipative structures during friction of antifriction alloys for slide bearings against a steel counterbody. It shows that during self-organization, the moment of friction in a tribosystem may be decreasing with the load growth and in the bifurcations of the coefficient of friction with respect to load. Self-organization and the formation of dissipative structures lead to an increase in the seizure load.

  16. Concept and Feasibility Study of Self-Organized Electrochemical Devices

    National Research Council Canada - National Science Library

    Moorehead, William

    2002-01-01

    .... In this work, using attractive and repulsive London-van der Waals forces, a self-organized, interpenetrating, separator-free rechargeable lithium ion battery called a self-organized battery system (SBS) is proposed...

  17. Self-organization of spatial patterning in human embryonic stem cells

    Science.gov (United States)

    Deglincerti, Alessia; Etoc, Fred; Ozair, M. Zeeshan; Brivanlou, Ali H.

    2017-01-01

    The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatio-temporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents a unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this essay, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. PMID:26970615

  18. Emergence or self-organization?: Look to the soil population.

    Science.gov (United States)

    Addiscott, Tom

    2011-07-01

    EMERGENCE IS NOT WELL DEFINED, BUT ALL EMERGENT SYSTEMS HAVE THE FOLLOWING CHARACTERISTICS: the whole is more than the sum of the parts, they show bottom-up rather top-down organization and, if biological, they involve chemical signaling. Self-organization can be understood in terms of the second and third stages of thermodynamics enabling these stages used as analogs of ecosystem functioning. The second stage system was suggested earlier to provide a useful analog of the behavior of natural and agricultural ecosystems subjected to perturbations, but for this it needs the capacity for self-organization. Considering the hierarchy of the ecosystem suggests that this self-organization is provided by the third stage, whose entropy maximization acts as an analog of that of the soil population when it releases small molecules from much larger molecules in dead plant matter. This it does as vigorously as conditions allow. Through this activity, the soil population confers self-organization at both the ecosystem and the global level. The soil population has been seen as both emergent and self-organizing, supporting the suggestion that the two concepts are are so closely linked as to be virtually interchangeable. If this idea is correct one of the characteristics of a biological emergent system seems to be the ability to confer self-organization on an ecosystem or other entity which may be larger than itself. The beehive and the termite colony are emergent systems which share this ability.

  19. An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing

    KAUST Repository

    Han, Renmin

    2017-12-24

    Long-reads, point-of-care, and PCR-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the global mapping between the raw electrical current signal sequence and the expected signal sequence from the pore model serves as the key building block to base calling, reads mapping, variant identification, and methylation detection. However, the ultra-long reads of nanopore sequencing and an order of magnitude difference in the sampling speeds of the two sequences make the classical dynamic time warping (DTW) and its variants infeasible to solve the problem. Here, we propose a novel multi-level DTW algorithm, cwDTW, based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa, as well as two benchmark datasets from previous studies, demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can almost always generate warping paths that are very close to the original DTW, which are remarkably more accurate than the state-of-the-art methods including FastDTW and PrunedDTW. Meanwhile, on the real nanopore datasets, cwDTW is about 440 times faster than FastDTW and 3000 times faster than the original DTW. Our program is available at https://github.com/realbigws/cwDTW.

  20. Deterministic self-organization: Ordered positioning of InAs quantum dots by self-organized anisotropic strain engineering on patterned GaAs(311)B

    International Nuclear Information System (INIS)

    Selcuk, E.; Hamhuis, G.J.; Noetzel, R.

    2009-01-01

    Laterally ordered InGaAs quantum dot (QD) arrays, InAs QD molecules, and single InAs QDs in a spot-like periodic arrangement are created by self-organized anisotropic strain engineering of InGaAs/GaAs superlattice (SL) templates on planar GaAs (311)B substrates in molecular beam epitaxy. On shallow- and deep-patterned substrates the respectively generated steps and facets guide the self-organization process during SL template formation to create more complex ordering such as periodic stripes, depending on pattern design. Here we demonstrate for patterns such as shallow- and deepetched round holes and deep-etched zigzag mesas that the self-organized periodic arrangement of QD molecules and single QDs is spatially locked to the pattern sidewalls and corners. This extends the concept of guided self-organization to deterministic self-organization. Absolute position control of the QDs is achieved without one-to-one pattern definition. This guarantees the excellent arrangement control of the ordered QD molecules and single QDs with strong photoluminescence emission up to room temperature, which is required for future quantum functional devices. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  1. Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems.

