WorldWideScience

Sample records for self-organizing map based

  1. 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

  2. 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.

  3. 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.

  4. Osteoarthritis Severity Determination using Self Organizing Map Based Gabor Kernel

    Science.gov (United States)

    Anifah, L.; Purnomo, M. H.; Mengko, T. L. R.; Purnama, I. K. E.

    2018-02-01

    The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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.

  12. 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.

  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. 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.

  15. 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

  16. 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

  17. 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%.

  18. 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 ...

  19. 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.

  20. 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.

  1. 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

  2. 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 ...

  3. 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)

  4. 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

  5. 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

  6. 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...

  7. 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.

  8. 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.

  9. 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...

  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. 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. 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 ...

  13. 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.

  14. Modeling and Inverse Controller Design for an Unmanned Aerial Vehicle Based on the Self-Organizing Map

    Science.gov (United States)

    Cho, Jeongho; Principe, Jose C.; Erdogmus, Deniz; Motter, Mark A.

    2005-01-01

    The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.

  15. 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.

  16. 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.

  17. 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.

  18. 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...

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  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. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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)

  3. Research on Corporate Social Responsibility of Supply Chain System Based on the Self-organization Theory

    OpenAIRE

    Baoying Wang

    2013-01-01

    In this study, the characteristics of supply chain system are analyzed based on the Self-organization theory from the angle of view of supply chain system. The mathematical models when the system fulfilling social responsibility including self-organization evolution model and self-organization function model are developed to discuss the formation and function of self-organization in supply chain system and coordination. Some basic conditions and tactics about self-organization establishment a...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

    Science.gov (United States)

    Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

    2016-04-01

    Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

  17. 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...

  18. 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)

  19. 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)

  20. 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.

  1. 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)

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  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. 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...

  18. 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.

  19. 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.

  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. 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.

  2. 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)

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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)

  12. 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

  13. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    International Nuclear Information System (INIS)

    Zhou Liming; Zhang Yingyue; Chen Tianlun

    2005-01-01

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  14. 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

  15. 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

  16. 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.

  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. 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

  19. 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.

  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. 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)

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  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. 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.

  11. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  13. 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.

  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. 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

  16. 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

  17. Mechanisms of self-organization and finite size effects in a minimal agent based model

    International Nuclear Information System (INIS)

    Alfi, V; Cristelli, M; Pietronero, L; Zaccaria, A

    2009-01-01

    We present a detailed analysis of the self-organization phenomenon in which the stylized facts originate from finite size effects with respect to the number of agents considered and disappear in the limit of an infinite population. By introducing the possibility that agents can enter or leave the market depending on the behavior of the price, it is possible to show that the system self-organizes in a regime with a finite number of agents which corresponds to the stylized facts. The mechanism for entering or leaving the market is based on the idea that a too stable market is unappealing for traders, while the presence of price movements attracts agents to enter and speculate on the market. We show that this mechanism is also compatible with the idea that agents are scared by a noisy and risky market at shorter timescales. We also show that the mechanism for self-organization is robust with respect to variations of the exit/entry rules and that the attempt to trigger the system to self-organize in a region without stylized facts leads to an unrealistic dynamics. We study the self-organization in a specific agent based model but we believe that the basic ideas should be of general validity

  18. KNOWBOT: a self-organizing interface for nuclear data bases

    International Nuclear Information System (INIS)

    Heger, A.S.; Koen, B.V.

    1988-01-01

    This paper describes the development of a practical intelligent associate, KNOWBOT, designed to act as a surrogate for engineers and scientists in accessing nuclear power industry data bases. The nuclear power industry has developed several safety and reliability data bases to facilitate the exchange of significant safety-related data for enhancement of its performance. The nuclear plant reliability data system and the licensee event reports are instances of such data bases. The creation of these data bases has, nevertheless, been paradoxical. They have provided rapid access to the needed data. Yet they have, at the same time, created their own impediments. Two of these problems that are addressed by the design of KNOWBOT are the interface and dimensionality. The interface problem falls in the broader category of man/machine interaction, which deals with the problems involved with this symbiosis. Dimensionality deals with the rapidly increasing sizes of the data bases and their ability to process queries in a timely fashion. Numerous programs have been developed to address the interface problem, but most have proven inadequate in real-time applications. The dimensionality problem has been approached through the development of faster processors and query optimizers. As these data bases continue to expand, the present solutions are reaching their performance limits, and a new approach such as that offered by KNOWBOT is needed

  19. Analysis of mass incident diffusion in Weibo based on self-organization theory

    Science.gov (United States)

    Pan, Jun; Shen, Huizhang

    2018-02-01

    This study introduces some theories and methods of self-organization system to the research of the diffusion mechanism of mass incidents in Weibo (Chinese Twitter). Based on the analysis on massive Weibo data from Songjiang battery factory incident happened in 2013 and Jiiangsu Qidong OJI PAPER incident happened in 2012, we find out that diffusion system of mass incident in Weibo satisfies Power Law, Zipf's Law, 1/f noise and Self-similarity. It means this system is the self-organization criticality system and dissemination bursts can be understood as one kind of Self-organization behavior. As the consequence, self-organized criticality (SOC) theory can be used to explain the evolution of mass incident diffusion and people may come up with the right strategy to control such kind of diffusion if they can handle the key ingredients of Self-organization well. Such a study is of practical importance which can offer opportunities for policy makers to have good management on these events.

  20. 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.

  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. 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. Actomyosin-based Self-organization of cell internalization during C. elegans gastrulation

    Directory of Open Access Journals (Sweden)

    Pohl Christian

    2012-11-01

    Full Text Available Abstract Background Gastrulation is a key transition in embryogenesis; it requires self-organized cellular coordination, which has to be both robust to allow efficient development and plastic to provide adaptability. Despite the conservation of gastrulation as a key event in Metazoan embryogenesis, the morphogenetic mechanisms of self-organization (how global order or coordination can arise from local interactions are poorly understood. Results We report a modular structure of cell internalization in Caenorhabditis elegans gastrulation that reveals mechanisms of self-organization. Cells that internalize during gastrulation show apical contractile flows, which are correlated with centripetal extensions from surrounding cells. These extensions converge to seal over the internalizing cells in the form of rosettes. This process represents a distinct mode of monolayer remodeling, with gradual extrusion of the internalizing cells and simultaneous tissue closure without an actin purse-string. We further report that this self-organizing module can adapt to severe topological alterations, providing evidence of scalability and plasticity of actomyosin-based patterning. Finally, we show that globally, the surface cell layer undergoes coplanar division to thin out and spread over the internalizing mass, which resembles epiboly. Conclusions The combination of coplanar division-based spreading and recurrent local modules for piecemeal internalization constitutes a system-level solution of gradual volume rearrangement under spatial constraint. Our results suggest that the mode of C. elegans gastrulation can be unified with the general notions of monolayer remodeling and with distinct cellular mechanisms of actomyosin-based morphogenesis.

  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. 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)

  6. 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.

  7. A distance weighted-based approach for self-organized aggregation in robot swarms

    KAUST Repository

    Khaldi, Belkacem

    2017-12-14

    In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.

  8. 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.

  9. 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.

  10. Sugar-Based Polyamides: Self-Organization in Strong Polar Organic Solvents.

    Science.gov (United States)

    Rosu, Cornelia; Russo, Paul S; Daly, William H; Cueto, Rafael; Pople, John A; Laine, Roger A; Negulescu, Ioan I

    2015-09-14

    Periodic patterns resembling spirals were observed to form spontaneously upon unassisted cooling of d-glucaric acid- and d-galactaric acid-based polyamide solutions in N-methyl-N-morpholine oxide (NMMO) monohydrate. Similar observations were made in d-galactaric acid-based polyamide/ionic liquid (IL) solutions. The morphologies were investigated by optical, polarized light and confocal microscopy assays to reveal pattern details. Differential scanning calorimetry was used to monitor solution thermal behavior. Small- and wide-angle X-ray scattering data reflected the complex and heterogeneous nature of the self-organized patterns. Factors such as concentration and temperature were found to influence spiral dimensions and geometry. The distance between rings followed a first-order exponential decay as a function of polymer concentration. Fourier-Transform Infrared Microspectroscopy analysis of spirals pointed to H-bonding between the solvent and the pendant hydroxyl groups of the glucose units from the polymer backbone. Tests on self-organization into spirals of ketal-protected d-galactaric acid polyamides in NMMO monohydrate confirmed the importance of the monosaccharide's pendant free hydroxyl groups on the formation of these patterns. Rheology performed on d-galactaric-based polyamides at high concentration in NMMO monohydrate solution revealed the optimum conditions necessary to process these materials as fibers by spinning. The self-organization of these sugar-based polyamides mimics certain biological materials.

  11. 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.

  12. 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.

  13. 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.

    Directory of Open Access Journals (Sweden)

    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

  14. A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality

    Science.gov (United States)

    Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.

    2013-09-01

    flaring events with scaling indices in good agreement with observations. We therefore conclude that well-known statistical properties of flares are reproduced after active regions reach self-organized criticality. The significant enhancement in both the static and the dynamic integrated flare models is that they initiate the simulation from observations, thus facilitating energy calculation in physical units. Especially in the dynamic version of the model, the driving of the system is based on observed, evolving vector magnetograms, allowing for the separation between MHD and kinetic timescales through the assignment of distinct MHD timestamps to each interpolation step.

  15. 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.

  16. 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.

  17. 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.

  18. 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)

  19. 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...

  20. 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.

  1. 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

  2. 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...