    Science.gov (United States)

    Semnani, Samaneh Hosseini; Basir, Otman A

    2015-01-01

    The ability of sensors to self-organize is an important asset in surveillance sensor networks. Self-organize implies self-control at the sensor level and coordination at the network level. Biologically inspired approaches have recently gained significant attention as a tool to address the issue of sensor control and coordination in sensor networks. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous robust dynamic area coverage and target coverage. These two coverage performance objectives are inherently conflicting. This paper presents Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. This allows the algorithm to strike balance between robust area coverage and target coverage. Such balance is facilitated via flock-sensor coordination. The performance of the proposed Semi-Flocking algorithm is examined and compared with other two flocking-based algorithms once using randomly moving targets and once using a standard walking pedestrian dataset. The results of both experiments show that the Semi-Flocking algorithm outperforms both the Flocking algorithm and the Anti-Flocking algorithm with respect to the area of coverage and the target coverage objectives. Furthermore, the results show that the proposed algorithm demonstrates shorter target detection time and fewer undetected targets than the other two flocking-based algorithms.

  2. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

    Directory of Open Access Journals (Sweden)

    Lili Shen

    2018-06-01

    Full Text Available The network real-time kinematic (RTK technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI, and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs, robotic equipment, etc. require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  3. Self-Organization of Spatial Patterning in Human Embryonic Stem Cells.

    Science.gov (United States)

    Deglincerti, Alessia; Etoc, Fred; Ozair, M Zeeshan; Brivanlou, Ali H

    2016-01-01

    The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatiotemporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents an unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this chapter, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. © 2016 Elsevier Inc. All rights reserved.

  4. On the efficiency of chaos optimization algorithms for global optimization

    International Nuclear Information System (INIS)

    Yang Dixiong; Li Gang; Cheng Gengdong

    2007-01-01

    Chaos optimization algorithms as a novel method of global optimization have attracted much attention, which were all based on Logistic map. However, we have noticed that the probability density function of the chaotic sequences derived from Logistic map is a Chebyshev-type one, which may affect the global searching capacity and computational efficiency of chaos optimization algorithms considerably. Considering the statistical property of the chaotic sequences of Logistic map and Kent map, the improved hybrid chaos-BFGS optimization algorithm and the Kent map based hybrid chaos-BFGS algorithm are proposed. Five typical nonlinear functions with multimodal characteristic are tested to compare the performance of five hybrid optimization algorithms, which are the conventional Logistic map based chaos-BFGS algorithm, improved Logistic map based chaos-BFGS algorithm, Kent map based chaos-BFGS algorithm, Monte Carlo-BFGS algorithm, mesh-BFGS algorithm. The computational performance of the five algorithms is compared, and the numerical results make us question the high efficiency of the chaos optimization algorithms claimed in some references. It is concluded that the efficiency of the hybrid optimization algorithms is influenced by the statistical property of chaotic/stochastic sequences generated from chaotic/stochastic algorithms, and the location of the global optimum of nonlinear functions. In addition, it is inappropriate to advocate the high efficiency of the global optimization algorithms only depending on several numerical examples of low-dimensional functions

  5. Self-organizing maps of Kohonen (SOM) applied to multidimensional monitoring data of the IEA-R1 nuclear research reactor

    International Nuclear Information System (INIS)

    Affonso, Gustavo S.; Pereira, Iraci M.; Mesquita, Roberto N. de; Bueno, Elaine I.

    2011-01-01

    Multivariate statistics comprise a set of statistical methods used in situations where many variables are database space subsets. Initially applied to human, social and biological sciences, these methods are being applied to many other areas such as education, geology, chemistry, physics, engineering, and many others. This spectra expansion was possible due to recent technological development of computation hardware and software that allows high and complex databases to be treated iteratively enabling further analysis. Following this trend, the neural networks called Self-Organizing Maps are turning into a powerful tool on visualization of implicit and unknown correlations in big sized database sets. Originally created by Kohonen in 1981, it was applied to speech recognition tasks. The SOM is being used as a comparative parameter to evaluate the performance of new multidimensional analysis methodologies. Most of methods require good variable input selection criteria and SOM has contributed to clustering, classification and prediction of multidimensional engineering process variables. This work proposes a method of applying SOM to a set of 58 IEA-R1 operational variables at IPEN research reactor which are monitored by a Data Acquisition System (DAS). This data set includes variables as temperature, flow mass rate, coolant level, nuclear radiation, nuclear power and control bars position. DAS enables the creation and storage of historical data which are used to contribute to Failure Detection and Monitoring System development. Results show good agreement with previous studies using other methods as GMDH and other predictive methods. (author)