  3. Leader-based and self-organized communication: modelling group-mass recruitment in ants.

    Science.gov (United States)

    Collignon, Bertrand; Deneubourg, Jean Louis; Detrain, Claire

    2012-11-21

    For collective decisions to be made, the information acquired by experienced individuals about resources' location has to be shared with naïve individuals through recruitment. Here, we investigate the properties of collective responses arising from a leader-based recruitment and a self-organized communication by chemical trails. We develop a generalized model based on biological data drawn from Tetramorium caespitum ant species of which collective foraging relies on the coupling of group leading and trail recruitment. We show that for leader-based recruitment, small groups of recruits have to be guided in a very efficient way to allow a collective exploitation of food while large group requires less attention from their leader. In the case of self-organized recruitment through a chemical trail, a critical value of trail amount has to be laid per forager in order to launch collective food exploitation. Thereafter, ants can maintain collective foraging by emitting signal intensity below this threshold. Finally, we demonstrate how the coupling of both recruitment mechanisms may benefit to collectively foraging species. These theoretical results are then compared with experimental data from recruitment by T. caespitum ant colonies performing group-mass recruitment towards a single food source. We evidence the key role of leaders as initiators and catalysts of recruitment before this leader-based process is overtaken by self-organised communication through trails. This model brings new insights as well as a theoretical background to empirical studies about cooperative foraging in group-living species. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Music Mood Player Implementation Applied in Daycare Using Self Organizing Map Method

    OpenAIRE

    Dewi, Kadek Cahya; Putri, Luh Arida Ayu Rahning

    2011-01-01

    . Music is an art, entertainment and human activity that involve some organized sounds. Music is closely related to human psychology. A piece of music often associated with certain adjectives such as happy, sad, romantic and many more. The linkage between the music with a certain mood has been widely used in various occasions by people, there for music classification based on relevance to a particular emotion is important. Daycare is one example of an institution that used music as therapy or...

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. Frequency Representation: Visualization and Clustering of Acoustic Data Using Self-Organizing Maps.

    Science.gov (United States)

    Guo, Xinhua; Sun, Song; Yu, Xiantao; Wang, Pan; Nakamura, Kentaro

    2017-11-01

    Extraction and display of frequency information in three-dimensional (3D) acoustic data are important steps to analyze object characteristics, because the characteristics, such as profiles, sizes, surface structures, and material properties, may show frequency dependence. In this study, frequency representation (FR) based on phase information in multispectral acoustic imaging (MSAI) is proposed to overcome the limit of intensity or amplitude information in image display. Experiments are performed on 3D acoustic data collected from a rigid surface engraved with five different letters. The results show that the proposed FR technique can not only identify the depth of the five letters by the colors representing frequency characteristics but also demonstrate the 3D image of the five letters, providing more detailed characteristics that are unavailable by conventional acoustic imaging.

  13. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  14. Globalization and Self-Organization in the Knowledge-Based Society

    Directory of Open Access Journals (Sweden)

    Christian Fuchs

    2008-07-01

    Full Text Available In this paper I suggest that a theory of self-organization can be used as a consistent background theory for explaining the dynamics and logics of globalization. Globalization is not confined to the human realm, it is an attribute of all complex, self-organizing systems. Globalization in a synchronous sense means a micro-macro-link where bottom-up-emergence of new qualities in the self-reproduction of complex systems takes place, it is accompanied by a macro-micro-link of top-down-localization. A dynamic interaction between a global and a local level (glocalization results in the permanent overall self-reproduction of the system. Globalization in a diachronic sense means the emergence of a new, higher level of self-organization during a phase of instability and heavy fluctuations by order through fluctuation. Globalization is shaped by a dialectic of change and continuity: in the hierarchy that stems from emergent evolution there are both general aspects of globalization and aspects that are specific for each organizational level. Applying this general notion of globalization to society means that human globalization is both a general process that can be found in all societies and a specific process with emergent qualities in concrete phases of societal development. Globalization processes in modern society are based on structural antagonisms that result in uneven developments in the technosphere, the ecosphere, the economy, polity, and culture. The transition to Postfordist, informational capitalism has been a consequence of the development of the structural antagonisms of Fordism and has been accompanied by a new phase of globalization that has transformed the subsystems of society and has resulted in new antagonism that are an expression of general antagonisms that shape modern societies. Hence we find antagonistic tendencies of contemporary globalization in all subsystems of society that result in both risks and opportunities. Human beings

  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. 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.

  18. Changes in balance coordination and transfer to an unlearned balance task after slackline training: a self-organizing map analysis.

    Science.gov (United States)

    Serrien, Ben; Hohenauer, Erich; Clijsen, Ron; Taube, Wolfgang; Baeyens, Jean-Pierre; Küng, Ursula

    2017-11-01

    How humans maintain balance and change postural control due to age, injury, immobility or training is one of the basic questions in motor control. One of the problems in understanding postural control is the large set of degrees of freedom in the human motor system. Therefore, a self-organizing map (SOM), a type of artificial neural network, was used in the present study to extract and visualize information about high-dimensional balance strategies before and after a 6-week slackline training intervention. Thirteen subjects performed a flamingo and slackline balance task before and after the training while full body kinematics were measured. Range of motion, velocity and frequency of the center of mass and joint angles from the pelvis, trunk and lower leg (45 variables) were calculated and subsequently analyzed with an SOM. Subjects increased their standing time significantly on the flamingo (average +2.93 s, Cohen's d = 1.04) and slackline (+9.55 s, d = 3.28) tasks, but the effect size was more than three times larger in the slackline. The SOM analysis, followed by a k-means clustering and marginal homogeneity test, showed that the balance coordination pattern was significantly different between pre- and post-test for the slackline task only (χ 2  = 82.247; p balance coordination on the slackline could be characterized by an increase in range of motion and a decrease in velocity and frequency in nearly all degrees of freedom simultaneously. The observation of low transfer of coordination strategies to the flamingo task adds further evidence for the task-specificity principle of balance training, meaning that slackline training alone will be insufficient to increase postural control in other challenging situations.

  19. 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.

  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. 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.

  2. 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.

  3. Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding.

    Science.gov (United States)

    Carreón, Gustavo; Gershenson, Carlos; Pineda, Luis A

    2017-01-01

    The equal headway instability-the fact that a configuration with regular time intervals between vehicles tends to be volatile-is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system's data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger's inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur. The correlation between the simulation of the current model and empirical observations carried out in the Mexico City Metro provides a base to calculate the expected performance of the self-organizing method in case it is implemented in the real system. We also performed a pilot study at the Balderas station to regulate the alighting and boarding of passengers through guide signs on platforms. The analysis of empirical data shows a delay reduction of the waiting time of trains at stations. Finally, we provide recommendations to improve public transportation systems.

  4. 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.

  5. Methodical approaches to providing sustainable development of the transport industry management system based on self-organization

    Science.gov (United States)

    Belyantseva, Oksana; Panenkov, Andrey; Safonova, Nataliya

    2017-10-01

    Current conditions of the cognitive economy formation demand to take into account the leading role of information, knowledge and human capital in the development of the transport industry management system. The article substantiates the conceptual approach to the self-organization of a management system on the basis of innovative changes. Human capital is the key aspect of self-organization, so the directions of improving the workforce quality are justified. Basing on the information-innovative genesis of the process of self-organization, the authors justified the necessity of preventing asymmetric information. For this pupose the actions against the resistance to innovations were proposed. The implementation of certain measures contributes to the effective development of the transport management system.

  6. 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.

  7. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. 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.

  9. Self-organization in multilayer network with adaptation mechanisms based on competition

    Science.gov (United States)

    Pitsik, Elena N.; Makarov, Vladimir V.; Nedaivozov, Vladimir O.; Kirsanov, Daniil V.; Goremyko, Mikhail V.

    2018-04-01

    The paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it's elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical selflearning based on homeostasis and homophily, as well as the result of the competition between the network's layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. This phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.

  10. Using Self-Organizing Map (SOM) Clusters of Ozonesonde Profiles to Evaluate Climatologies and Create Linkages between Meteorology and Pollution

    Science.gov (United States)

    Stauffer, R. M.; Thompson, A. M.; Young, G. S.; Oltmans, S. J.; Johnson, B.

    2016-12-01

    Ozone (O3) climatologies are typically created by averaging ozonesonde profiles on a monthly or seasonal basis, either for specific regions or zonally. We demonstrate the advantages of using a statistical clustering technique, self-organizing maps (SOM), over this simple averaging, through analysis of more than 4500 sonde profiles taken from the long-term US sites at Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA. First, we apply SOM to O3 mixing ratios from surface to 12 km amsl. At all four sites, profiles in SOM clusters exhibit similar tropopause height, 500 hPa height and temperature, and total and tropospheric column O3. Second, when profiles from each SOM cluster are compared to monthly O3 means, near-tropopause O3 in three of the clusters is double (over +100 ppbv) the climatological O3 mixing ratio. The three clusters include 13-16% of all profiles, mostly from winter and spring. Large mid-tropospheric deviations from monthly means are found in two highly-populated clusters that represent either distinctly polluted (summer) or clean O3 (fall-winter, high tropopause) profiles. Thus, SOM indeed appear to represent US O3 profile statistics better than conventional climatologies. In the case of Trinidad Head, SOM clusters of O3 profile data from the lower troposphere (surface-6 km amsl) can discriminate background vs polluted O3 and the meteorology associated with each. Two of nine O3 clusters exhibit thin layers ( 100s of m thick) of high O3, typically between 1 and 4 km. Comparisons between clusters and downwind, high-altitude surface O3 measurements display a marked impact of the elevated tropospheric O­­3. Days corresponding to the high O3 clusters exhibit hourly surface O3 anomalies at surface sites of +5 -10 ppbv compared to a climatology; the anomalies can last up to four days. We also explore applications of SOM to tropical ozonesonde profiles, where tropospheric O3 variability is generally smaller.

  11. Using Self Organizing Maps to evaluate the NASA GISS AR5 SCM at the ARM SGP Site

    Science.gov (United States)

    Dong, X.; Kennedy, A. D.; Xi, B.

    2010-12-01

    Cluster analyses have gained popularity in recent years to establish cloud regimes using satellite and radar cloud data. These regimes can then be used to evaluate climate models or to determine what large-scale or subgrid processes are responsible for cloud formation. An alternative approach is to first classify the meteorological regimes (i.e. synoptic pattern and forcing) and then determine what cloud scenes occur. In this study, a competitive neural network known as the Self Organizing Map (SOM) is used to classify synoptic patterns over the Southern Great Plains (SGP) region to evaluate simulated clouds from the AR5 version of the NASA GISS Model E Single Column Model (SCM). In detail, 54-class SOMs have been developed using North American Regional Reanalysis (NARR) variables averaged to 2x2.5 degree latitude longitude grid boxes for a region of 7x7 grid boxes centered on the ARM SGP site. Variables input into the SOM include mean sea-level pressure and the horizontal wind components, relative humidity, and geopotential height at the 900, 500, and 300 hPa levels. These SOMs are produced for the winter (DJF), spring (MAM), summer (JJA), and fall (SON) seasons during 1999-2001. This synoptic typing will be associated with observed cloud fractions and forcing properties from the ARM SGP site and then used to evaluate simulated clouds from the SCM. SOMs provide a visually intuitive way to understand their classifications because classes are related to each other in a two-dimensional space. In Fig. 1 for example, the reader can easily see for a 54 class SOM during the winter season, classes with higher 300 hPa mean relative humidities are clustered near each other. This allows for the user to identify that there appears to be a relationship between mean 300 hPa RH and high cloud fraction as observed by the ARM SGP site. Figure 1. Mean high cloud fraction (top panel) and 300 hPa Relative Humidity (bottom panel) for a 9x6 (54 class) SOM during the winter (DJF) season

  12. Self-organizing maps applied to two-phase flow on natural circulation loop study; Aplicacao de mapas auto-organizaveis na classificacao de padroes de escoamento bifasico

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Leonardo Ferreira

    2016-11-01

    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.

  13. 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.

  14. Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

    Science.gov (United States)

    Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

    1996-04-01

    The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

  15. A simple rank-based Markov chain with self-organized criticality

    Czech Academy of Sciences Publication Activity Database

    Swart, Jan M.

    2017-01-01

    Roč. 23, č. 1 (2017), s. 87-102 ISSN 1024-2953 R&D Projects: GA ČR GAP201/12/2613; GA ČR(CZ) GA15-08819S Institutional support: RVO:67985556 Keywords : self-reinforcement * self-organized criticality * canyon Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.397, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/swart-0476009.pdf

  16. 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.

  17. Dynamic data-driven integrated flare model based on self-organized criticality

    Science.gov (United States)

    Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M. K.

    2013-05-01

    Context. We interpret solar flares as events originating in active regions that have reached the self-organized critical state. We describe them with a dynamic integrated flare model whose initial conditions and driving mechanism are derived from observations. Aims: We investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. Methods: To investigate whether the distribution functions of total energy, peak energy, and event duration follow the expected scaling laws, we first applied the previously reported static cellular automaton model to a time series of seven solar vector magnetograms of the NOAA active region 8210 recorded by the Imaging Vector Magnetograph on May 1 1998 between 18:59 UT and 23:16 UT until the self-organized critical state was reached. We then evolved the magnetic field between these processed snapshots through spline interpolation, mimicking a natural driver in our dynamic model. We identified magnetic discontinuities that exceeded a threshold in the Laplacian of the magnetic field after each interpolation step. These discontinuities were relaxed in local diffusion events, implemented in the form of cellular automaton evolution rules. Subsequent interpolation and relaxation steps covered all transitions until the end of the processed magnetograms' sequence. We additionally advanced each magnetic configuration that has reached the self-organized critical state (SOC configuration) by the static model until 50 more flares were triggered, applied the dynamic model again to the new sequence, and repeated the same process sufficiently often to generate adequate statistics. Physical requirements, such as the divergence-free condition for the magnetic field, were approximately imposed. Results: We obtain robust power laws in the distribution functions of the modeled flaring events with scaling indices that agree well

  18. 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)

  19. 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 

  20. 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.

  1. Field-effect transistors based on self-organized molecular nanostripes

    DEFF Research Database (Denmark)

    Cavallini, M.; Stoliare, P.; Moulin, J.-F.

    2005-01-01

    Charge transport properties in organic semiconductors depend strongly on molecular order. Here we demonstrate field-effect transistors where drain current flows through a precisely defined array of nanostripes made of crystalline and highly ordered molecules. The molecular stripes are fabricated ...... by the menisci once the critical concentration is reached and self-organizes into molecularly ordered stripes 100-200 nm wide and a few monolayers high. The charge mobility measured along the stripes is 2 orders of magnitude larger than the values measured for spin-coated thin films....... across the channel of the transistor by a stamp-assisted deposition of the molecular semiconductors from a solution. As the solvent evaporates, the capillary forces drive the solution to form menisci under the stamp protrusions. The solute precipitates only in the regions where the solution is confined...

  2. Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family.

    Science.gov (United States)

    Scotti, Marcus T; Emerenciano, Vicente; Ferreira, Marcelo J P; Scotti, Luciana; Stefani, Ricardo; da Silva, Marcelo S; Mendonça Junior, Francisco Jaime B

    2012-04-20

    The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

  3. ORGANIZATIONAL AND METHODICAL BASES OF MULTICULTURAL SELF-ORGANIZATION OF STUDENTS IN THE ADDITIONAL FOREIGN LANGUAGE EDUCATION

    Directory of Open Access Journals (Sweden)

    V N Kartashova

    2016-12-01

    Full Text Available The article deals with the organizational and methodical bases of multicultural self-organization of the linguistic personality of the student in terms of additional foreign language education. According to the authors’ opinion, the methodological basis is a dialogue of cultures as a philosophy of mutual understanding, mutual relations in today’s global environment. Changing the socio-cultural context of foreign language education paradigm allows you to select multicultural (teaching a foreign language on the principle of “native culture - the culture of the foreign language speaking country - the culture of the world” and active (the development and functioning of a person in the normal course of his activities, where the starting point is the introduction of man to the world of culture and his self-development approach as a priority. The contents of additional education is oriented towards the disclosure and a possible solution of specific problems in the educational practice, and based on the principles of humanism and diversification.The article analyzes the individual programs of additional education and professional training in the field of foreign languages, developed by universities in recent decades. The authors present the experience of the scientific and methodological development and implementation at the department of foreign languages and teaching methods of Bunin Yelets State University of the additional foreign language education system that provides the multicultural self-organization of the student’s linguistic personality. The description of the program of professional retraining “The multicultural self-organization expert in the field of business, management and tourism (English”, designed for the students of non-language training areas, is presented, too. The positive dynamics of mastering foreign languages, the development of global attitude of the linguistic personality to the culture and his/her cultural

  4. 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.

  5. 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)

  6. 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.

  7. 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.

  8. 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)

  9. 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.

  10. 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...

  11. 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)

  12. Information-based self-organization of sensor nodes of a sensor network

    Science.gov (United States)

    Ko, Teresa H [Castro Valley, CA; Berry, Nina M [Tracy, CA

    2011-09-20

    A sensor node detects a plurality of information-based events. The sensor node determines whether at least one other sensor node is an information neighbor of the sensor node based on at least a portion of the plurality of information-based events. The information neighbor has an overlapping field of view with the sensor node. The sensor node sends at least one communication to the at least one other sensor node that is an information neighbor of the sensor node in response to at least one information-based event of the plurality of information-based events.

  13. Beyond Academic Tracking: Using Cluster Analysis and Self-Organizing Maps to Investigate Secondary Students' Chemistry Self-Concept

    Science.gov (United States)

    Nielsen, Sara E.; Yezierski, Ellen J.

    2016-01-01

    Academic tracking, placing students in different classes based on past performance, is a common feature of the American secondary school system. A longitudinal study of secondary students' chemistry self-concept scores was conducted, and one feature of the study was the presence of academic tracking. Though academic tracking is one way to group…

  14. Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part II: interannual time scale

    Energy Technology Data Exchange (ETDEWEB)

    Gueye, A.K. [ESP, UCAD, Dakar (Senegal); Janicot, Serge; Sultan, Benjamin [LOCEAN/IPSL, IRD, Universite Pierre et Marie Curie, Paris cedex 05 (France); Niang, A. [LTI, ESP/UCAD, Dakar (Senegal); Sawadogo, S. [LTI, EPT, Thies (Senegal); Diongue-Niang, A. [ANACIM, Dakar (Senegal); Thiria, S. [LOCEAN/IPSL, UPMC, Paris (France)

    2012-11-15

    The aim of this work is to define over the period 1979-2002 the main synoptic weather regimes relevant for understanding the daily variability of rainfall during the summer monsoon season over Senegal. ''Interannual'' synoptic weather regimes are defined by removing the influence of the mean 1979-2002 seasonal cycle. This is different from Part I where the seasonal evolution of each year was removed, then removing also the contribution of interannual variability. As in Part I, the self-organizing maps approach, a clustering methodology based on non-linear artificial neural network, is combined with a hierarchical ascendant classification to compute these regimes. Nine weather regimes are identified using the mean sea level pressure and 850 hPa wind field as variables. The composite circulation patterns of all these nine weather regimes are very consistent with the associated anomaly patterns of precipitable water, mid-troposphere vertical velocity and rainfall. They are also consistent with the distribution of rainfall extremes. These regimes have been then gathered into different groups. A first group of four regimes is included in an inner circuit and is characterized by a modulation of the semi-permanent trough located along the western coast of West Africa and an opposite modulation on the east. This circuit is important because it associates the two wettest and highly persistent weather regimes over Senegal with the driest and the most persistent one. One derivation of this circuit is highlighted, including the two driest regimes and the most persistent one, what can provide important dry sequences occurrence. An exit of this circuit is characterised by a filling of the Saharan heat low. An entry into the main circuit includes a southward location of the Saharan heat low followed by its deepening. The last weather regime is isolated from the other ones and it has no significant impact on Senegal. It is present in June and September, and

  15. Self-organization of a tetrasubstituted tetrathiafulvalene (TTF) in a silica based hybrid organic-inorganic material.