  6. Self-organizing maps of Kohonen (SOM) applied to multidimensional monitoring data of the IEA-R1 nuclear research reactor

    Energy Technology Data Exchange (ETDEWEB)

    Affonso, Gustavo S.; Pereira, Iraci M.; Mesquita, Roberto N. de, E-mail: rnavarro@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Bueno, Elaine I., E-mail: ebueno@ifsp.gov.b [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), SP (Brazil)

    2011-07-01

    Multivariate statistics comprise a set of statistical methods used in situations where many variables are database space subsets. Initially applied to human, social and biological sciences, these methods are being applied to many other areas such as education, geology, chemistry, physics, engineering, and many others. This spectra expansion was possible due to recent technological development of computation hardware and software that allows high and complex databases to be treated iteratively enabling further analysis. Following this trend, the neural networks called Self-Organizing Maps are turning into a powerful tool on visualization of implicit and unknown correlations in big sized database sets. Originally created by Kohonen in 1981, it was applied to speech recognition tasks. The SOM is being used as a comparative parameter to evaluate the performance of new multidimensional analysis methodologies. Most of methods require good variable input selection criteria and SOM has contributed to clustering, classification and prediction of multidimensional engineering process variables. This work proposes a method of applying SOM to a set of 58 IEA-R1 operational variables at IPEN research reactor which are monitored by a Data Acquisition System (DAS). This data set includes variables as temperature, flow mass rate, coolant level, nuclear radiation, nuclear power and control bars position. DAS enables the creation and storage of historical data which are used to contribute to Failure Detection and Monitoring System development. Results show good agreement with previous studies using other methods as GMDH and other predictive methods. (author)

  7. Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

    Directory of Open Access Journals (Sweden)

    Fronita Mona

    2018-01-01

    Full Text Available Traveling Salesman Problem (TSP is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

  8. Methodical Aspects of Applying Strategy Map in an Organization

    Directory of Open Access Journals (Sweden)

    Piotr Markiewicz

    2013-06-01

    Full Text Available One of important aspects of strategic management is the instrumental aspect included in a rich set of methods and techniques used at particular stages of strategic management process. The object of interest in this study is the development of views and the implementation of strategy as an element of strategic management and instruments in the form of methods and techniques. The commonly used method in strategy implementation and measuring progress is Balanced Scorecard (BSC. The method was created as a result of implementing the project “Measuring performance in the Organization of the future” of 1990, completed by a team under the supervision of David Norton (Kaplan, Norton 2002. The developed method was used first of all to evaluate performance by decomposition of a strategy into four perspectives and identification of measures of achievement. In the middle of 1990s the method was improved by enriching it, first of all, with a strategy map, in which the process of transition of intangible assets into tangible financial effects is reflected (Kaplan, Norton 2001. Strategy map enables illustration of cause and effect relationship between processes in all four perspectives and performance indicators at the level of organization. The purpose of the study being prepared is to present methodical conditions of using strategy maps in the strategy implementation process in organizations of different nature.

  9. Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants; Classificacao de defeitos em tubos de gerador de vapor de plantas nucleares utilizando mapas auto-organizaveis

    Energy Technology Data Exchange (ETDEWEB)

    Mesquita, Roberto Navarro de

    2002-07-01

    This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

  10. Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

    Science.gov (United States)

    Kempe, Matthias; Grunz, Andreas; Memmert, Daniel

    2015-01-01

    The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal data. In this study, we applied the merge self-organising map (MSOM) to spatio-temporal data. To do so, we investigated the ability of MSOM's to analyse spatio-temporal data and compared its performance to the common dynamical controlled network (DyCoN) approach to analyse team sport position data. The position data of 10 players were recorded via the Ubisense tracking system during a basketball game. Furthermore, three different pre-selected plays were recorded for classification. Following data preparation, the different nets were trained with the data of the first half. The training success of both networks was evaluated by achieved entropy. The second half of the basketball game was presented to both nets for automatic classification. Both approaches were able to present the trained data extremely well and to detect the pre-selected plays correctly. In conclusion, MSOMs are a useful tool to analyse spatial-temporal data, especially in team sports. By their direct inclusion of different time length of tactical patterns, they open up new opportunities within team sports.