    Science.gov (United States)

    Cerveau, Geneviève; Corriu, Robert J P; Lerouge, Frédéric; Bellec, Nathalie; Lorcy, Dominique; Nobili, Maurizio

    2004-02-21

    A hybrid organic inorganic nanostructured material containing a TTF core substituted by four arms exhibited a high level of both condensation at silicon (96%) and self-organization as evidenced by X-ray diffraction and an unprecedented birefringent behaviour.

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. 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)

  1. 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.

  2. 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.

  3. A Novel Real-Time Coal Miner Localization and Tracking System Based on Self-Organized Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wang Yang

    2010-01-01

    Full Text Available With the development of information technology, we envision that the key of improving coal mine safety is how to get real-time positions of miners. In this paper, we propose a prototype system for real-time coal miner localization and tracking based on self-organized sensor networks. The system is composed of hardware and software platform. We develop a set of localization hardware devices with the Safety Certificate of Approval for Mining Products include miner node, wired fixed access station, and base with optical port. On the software side, we develop a layered software architecture of node application, server management, and information dissemination and broadcasting. We also develop three key localization technologies: an underground localization algorithm using received signal strength indication- (RSSI- verifying algorithm to reduce the influence of the severe environment in a coal mine; a robust fault-tolerant localization mechanism to improve the inherent defect of instability of RSSI localization; an accurate localization algorithm based on Monte Carlo localization (MCL to adapt to the underground tunnel structure. In addition, we conduct an experimental evaluation based on a real prototype implementation using MICA2 motes. The results show that our system is more accurate and more adaptive in general than traditional localization algorithms.

  4. A distance weighted-based approach for self-organized aggregation in robot swarms

    KAUST Repository

    Khaldi, Belkacem; Harrou, Fouzi; Cherif, Foudil; Sun, Ying

    2017-01-01

    topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra

  5. Ultra Innovative Approach to Integrate Cellphone Customer Market Segmentation Model Using Self Organizing Maps and K-Means Methodology

    Directory of Open Access Journals (Sweden)

    mohammad reza karimi alavijeh

    2016-07-01

    Full Text Available The utilization of 3G and 4G is rapidly increasing and also cellphone users are briskly changing their consumption behavior, using preferences and shopping manner. Accordingly, cellphone manufacturers should create an accurate insight of their target market and provide a “special offer” to their target consumers. In order to reach a correct understanding of the target market, consumption behavior and lifestyle of the submarkets we found the appropriate number of community clusters after criticizing the traditional methods and introducing market segmentation techniques which were based on neural networks. By utilizing the fuzzy Delphi technique, variables of target market segmentation were found. Finally, the obtained clusters and segmentations of the market were refined by using the techniques of K-means and aggregation (Agglomerative. The population of this research included the consumers of mobile in Tehran with a sample of 130 specimens after collecting data through questionnaires, results demonstrated that the Tehran cellphone market was comprised of 5 Clusters, each one are capable of implementing marketing strategy and marketing mix separately with taking into account the competitive advantages of ICT companies to maximize their demand and margin.

  6. Prediction based Greedy Perimeter Stateless Routing Protocol for Vehicular Self-organizing Network

    Science.gov (United States)

    Wang, Chunlin; Fan, Quanrun; Chen, Xiaolin; Xu, Wanjin

    2018-03-01

    PGPSR (Prediction based Greedy Perimeter Stateless Routing) is based on and extended the GPSR protocol to adapt to the high speed mobility of the vehicle auto organization network (VANET) and the changes in the network topology. GPSR is used in the VANET network environment, the network loss rate and throughput are not ideal, even cannot work. Aiming at the problems of the GPSR, the proposed PGPSR routing protocol, it redefines the hello and query packet structure, in the structure of the new node speed and direction information, which received the next update before you can take advantage of its speed and direction to predict the position of node and new network topology, select the right the next hop routing and path. Secondly, the update of the outdated node information of the neighbor’s table is deleted in time. The simulation experiment shows the performance of PGPSR is better than that of GPSR.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. Social self-organization agent-based simulations and experiments to study emergent social behavior

    CERN Document Server

    2012-01-01

    What are the principles that keep our society together? This question is even more difficult to answer than the long-standing question, what are the forces that keep our world together. However, the social challenges of humanity in the 21st century ranging from the financial crises to the impacts of globalization, require us to make fast progress in our understanding of how society works, and how our future can be managed in a resilient and sustainable way. This book can present only a few very first steps towards this ambitious goal. However, based on simple models of social interactions, one can already gain some surprising insights into the social, ``macro-level'' outcomes and dynamics that is implied by individual, ``micro-level'' interactions. Depending on the nature of these interactions, they may imply the spontaneous formation of social conventions or the birth of social cooperation, but also their sudden breakdown. This can end in deadly crowd disasters or tragedies of the commons (such as financial ...

  12. A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network

    Science.gov (United States)

    Han, Zhao; Wu, Jie; Zhang, Jie; Liu, Liefeng; Tian, Kaiyun

    2014-04-01

    Wireless sensor network (WSN) is a system composed of a large number of low-cost micro-sensors. This network is used to collect and send various kinds of messages to a base station (BS). WSN consists of low-cost nodes with limited battery power, and the battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long-life work time. To achieve the aim, we need not only to minimize total energy consumption but also to balance WSN load. Researchers have proposed many protocols such as LEACH, HEED, PEGASIS, TBC and PEDAP. In this paper, we propose a General Self-Organized Tree-Based Energy-Balance routing protocol (GSTEB) which builds a routing tree using a process where, for each round, BS assigns a root node and broadcasts this selection to all sensor nodes. Subsequently, each node selects its parent by considering only itself and its neighbors' information, thus making GSTEB a dynamic protocol. Simulation results show that GSTEB has a better performance than other protocols in balancing energy consumption, thus prolonging the lifetime of WSN.

  13. Highly Sensitive Flexible Pressure Sensors Based on Printed Organic Transistors with Centro-Apically Self-Organized Organic Semiconductor Microstructures.

    Science.gov (United States)

    Yeo, So Young; Park, Sangsik; Yi, Yeon Jin; Kim, Do Hwan; Lim, Jung Ah

    2017-12-13

    A highly sensitive pressure sensor based on printed organic transistors with three-dimensionally self-organized organic semiconductor microstructures (3D OSCs) was demonstrated. A unique organic transistor with semiconductor channels positioned at the highest summit of printed cylindrical microstructures was achieved simply by printing an organic semiconductor and polymer blend on the plastic substrate without the use of additional etching or replication processes. A combination of the printed organic semiconductor microstructure and an elastomeric top-gate dielectric resulted in a highly sensitive organic field-effect transistor (FET) pressure sensor with a high pressure sensitivity of 1.07 kPa -1 and a rapid response time of <20 ms with a high reliability over 1000 cycles. The flexibility and high performance of the 3D OSC FET pressure sensor were exploited in the successful application of our sensors to real-time monitoring of the radial artery pulse, which is useful for healthcare monitoring, and to touch sensing in the e-skin of a realistic prosthetic hand.

  14. Diffusion Based Photon Mapping

    DEFF Research Database (Denmark)

    Schjøth, Lars; Fogh Olsen, Ole; Sporring, Jon

    2007-01-01

    . To address this problem we introduce a novel photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the structure of the photon map such that smoothing occurs along edges and structures and not across. In this way we preserve the important illumination features......, while eliminating noise. We call our method diffusion based photon mapping....

  15. Diffusion Based Photon Mapping

    DEFF Research Database (Denmark)

    Schjøth, Lars; Olsen, Ole Fogh; Sporring, Jon

    2006-01-01

    . To address this problem we introduce a novel photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the structure of the photon map such that smoothing occurs along edges and structures and not across. In this way we preserve the important illumination features......, while eliminating noise. We call our method diffusion based photon mapping....

  16. How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map.

    Directory of Open Access Journals (Sweden)

    Stephen Grossberg

    Full Text Available Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs, both increase along this axis. Slower (faster subthreshold MPOs and slower (faster EPSPs correlate with larger (smaller grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic "neural relativity" that

  17. Physical processes in thin-film electroluminescent structures based on ZnS:Mn showing self-organized patterns

    International Nuclear Information System (INIS)

    Zuccaro, S.; Raker, Th.; Niedernostheide, F.-J.; Kuhn, T.; Purwins, H.-G.

    2003-01-01

    Physical processes in thin ZnS:Mn films and their relation to the formation of dynamical patterns in the electroluminescence of AC driven films are investigated. The technique of photo-depolarization-spectroscopy is used to investigate defect states in these films and it is shown that specific features in the spectra correlate with the observed self-organized patterns. Furthermore, the time dependence of the dissipative current is measured at the same samples and compared with current waveforms obtained from numerical simulations of a drift-diffusion model. The results are used to discuss the origin of the self-organized processes in ZnS:Mn-films

  18. 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

  19. 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...

  20. 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

  1. Diffusion Based Photon Mapping

    DEFF Research Database (Denmark)

    Schjøth, Lars; Sporring, Jon; Fogh Olsen, Ole

    2008-01-01

    . To address this problem, we introduce a photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the structure of the photon map such that smoothing occurs along edges and structures and not across. In this way, we preserve important illumination features, while...

  2. Comparative Visual Analysis of Large Customer Feedback Based on Self-Organizing Sentiment Maps

    OpenAIRE

    Janetzko, Halldór; Jäckle, Dominik; Schreck, Tobias

    2013-01-01

    Textual customer feedback data, e.g., received by surveys or incoming customer email notifications, can be a rich source of information with many applications in Customer Relationship Management (CRM). Nevertheless, to date this valuable source of information is often neglected in practice, as service managers would have to read manually through potentially large amounts of feedback text documents to extract actionable information. As in many cases, a purely manual approach is not feasible, w...