  11. Self-organization at the frictional interface for green tribology.

    Science.gov (United States)

    Nosonovsky, Michael

    2010-10-28

    Despite the fact that self-organization during friction has received relatively little attention from tribologists so far, it has the potential for the creation of self-healing and self-lubricating materials, which are important for green or environment-friendly tribology. The principles of the thermodynamics of irreversible processes and of the nonlinear theory of dynamical systems are used to investigate the formation of spatial and temporal structures during friction. The transition to the self-organized state with low friction and wear occurs through destabilization of steady-state (stationary) sliding. The criterion for destabilization is formulated and several examples are discussed: the formation of a protective film, microtopography evolution and slip waves. The pattern formation may involve self-organized criticality and reaction-diffusion systems. A special self-healing mechanism may be embedded into the material by coupling the corresponding required forces. The analysis provides the structure-property relationship, which can be applied for the design optimization of composite self-lubricating and self-healing materials for various ecologically friendly applications and green tribology.

  12. Analysis and Enhancements of Leader Elections algorithms in Mobile Ad Hoc Networks

    OpenAIRE

    Shayeji, Mohammad H. Al; Al-Azmi, AbdulRahman R.; Al-Azmi, AbdulAziz R.; Samrajesh, M. D.

    2012-01-01

    Mobile Ad Hoc networks (MANET), distinct from traditional distributed systems, are dynamic and self-organizing networks. MANET requires a leader to coordinate and organize tasks. The challenge is to have the right election algorithm that chooses the right leader based on various factors in MANET. In this paper, we analyze four leader election algorithms used in mobile Ad Hoc Networks. Factors considered in our analysis are time complexity, message complexity, assumptions considered, fault tol...

  13. A concept mapping study on organic food consumers in Shanghai, China.

    Science.gov (United States)

    Hasimu, Huliyeti; Marchesini, Sergio; Canavari, Maurizio

    2017-01-01

    Despite some similarities with developed countries, the growth of organic market in China seems to follow a different path. Thus, important questions are how Chinese urban consumers perceive organic food, and what are the main concepts associated to the organic attribute. We aimed at representing in graphic form the network of mental associations with the organic concept. We used an adapted version of the "Brand concept mapping" method to acquire, process, and draw individual concept networks perceived by 50 organic food consumers in Shanghai. We then analyzed the data using network and cluster analysis to create aggregated maps for two distinct groups of consumers. Similarly to their peers in developed countries, Chinese consumers perceive organic food as healthy, safe and expensive. However, organic is not necessarily synonymous with natural produce in China, also due to a translation of the term that conveys the idea of a "technology advanced" product. Organic overlaps with the green food label in terms of image and positioning in the market, since they are easily associated and often confused. The two groups we identified show clear differences in the way the organic concept is associated to other concepts and features. The study provides useful information for practitioners: marketers of organic products in China should invest in communication to emphasize the differences with Green Food products and they should consider the possibility of segmenting organic consumers; Chinese policy makers should consider implementing information campaigns aimed at achieving a better understanding of the features of these quality labels among consumers. For researchers, the study confirms that the BCM method is effective and its integration with network and cluster analysis improves the interpretation of individual and aggregated maps. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Self-organization of physical fields and spin

    International Nuclear Information System (INIS)

    Pestov, I.B.

    2008-01-01

    The subject of the present investigation is the laws of intrinsic self-organization of fundamental physical fields. In the framework of the Theory of Self-Organization the geometrical and physical nature of spin phenomena is uncovered. The key points are spin symmetry (the fundamental realization of the concept of geometrical internal symmetry) and the spinning field (space of defining representation of spin symmetry). It is shown that the essence of spin is the bipolar structure of spin symmetry induced by the gravitational potentials. The bipolar structure provides natural violation of spin symmetry and leads to spinstatics (theory of spinning field outside the time) and spindynamics. The equations of spinstatics and spindynamics are derived. It is shown that Sommerfeld's formula can be derived from the equations of spindynamics and hence the correspondence principle is valid. This means that the Theory of Self-Organization provides the new understanding of spin phenomena

  15. A self-learning algorithm for biased molecular dynamics

    Science.gov (United States)

    Tribello, Gareth A.; Ceriotti, Michele; Parrinello, Michele

    2010-01-01

    A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135

  16. Algorithms for the process management of sealed source brachytherapy

    International Nuclear Information System (INIS)

    Engler, M.J.; Ulin, K.; Sternick, E.S.