  3. Bio inspired self-organizing map based architecture for improved capture of data sequence

    OpenAIRE

    Wickramasinghe, Manjusri Ishwara Ellepola

    2017-01-01

    The advancement of ICTs has enabled higher prevalence of sequential data generated by various fields of study such as engineering, biology and finances. The availability of such sequential data has generated significant interest in both the research community and businesses to develop models and techniques to capture,analyze and learn from sequential data. Although models generated from sequential information could provide a plethora of useful information that are not visible in non-...

  4. The self-organization and functional activity of binary system based on erucyl amidopropyl betaine - alkylated polyethyleneimine

    Science.gov (United States)

    Gaynanova, Gulnara A.; Valiakhmetova, Alsu R.; Kuryashov, Dmitry A.; Kudryashova, Yuliana R.; Lukashenko, Svetlana S.; Syakaev, Victor V.; Latypov, Shamil K.; Bukharov, Sergey V.; Bashkirtseva, Natalia Yu.; Zakharova, Lucia Ya.

    2013-11-01

    The self-organization in individual and binary aqueous solutions of a zwitterionic surfactant erucyl amidopropyl betaine and alkylated polyethyleneimine is carried out with a wide range of physical and chemical methods, including tensiometry, conductometry, dynamic light scattering, pH-metry, spectrophotometry, and fluorescence spectroscopy. The data obtained strongly support the formation of nanosized aggregates in the systems and provide information on their structure and probable morphological transitions. High solubilization capacity and data on the contact angle showed a possibility of the application of these systems as nanocontainers or oil wetting agents in the oil recovery.

  5. Theoretical approach on microscopic bases of stochastic functional self-organization: quantitative measures of the organizational degree of the environment

    Energy Technology Data Exchange (ETDEWEB)

    Oprisan, Sorinel Adrian [Department of Psychology, University of New Orleans, New Orleans, LA (United States)]. E-mail: soprisan@uno.edu

    2001-11-30

    There has been increased theoretical and experimental research interest in autonomous mobile robots exhibiting cooperative behaviour. This paper provides consistent quantitative measures of organizational degree of a two-dimensional environment. We proved, by the way of numerical simulations, that the theoretically derived values of the feature are reliable measures of aggregation degree. The slope of the feature's dependence on memory radius leads to an optimization criterion for stochastic functional self-organization. We also described the intellectual heritages that have guided our research, as well as possible future developments. (author)

  6. 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,…

  7. BaseMap

    Data.gov (United States)

    California Natural Resource Agency — The goal of this project is to provide a convenient base map that can be used as a starting point for CA projects. It's simple, but designed to work at a number of...

  8. Spot profile analysis and lifetime mapping in ultrafast electron diffraction: Lattice excitation of self-organized Ge nanostructures on Si(001

    Directory of Open Access Journals (Sweden)

    T. Frigge

    2015-05-01

    Full Text Available Ultrafast high energy electron diffraction in reflection geometry is employed to study the structural dynamics of self-organized Germanium hut-, dome-, and relaxed clusters on Si(001 upon femtosecond laser excitation. Utilizing the difference in size and strain state the response of hut- and dome clusters can be distinguished by a transient spot profile analysis. Surface diffraction from {105}-type facets provide exclusive information on hut clusters. A pixel-by-pixel analysis of the dynamics of the entire diffraction pattern gives time constants of 40, 160, and 390 ps, which are assigned to the cooling time constants for hut-, dome-, and relaxed clusters.

  9. Тhe elite and the base for the „otherwise possible” matrix in the self - organization process

    Directory of Open Access Journals (Sweden)

    Roman BOGACIOV

    2017-03-01

    Full Text Available New basic social relations in the format of dialogue and cooperation are the basis of subject- to-subject relationship. Their polyphony in the field of „live communication” becomes the basis for developing new approaches and mechanisms for self-organizing social systems, the social development and the reproduction in general. The proposed methodological framework of the Matrix of „Otherwise Possible” is the basis for the social development and relevant social system management tools. The theoretical elaboration and practical implementation of the proposed approach to the development strategy of self-social system determines further directions of the scientific exploration – the mechanisms and practices towards self-harmonization of the nature and the form of the process of social development.

  10. 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

  11. Patterns identification in supervisory systems of nuclear reactors installations and gas pipelines systems using self-organizing maps; Identificacao de padroes em sistemas supervisorios de instalacoes de reatores nucleares e em sistemas de gasodutos utilizando mapas auto-organizaveis

    Energy Technology Data Exchange (ETDEWEB)

    Doraskevicius Junior, Waldemar

    2005-07-01

    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)

  12. 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

  13. Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach

    Science.gov (United States)

    Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

    This paper introduces the implementation of a computational agent-based financial market model in which the system is described on both microscopic and macroscopic levels. This artificial financial market model is used to study the system response when a shock occurs. Indeed, when a market experiences perturbations, financial systems behavior can exhibit two different properties: resilience and robustness. Through simulations and different scenarios of market shocks, these system properties are studied. The results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption of the system self-organization. Numerical simulations highlight that the market can absorb strong mono-shocks but can also be led to rupture by low but repeated perturbations.

  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. 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...

  16. 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)

  17. Enabling Self-Organization in Embedded Systems with Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Christophe Bobda

    2009-01-01

    Full Text Available We present a methodology based on self-organization to manage resources in networked embedded systems based on reconfigurable hardware. Two points are detailed in this paper, the monitoring system used to analyse the system and the Local Marketplaces Global Symbiosis (LMGS concept defined for self-organization of dynamically reconfigurable nodes.

  18. 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.)

  19. An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Loparo, Kenneth [Case Western Reserve Univ., Cleveland, OH (United States); Kolacinski, Richard [Case Western Reserve Univ., Cleveland, OH (United States); Threeanaew, Wanchat [Case Western Reserve Univ., Cleveland, OH (United States); Agharazi, Hanieh [Case Western Reserve Univ., Cleveland, OH (United States)

    2017-01-30

    A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the current architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.

  20. 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)

  1. Application of a self-organizing map and positive matrix factorization to investigate the spatial distributions and sources of polycyclic aromatic hydrocarbons in soils from Xiangfen County, northern China.

    Science.gov (United States)

    Tao, Shi-Yang; Zhong, Bu-Qing; Lin, Yan; Ma, Jin; Zhou, Yongzhang; Hou, Hong; Zhao, Long; Sun, Zaijin; Qin, Xiaopeng; Shi, Huading

    2017-07-01

    The concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in 128 surface soil samples from Xiangfen County, northern China. The total mass concentration of these PAHs ranged from 52 to 10,524ng/g, with a mean of 723ng/g. Four-ring PAHs contributed almost 50% of the total PAH burden. A self-organizing map and positive matrix factorization were applied to investigate the spatial distribution and source apportionment of PAHs. Three emission sources of PAHs were identified, namely, coking ovens (21.9%), coal/biomass combustion (60.1%), and anthracene oil (18.0%). High concentrations of low-molecular-weight PAHs were particularly apparent in the coking plant zone in the region around Gucheng Town. High-molecular-weight PAHs mainly originated from coal/biomass combustion around Gucheng Town, Xincheng Town, and Taosi Town. PAHs in the soil of Xiangfen County are unlikely to pose a significant cancer risk for the population. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. USGS Topo Base Map from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The USGS Topographic Base Map from The National Map. This tile cached web map service combines the most current data services (Boundaries, Names, Transportation,...

  3. 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.

  4. Classification of Fetal Heart Rate Tracings Based on Wavelet-Transform & Self-Organizing Map Neural Networks

    National Research Council Canada - National Science Library

    Vasios, G

    2001-01-01

    .... We demonstrate that it is possible to distinguish between healthy subjects and acidemic fetuses by way of wavelet transform analysis of the fetal heart rate recordings and fetal pulse oximetry (FSpO2...

  5. Self Organization in Compensated Semiconductors

    Science.gov (United States)

    Berezin, Alexander A.

    2004-03-01

    In partially compensated semiconductor (PCS) Fermi level is pinned to donor sub-band. Due to positional randomness and almost isoenergetic hoppings, donor-spanned electronic subsystem in PCS forms fluid-like highly mobile collective state. This makes PCS playground for pattern formation, self-organization, complexity emergence, electronic neural networks, and perhaps even for origins of life, bioevolution and consciousness. Through effects of impact and/or Auger ionization of donor sites, whole PCS may collapse (spinodal decomposition) into microblocks potentially capable of replication and protobiological activity (DNA analogue). Electronic screening effects may act in RNA fashion by introducing additional length scale(s) to system. Spontaneous quantum computing on charged/neutral sites becomes potential generator of informationally loaded microstructures akin to "Carl Sagan Effect" (hidden messages in Pi in his "Contact") or informational self-organization of "Library of Babel" of J.L. Borges. Even general relativity effects at Planck scale (R.Penrose) may affect the dynamics through (e.g.) isotopic variations of atomic mass and local density (A.A.Berezin, 1992). Thus, PCS can serve as toy model (experimental and computational) at interface of physics and life sciences.

  6. USGS Imagery Only Base Map Service from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — USGS Imagery Only is a tile cache base map of orthoimagery in The National Map visible to the 1:18,000 scale. Orthoimagery data are typically high resolution images...

  7. Self-organization, Networks, Future

    Directory of Open Access Journals (Sweden)

    T. S. Akhromeyeva

    2013-01-01

    Full Text Available This paper presents an analytical review of a conference on the great scientist, a brilliant professor, an outstanding educator Sergei Kapitsa, held in November 2012. In the focus of this forum were problems of self-organization and a paradigm of network structures. The use of networks in the context of national defense, economics, management of mass consciousness was discussed. The analysis of neural networks in technical systems, the structure of the brain, as well as in the space of knowledge, information, and behavioral strategies plays an important role. One of the conference purposes was to an online organize community in Russia and to identify the most promising directions in this field. Some of them are presented in this paper.

  8. Self-organizing magnetohydrodynamic plasma

    International Nuclear Information System (INIS)

    Sato, T.; Horiuchi, R.; Watanabe, K.; Hayashi, T.; Kusano, K.

    1990-09-01

    In a resistive magnetohydrodynamic (MHD) plasma, both the magnetic energy and the magnetic helicity dissipate with the resistive time scale. When sufficiently large free magnetic energy does exist, however, an ideal current driven instability is excited whereby magnetic reconnection is driven at a converging point of induced plasma flows which does exist in a bounded compressible plasma. At a reconnection point excess free energy (entropy) is rapidly dissipated by ohmic heating and lost by radiation, while magnetic helicity is completely conserved. The magnetic topology is largely changed by reconnection and a new ordered structure with the same helicity is created. It is discussed that magnetic reconnection plays a key role in the MHD self-organization process. (author)

  9. 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...

  10. Self-organization through decoupling

    Directory of Open Access Journals (Sweden)

    Romar Correa

    2000-01-01

    Full Text Available In one line of research, the transition from Fordism to flexible specialisation is explained by the infeasibility of a mode of regulation that relied on central controls. According to another explanation, which we favour, the disintegration of vertically integrated production is unpredictable. The concept of self-organization is often recommended to model the transition from hierarchical organizational forms to flatter structures. Formally, a conditionally stable nonlinear system of differential equations is examined. In the first thesis, the characteristic roots with positive real parts play the role of ‘order’ parameters which can become unstable modes. The rest of the variables refer to stable modes. The strategy is to show that the stable modes can be expressed in terms of the unstable modes so that the former can be eliminated from the system. On the other hand, we provide a theorem showing that a coupled set of differential equations can become uncoupled and vice versa as an argument in favour of the second thesis. The path of evolution can turn both ways.

  11. Self-organized criticality paradigm

    International Nuclear Information System (INIS)

    Duran, I.; Stoeckel, J.; Hron, M.; Horacek, J.; Jakubka, K.; Kryska, L.

    2000-01-01

    According to the paradigm of the Self-Organized Criticality (SOC), the anomalous transport in tokamaks is caused by fast transient processes - avalanches. One of the manifestations of these phenomena should be 1/f decay of electrostatic fluctuations power spectra in a certain frequency range. In this paper, the frequency spectra of floating potential, density and fluctuation-induced flux, measured by poloidal and radial arrays of Langmuir probes on the CASTOR tokamak, are presented. The floating potential and the fluctuation-induced flux decay from 30 kHz up to 100 kHz as f -1 . The plasma density decays as f -1 in a more narrow band, 20 to 40 kHz. The possible limitation of SOC behavior for frequencies higher than 100 kHz due to intermittency is stressed. For this reason the Probability Distribution Functions (PDFs) of floating potential fluctuations were computed at different time scales using wavelet transform. A clear departure of the computed PDFs from Gaussianity, which is a classical signature of intermittency, is observed at time scales under 10 μs (100 kHz). (author)

  12. European wet deposition maps based on measurements

    NARCIS (Netherlands)

    Leeuwen EP van; Erisman JW; Draaijers GPJ; Potma CJM; Pul WAJ van; LLO

    1995-01-01

    To date, wet deposition maps on a European scale have been based on long-range transport model results. For most components wet deposition maps based on measurements are only available on national scales. Wet deposition maps of acidifying components and base cations based on measurements are needed

  13. 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...

  14. Measuring the Complexity of Self-Organizing Traffic Lights

    Directory of Open Access Journals (Sweden)

    Darío Zubillaga

    2014-04-01

    Full Text Available We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.

  15. Self-organized criticality and urban development

    Directory of Open Access Journals (Sweden)

    Michael Batty

    1999-01-01

    Full Text Available Urban society is undergoing as profound a spatial transformation as that associated with the emergence of the industrial city two centuries ago. To describe and measure this transition, we introduce a new theory based on the concept that large-scale, complex systems composed of many interacting elements, show a surprising degree of resilience to change, holding themselves at critical levels for long periods until conditions emerge which move the system, often abruptly, to a new threshold. This theory is called ‘self-organized criticality’; it is consistent with systems in which global patterns emerge from local action which is the hallmark of self-organization, and it is consistent with developments in system dynamics and their morphology which find expression in fractal geometry and weak chaos theory. We illustrate the theory using a unique space–time series of urban development for Buffalo, Western New York, which contains the locations of over one quarter of a million sites coded by their year of construction and dating back to 1773, some 60 years before the city began to develop. We measure the emergence and growth of the city using urban density functions from which measures of fractal dimension are used to construct growth paths of the way the city has grown to fill its region. These phase portraits suggest the existence of transitions between the frontier, the settled agricultural region, the centralized industrial city and the decentralized postindustrial city, and our analysis reveals that Buffalo has maintained itself at a critical threshold since the emergence of the automobile city some 70 years ago. Our implied speculation is: how long will this kind of urban form be maintained in the face of seemingly unstoppable technological change?

  16. 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).

  17. A self-organized system of smart preys and predators

    Energy Technology Data Exchange (ETDEWEB)

    Rozenfeld, Alejandro F. [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas (INIFTA), Facultad de Ciencias Exactas, UNLP, CONICET, Suc. 4, C.C. 16 (1900) La Plata (Argentina); Albano, Ezequiel V. [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas (INIFTA), Facultad de Ciencias Exactas, UNLP, CONICET, Suc. 4, C.C. 16 (1900) La Plata (Argentina)]. E-mail: ealbano@inifta.unlp.edu.ar

    2004-11-22

    Based on the fact that, a standard prey-predator model (SPPM), exhibits irreversible phase transitions, belonging to the universality class of directed percolation (DP), between prey-predator coexistence and predator extinction [Phys. Lett. A 280 (2001) 45], a self-organized prey-predator model (SOPPM) is formulated and studied by means of extensive Monte Carlo simulations. The SOPPM is achieved defining the parameters of the SPPM as functions of the density of species. It is shown that the SOPPM self-organizes into an active state close the absorbing phase of the SPPM, and consequently their avalanche exponents also belong to the universality class of DP.

  18. 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.

  19. Ischemia Detection Using Supervised Learning for Hierarchical Neural Networks Based on Kohonen-Maps

    National Research Council Canada - National Science Library

    Vladutu, L

    2001-01-01

    .... The motivation for developing the Supervising Network - Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications...

  20. Self-organization as a possible route to fusion energy

    International Nuclear Information System (INIS)

    Sanduloviciu, M.; Lozneanu, E.; Popescu, S.

    2000-01-01

    The generation of a ball lightning-like complex structure by sudden injection of matter and energy proves the presence of a cascading self-organization scenario in an experimental device containing a collisional plasma. Based on these results, we suggest the possibility to replicate, under controlled laboratory conditions, the ball lightning-like structures with potential fusion applications. (author)

  1. USGS Imagery Topo Base Map Service from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — USGS Imagery Topo is a topographic tile cache base map with orthoimagery as a backdrop, and combines the most current data (Boundaries, Names, Transportation,...

  2. USGS Hill Shade Base Map Service from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — USGS Hill Shade (or Shaded Relief) is a tile cache base map created from the National Elevation Dataset (NED), a seamless dataset of best available raster elevation...

  3. USGS Topo Base Map Service from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — USGS Topo is a topographic tile cache base map that combines the most current data (Boundaries, Names, Transportation, Elevation, Hydrography, Land Cover, and other...

  4. Vision-based mapping with cooperative robots

    Science.gov (United States)

    Little, James J.; Jennings, Cullen; Murray, Don

    1998-10-01

    Two stereo-vision-based mobile robots navigate and autonomously explore their environment safely while building occupancy grid maps of the environment. The robots maintain position estimates within a global coordinate frame using landmark recognition. This allows them to build a common map by sharing position information and stereo data. Stereo vision processing and map updates are done at 3 Hz and the robots move at speeds of 200 cm/s. Cooperative mapping is achieved through autonomous exploration of unstructured and dynamic environments. The map is constructed conservatively, so as to be useful for collision-free path planning. Each robot maintains a separate copy of a shared map, and then posts updates to the common map when it returns to observe a landmark at home base. Issues include synchronization, mutual localization, navigation, exploration, registration of maps, merging repeated views (fusion), centralized vs decentralized maps.

  5. 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.

  6. 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

  7. Self-organized Segregation on the Grid

    Science.gov (United States)

    Omidvar, Hamed; Franceschetti, Massimo

    2018-02-01

    We consider an agent-based model with exponentially distributed waiting times in which two types of agents interact locally over a graph, and based on this interaction and on the value of a common intolerance threshold τ , decide whether to change their types. This is equivalent to a zero-temperature ising model with Glauber dynamics, an asynchronous cellular automaton with extended Moore neighborhoods, or a Schelling model of self-organized segregation in an open system, and has applications in the analysis of social and biological networks, and spin glasses systems. Some rigorous results were recently obtained in the theoretical computer science literature, and this work provides several extensions. We enlarge the intolerance interval leading to the expected formation of large segregated regions of agents of a single type from the known size ɛ >0 to size ≈ 0.134. Namely, we show that for 0.433sites can be observed within any sufficiently large region of the occupied percolation cluster. The exponential bounds that we provide also imply that complete segregation, where agents of a single type cover the whole grid, does not occur with high probability for p=1/2 and the range of intolerance considered.

  8. 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.)