    1996-01-01

    Incidents and misadministrations suggest that brachytherapy may benefit form clarification of the quality management program and other mandates of the US Nuclear Regulatory Commission. To that end, flowcharts of step by step subprocesses were developed and formatted with dedicated software. The overall process was similarly organized in a complex flowchart termed a general process map. Procedural and structural indicators associated with each flowchart and map were critiqued and pre-existing documentation was revised. open-quotes Step-regulation tablesclose quotes were created to refer steps and subprocesses to Nuclear Regulatory Commission rules and recommendations in their sequences of applicability. Brachytherapy algorithms were specified as programmable, recursive processes, including therapeutic dose determination and monitoring doses to the public. These algorithms are embodied in flowcharts and step-regulation tables. A general algorithm is suggested as a template form which other facilities may derive tools to facilitate process management of sealed source brachytherapy. 11 refs., 9 figs., 2 tabs

  17. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    Science.gov (United States)

    Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

  18. A self-organized internal models architecture for coding sensory-motor schemes

    Directory of Open Access Journals (Sweden)

    Esaú eEscobar Juárez

    2016-04-01

    Full Text Available Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that textit{simulation}, textit{prediction}, and textit{multi-modal integration} are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity.Research in this direction has brought extensive empirical evidencesuggesting that textit{Internal Models} are suitable mechanisms forsensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints.We propose the Self-Organized Internal ModelsArchitecture (SOIMA, a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory-motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models.We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

  19. An accurate algorithm to calculate the Hurst exponent of self-similar processes

    International Nuclear Information System (INIS)

    Fernández-Martínez, M.; Sánchez-Granero, M.A.; Trinidad Segovia, J.E.; Román-Sánchez, I.M.

    2014-01-01

    In this paper, we introduce a new approach which generalizes the GM2 algorithm (introduced in Sánchez-Granero et al. (2008) [52]) as well as fractal dimension algorithms (FD1, FD2 and FD3) (first appeared in Sánchez-Granero et al. (2012) [51]), providing an accurate algorithm to calculate the Hurst exponent of self-similar processes. We prove that this algorithm performs properly in the case of short time series when fractional Brownian motions and Lévy stable motions are considered. We conclude the paper with a dynamic study of the Hurst exponent evolution in the S and P500 index stocks. - Highlights: • We provide a new approach to properly calculate the Hurst exponent. • This generalizes FD algorithms and GM2, introduced previously by the authors. • This method (FD4) results especially appropriate for short time series. • FD4 may be used in both unifractal and multifractal contexts. • As an empirical application, we show that S and P500 stocks improved their efficiency

  20. An accurate algorithm to calculate the Hurst exponent of self-similar processes

    Energy Technology Data Exchange (ETDEWEB)

    Fernández-Martínez, M., E-mail: fmm124@ual.es [Department of Mathematics, Faculty of Science, Universidad de Almería, 04120 Almería (Spain); Sánchez-Granero, M.A., E-mail: misanche@ual.es [Department of Mathematics, Faculty of Science, Universidad de Almería, 04120 Almería (Spain); Trinidad Segovia, J.E., E-mail: jetrini@ual.es [Department of Accounting and Finance, Faculty of Economics and Business, Universidad de Almería, 04120 Almería (Spain); Román-Sánchez, I.M., E-mail: iroman@ual.es [Department of Accounting and Finance, Faculty of Economics and Business, Universidad de Almería, 04120 Almería (Spain)

    2014-06-27

    In this paper, we introduce a new approach which generalizes the GM2 algorithm (introduced in Sánchez-Granero et al. (2008) [52]) as well as fractal dimension algorithms (FD1, FD2 and FD3) (first appeared in Sánchez-Granero et al. (2012) [51]), providing an accurate algorithm to calculate the Hurst exponent of self-similar processes. We prove that this algorithm performs properly in the case of short time series when fractional Brownian motions and Lévy stable motions are considered. We conclude the paper with a dynamic study of the Hurst exponent evolution in the S and P500 index stocks. - Highlights: • We provide a new approach to properly calculate the Hurst exponent. • This generalizes FD algorithms and GM2, introduced previously by the authors. • This method (FD4) results especially appropriate for short time series. • FD4 may be used in both unifractal and multifractal contexts. • As an empirical application, we show that S and P500 stocks improved their efficiency.