  9. 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)

  10. 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)

  11. Smartphones Based Mobile Mapping Systems

    Directory of Open Access Journals (Sweden)

    A. Al-Hamad

    2014-06-01

    Full Text Available The past 20 years have witnessed an explosive growth in the demand for geo-spatial data. This demand has numerous sources and takes many forms; however, the net effect is an ever-increasing thirst for data that is more accurate, has higher density, is produced more rapidly, and is acquired less expensively. For mapping and Geographic Information Systems (GIS projects, this has been achieved through the major development of Mobile Mapping Systems (MMS. MMS integrate various navigation and remote sensing technologies which allow mapping from moving platforms (e.g. cars, airplanes, boats, etc. to obtain the 3D coordinates of the points of interest. Such systems obtain accuracies that are suitable for all but the most demanding mapping and engineering applications. However, this accuracy doesn't come cheaply. As a consequence of the platform and navigation and mapping technologies used, even an "inexpensive" system costs well over 200 000 USD. Today's mobile phones are getting ever more sophisticated. Phone makers are determined to reduce the gap between computers and mobile phones. Smartphones, in addition to becoming status symbols, are increasingly being equipped with extended Global Positioning System (GPS capabilities, Micro Electro Mechanical System (MEMS inertial sensors, extremely powerful computing power and very high resolution cameras. Using all of these components, smartphones have the potential to replace the traditional land MMS and portable GPS/GIS equipment. This paper introduces an innovative application of smartphones as a very low cost portable MMS for mapping and GIS applications.

  12. Smartphones Based Mobile Mapping Systems

    Science.gov (United States)

    Al-Hamad, A.; El-Sheimy, N.

    2014-06-01

    The past 20 years have witnessed an explosive growth in the demand for geo-spatial data. This demand has numerous sources and takes many forms; however, the net effect is an ever-increasing thirst for data that is more accurate, has higher density, is produced more rapidly, and is acquired less expensively. For mapping and Geographic Information Systems (GIS) projects, this has been achieved through the major development of Mobile Mapping Systems (MMS). MMS integrate various navigation and remote sensing technologies which allow mapping from moving platforms (e.g. cars, airplanes, boats, etc.) to obtain the 3D coordinates of the points of interest. Such systems obtain accuracies that are suitable for all but the most demanding mapping and engineering applications. However, this accuracy doesn't come cheaply. As a consequence of the platform and navigation and mapping technologies used, even an "inexpensive" system costs well over 200 000 USD. Today's mobile phones are getting ever more sophisticated. Phone makers are determined to reduce the gap between computers and mobile phones. Smartphones, in addition to becoming status symbols, are increasingly being equipped with extended Global Positioning System (GPS) capabilities, Micro Electro Mechanical System (MEMS) inertial sensors, extremely powerful computing power and very high resolution cameras. Using all of these components, smartphones have the potential to replace the traditional land MMS and portable GPS/GIS equipment. This paper introduces an innovative application of smartphones as a very low cost portable MMS for mapping and GIS applications.

  13. Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach.

    Directory of Open Access Journals (Sweden)

    Samreen Laghari

    Full Text Available Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT implies an inherent difficulty in modeling problems.It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS. The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC framework to model a Complex communication network problem.We use Exploratory Agent-based Modeling (EABM, as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy.The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

  14. Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach.

    Science.gov (United States)

    Laghari, Samreen; Niazi, Muaz A

    2016-01-01

    Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

  15. A self-organized criticality model for plasma transport

    International Nuclear Information System (INIS)

    Carreras, B.A.; Newman, D.; Lynch, V.E.

    1996-01-01

    Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC). The SOC concept brings together the self-similarity on space and time scales that is common to many of these phenomena. The application of the SOC modelling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad band fluctuation spectra with universal characteristics and fast time scales. A model realization of self-organized criticality for plasma transport in a magnetic confinement device is presented. The model is based on subcritical resistive pressure-gradient-driven turbulence. Three-dimensional nonlinear calculations based on this model show the existence of transport under subcritical conditions. This model that includes fluctuation dynamics leads to results very similar to the running sandpile paradigm

  16. Self-organized service negotiation for collaborative decision making.

    Science.gov (United States)

    Zhang, Bo; Huang, Zhenhua; Zheng, Ziming

    2014-01-01

    This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM.

  17. Self-organized critical pinball machine

    DEFF Research Database (Denmark)

    Flyvbjerg, H.

    2004-01-01

    The nature of self-organized criticality (SOC) is pin-pointed with a simple mechanical model: a pinball machine. Its phase space is fully parameterized by two integer variables, one describing the state of an on-going game, the other describing the state of the machine. This is the simplest...

  18. Self-organized criticality in fragmenting

    DEFF Research Database (Denmark)

    Oddershede, L.; Dimon, P.; Bohr, J.

    1993-01-01

    The measured mass distributions of fragments from 26 fractured objects of gypsum, soap, stearic paraffin, and potato show evidence of obeying scaling laws; this suggests the possibility of self-organized criticality in fragmenting. The probability of finding a fragment scales inversely to a power...

  19. Functional self-organization in complex systems

    Energy Technology Data Exchange (ETDEWEB)

    Fontana, W. (Los Alamos National Lab., NM (USA) Santa Fe Inst., NM (USA))

    1990-01-01

    A novel approach to functional self-organization is presented. It consists of a universe generated by a formal language that defines objects (=programs), their meaning (=functions), and their interactions (=composition). Results obtained so far are briefly discussed. 17 refs., 5 figs.

  20. Architectural Patterns for Self-Organizing Systems-of-Systems

    Science.gov (United States)

    2011-05-01

    show that they are necessary for self-organization to occur. Common Purpose Abraham Maslow proposed a theory on human motivation based on a hierarchy...http://www.hole-in-the-wall.com/abouthiwel.html (accessed October 28, 2010). 21. Maslow , Abraham . 1943. A theory of human motivation. In Psychological...in-the-wall Education Ltd. http://www.hole- in-the-wall.com/abouthiwel.html (accessed October 28, 2010). 22. Maslow , Abraham . 1943. A theory of human

  1. 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.

  2. The importance of orientation in proton transport of a polymer film based on an oriented self-organized columnar liquid-crystalline polyether

    Energy Technology Data Exchange (ETDEWEB)

    Tylkowski, Bartosz; Castelao, Nuria [Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Paiesos Catalans, 26, E-43007, Tarragona (Spain); Giamberini, Marta, E-mail: marta.giamberini@urv.net [Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Paiesos Catalans, 26, E-43007, Tarragona (Spain); Garcia-Valls, Ricard [Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Paiesos Catalans, 26, E-43007, Tarragona (Spain); Reina, Jose Antonio [Departament de Quimica Analitica i Quimica Organica, Universitat Rovira i Virgili, Carrer Marcel.li Domingo s/n, E-43007, Tarragona (Spain); Gumi, Tania [Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Paiesos Catalans, 26, E-43007, Tarragona (Spain)

    2012-02-01

    We prepared membranes based on a liquid-crystalline side-chain polyether obtained by chemical modification of commercial poly(epichlorohydrin) (PECH) with dendrons. This polymer exhibited a columnar structure, which could form an ion channel in the inner part. The columns were successfully oriented by taking advantage of surface interactions between the polymer and hydrophilic substrates, as confirmed by X-ray diffraction analysis (XRD), environmental scanning electron microscopy (ESEM) and optical microscopy between crossed polars (POM). Column orientation was found to be crucial for effective transport: the oriented membranes exhibited proton transport comparable to that of Nafion Registered-Sign N117 and no water uptake. An increase in sodium ion concentration in the feed phase suggested a proton/cation antiport. On the contrary, no proton transport was detected on unoriented membranes based on the same liquid-crystalline side-chain polyether or on unmodified PECH. - Highlights: Black-Right-Pointing-Pointer We prepared oriented membranes based on a liquid crystalline columnar polyether. Black-Right-Pointing-Pointer In this structure, the inner polyether chain could work as an ion channel. Black-Right-Pointing-Pointer We obtained membranes by casting a chloroform solution in the presence of water. Black-Right-Pointing-Pointer Membranes showed good proton permeability due to the presence of oriented channels.

  3. The importance of orientation in proton transport of a polymer film based on an oriented self-organized columnar liquid-crystalline polyether

    International Nuclear Information System (INIS)

    Tylkowski, Bartosz; Castelao, Nuria; Giamberini, Marta; Garcia-Valls, Ricard; Reina, José Antonio; Gumí, Tània

    2012-01-01

    We prepared membranes based on a liquid-crystalline side-chain polyether obtained by chemical modification of commercial poly(epichlorohydrin) (PECH) with dendrons. This polymer exhibited a columnar structure, which could form an ion channel in the inner part. The columns were successfully oriented by taking advantage of surface interactions between the polymer and hydrophilic substrates, as confirmed by X-ray diffraction analysis (XRD), environmental scanning electron microscopy (ESEM) and optical microscopy between crossed polars (POM). Column orientation was found to be crucial for effective transport: the oriented membranes exhibited proton transport comparable to that of Nafion® N117 and no water uptake. An increase in sodium ion concentration in the feed phase suggested a proton/cation antiport. On the contrary, no proton transport was detected on unoriented membranes based on the same liquid-crystalline side-chain polyether or on unmodified PECH. - Highlights: ► We prepared oriented membranes based on a liquid crystalline columnar polyether. ► In this structure, the inner polyether chain could work as an ion channel. ► We obtained membranes by casting a chloroform solution in the presence of water. ► Membranes showed good proton permeability due to the presence of oriented channels.

  4. Self-organization via active exploration in robotic applications

    Science.gov (United States)

    Ogmen, H.; Prakash, R. V.

    1992-01-01

    We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization

  5. Energy driven self-organization in nanoscale metallic liquid films.

    Science.gov (United States)

    Krishna, H; Shirato, N; Favazza, C; Kalyanaraman, R

    2009-10-01

    Nanometre thick metallic liquid films on inert substrates can spontaneously dewet and self-organize into complex nanomorphologies and nanostructures with well-defined length scales. Nanosecond pulses of an ultraviolet laser can capture the dewetting evolution and ensuing nanomorphologies, as well as introduce dramatic changes to dewetting length scales due to the nanoscopic nature of film heating. Here, we show theoretically that the self-organization principle, based on equating the rate of transfer of thermodynamic free energy to rate of loss in liquid flow, accurately describes the spontaneous dewetting. Experimental measurements of laser dewetting of Ag and Co liquid films on SiO(2) substrates confirm this principle. This energy transfer approach could be useful for analyzing the behavior of nanomaterials and chemical processes in which spontaneous changes are important.

  6. 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.

  7. 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)

  8. Self-organized critical neural networks

    International Nuclear Information System (INIS)

    Bornholdt, Stefan; Roehl, Torsten

    2003-01-01

    A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters

  9. 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.

  10. 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

  11. Workplace Accidents and Self-Organized Criticality

    OpenAIRE

    Mauro, John C.; Diehl, Brett; Marcellin, Richard F.; Vaughn, Daniel J.

    2018-01-01

    The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace a...

  12. Self-organization in circular shear layers

    DEFF Research Database (Denmark)

    Bergeron, K.; Coutsias, E.A.; Lynov, Jens-Peter

    1996-01-01

    Experiments on forced circular shear layers performed in both magnetized plasmas and in rotating fluids reveal qualitatively similar self-organization processes leading to the formation of patterns of coherent vortical structures with varying complexity. In this paper results are presented from...... both weakly nonlinear analysis and full numerical simulations that closely reproduce the experimental observations. Varying the Reynolds number leads to bifurcation sequences accompanied by topological changes in the distribution of the coherent structures as well as clear transitions in the total...

  13. Self-organized criticality in neural networks

    Science.gov (United States)

    Makarenkov, Vladimir I.; Kirillov, A. B.

    1991-08-01

    Possible mechanisms of creating different types of persistent states for informational processing are regarded. It is presented two origins of criticalities - self-organized and phase transition. A comparative analyses of their behavior is given. It is demonstrated that despite a likeness there are important differences. These differences can play a significant role to explain the physical issue of such highest functions of the brain as a short-term memory and attention. 1.

  14. Instantons in Self-Organizing Logic Gates

    Science.gov (United States)

    Bearden, Sean R. B.; Manukian, Haik; Traversa, Fabio L.; Di Ventra, Massimiliano

    2018-03-01

    Self-organizing logic is a recently suggested framework that allows the solution of Boolean truth tables "in reverse"; i.e., it is able to satisfy the logical proposition of gates regardless to which terminal(s) the truth value is assigned ("terminal-agnostic logic"). It can be realized if time nonlocality (memory) is present. A practical realization of self-organizing logic gates (SOLGs) can be done by combining circuit elements with and without memory. By employing one such realization, we show, numerically, that SOLGs exploit elementary instantons to reach equilibrium points. Instantons are classical trajectories of the nonlinear equations of motion describing SOLGs and connect topologically distinct critical points in the phase space. By linear analysis at those points, we show that these instantons connect the initial critical point of the dynamics, with at least one unstable direction, directly to the final fixed point. We also show that the memory content of these gates affects only the relaxation time to reach the logically consistent solution. Finally, we demonstrate, by solving the corresponding stochastic differential equations, that, since instantons connect critical points, noise and perturbations may change the instanton trajectory in the phase space but not the initial and final critical points. Therefore, even for extremely large noise levels, the gates self-organize to the correct solution. Our work provides a physical understanding of, and can serve as an inspiration for, models of bidirectional logic gates that are emerging as important tools in physics-inspired, unconventional computing.

  15. Mapping specific soil functions based on digital soil property maps

    Science.gov (United States)

    Pásztor, László; Fodor, Nándor; Farkas-Iványi, Kinga; Szabó, József; Bakacsi, Zsófia; Koós, Sándor

    2016-04-01

    Quantification of soil functions and services is a great challenge in itself even if the spatial relevance is supposed to be identified and regionalized. Proxies and indicators are widely used in ecosystem service mapping. Soil services could also be approximated by elementary soil features. One solution is the association of soil types with services as basic principle. Soil property maps however provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. In the frame of the activities referred as "Digital, Optimized, Soil Related Maps and Information in Hungary" (DOSoReMI.hu) numerous soil property maps have been compiled so far with proper DSM techniques partly according to GSM.net specifications, partly by slightly or more strictly changing some of its predefined parameters (depth intervals, pixel size, property etc.). The elaborated maps have been further utilized, since even DOSoReMI.hu was intended to take steps toward the regionalization of higher level soil information (secondary properties, functions, services). In the meantime the recently started AGRAGIS project requested spatial soil related information in order to estimate agri-environmental related impacts of climate change and support the associated vulnerability assessment. One of the most vulnerable services of soils in the context of climate change is their provisioning service. In our work it was approximated by productivity, which was estimated by a sequential scenario based crop modelling. It took into consideration long term (50 years) time series of both measured and predicted climatic parameters as well as accounted for the potential differences in agricultural practice and crop production. The flexible parametrization and multiple results of modelling was then applied for the spatial assessment of sensitivity, vulnerability, exposure and adaptive capacity of soils in the context of the forecasted changes in

  16. 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.

  17. 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...

  18. 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…

  19. Do earthquakes exhibit self-organized criticality?

    International Nuclear Information System (INIS)

    Yang Xiaosong; Ma Jin; Du Shuming

    2004-01-01

    If earthquakes are phenomena of self-organized criticality (SOC), statistical characteristics of the earthquake time series should be invariant after the sequence of events in an earthquake catalog are randomly rearranged. In this Letter we argue that earthquakes are unlikely phenomena of SOC because our analysis of the Southern California Earthquake Catalog shows that the first-return-time probability P M (T) is apparently changed after the time series is rearranged. This suggests that the SOC theory should not be used to oppose the efforts of earthquake prediction

  20. Leader Election and Shape Formation with Self-Organizing Programmable Matter

    OpenAIRE

    Daymude, Joshua J.; Derakhshandeh, Zahra; Gmyr, Robert; Strothmann, Thim; Bazzi, Rida; Richa, Andréa W.; Scheideler, Christian

    2015-01-01

    We consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a suitable model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in SPAA 2014. Based on the geometric model, we present efficient local-control ...

  1. 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.

  2. Self-organization in irradiated materials

    International Nuclear Information System (INIS)

    Gerasimenko, N.N.; Dzhamanbalin, K.K.; Medetov, N.A.

    2003-01-01

    Full text: By the present time a great deal of experimental material concerning self-organization in irradiated materials is stored. It means that in different materials (single crystal and amorphous semiconductor, metals, polymers) during one process of irradiation with accelerated particles or energetic quanta the structure previously disordered can be reordered to the previous or different order. These processes are considered separately from the processes of radiation-stimulated ordering when the renewal of the structure occurs as the result of extra irradiation, sometimes accompanied with another influence (heating, lighting, application of mechanical tensions). The processes of reordering are divided into two basic classes: the reconstruction of crystalline structure (1) and the formation of space-ordered system (2). The processes of ordering are considered with the use of synergetic approach and are analyzed conformably to the concrete conditions of new order appearance process realization in order to reveal the self-organization factor's role. The concrete experimental results of investigating of the radiation ordering processes are analyzed for different materials: semiconductor, metals, inorganic dielectrics, polymers. The ordering processes are examined from the point of their possible use in the technology of creating nano-dimensional structures general and quantum-dimensional ones in particular

  3. Self-organized template formation for quantum dot ordering

    International Nuclear Information System (INIS)

    Noetzel, Richard; Mano, Takaaki; Wolter, Joachim H.

    2004-01-01

    Ordered arrays of quantum dots (QDs) are created by self-organized anisotropic strain engineering of (In,Ga)As/GaAs quantum wire (QWR) superlattice (SL) templates on exactly oriented GaAs (100) substrates by molecular beam epitaxy (MBE). The well-defined one-dimensional arrays of (In,Ga)As QDs formed on top of these templates due to local strain recognition are of excellent structural and optical quality up to room temperature. The QD arrays thus allow for fundamental studies and device operation principles based on single- and multiple carrier- and photon-, and coherent quantum interference effects

  4. Self-organized Criticality Model for Ocean Internal Waves

    International Nuclear Information System (INIS)

    Wang Gang; Hou Yijun; Lin Min; Qiao Fangli

    2009-01-01

    In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality (SOC). The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 (1972) 225; J. Geophys. Res. 80 (1975) 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed. (general)

  5. 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...

  6. Study on self organized criticality of China power grid blackouts

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Xingyong; Zhang, Xiubin; He, Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)

    2009-03-15

    Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts. (author)

  7. Study on self organized criticality of China power grid blackouts

    Energy Technology Data Exchange (ETDEWEB)

    Zhao Xingyong [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)], E-mail: zhaoxingyong@sjtu.edu.cn; Zhang Xiubin; He Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)

    2009-03-15

    Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts.

  8. 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.

  9. Self-Organization in Integrated Conservation and Development Initiatives

    Directory of Open Access Journals (Sweden)

    Cristiana Simão Seixas

    2007-11-01

    Full Text Available This paper uses a cooking metaphor to explore key elements (i.e., ingredients for a great meal that contribute to self-organization processes in the context of successful community-based conservation (CBC or integrated conservation and development projects (ICDP. We pose two major questions: (1 What are the key factors that drive peoples' and/or organizations' willingness to take responsibilities and to act? (2 What contributes to community self-organization? In other words, how conservation-development projects originate, evolve, survive or disappear? In order to address these questions we examine trigger events and catalytic elements in several cases among the Equator Prize finalists and short-listed nominees, from both the 2002 and 2004 awards. The Prize recognizes efforts in integrating biodiversity conservation and poverty reduction. We use secondary data in our analysis, including data from several technical reports and scientific papers written about the Equator Prize finalists and short-listed nominees. We observed common ingredients in most projects including: (1 involvement and commitment of key players (including communities, (2 funding, (3 strong leadership, (4 capacity building, (5 partnership with supportive organizations and government, and (6 economic incentives (including alternative livelihood options. We also observed that CBC and ICDP initiatives opportunistically evolve in a multi-level world, in which local communities establish linkages with people and organizations at different political levels, across different geographical scales and for different purposes. We conclude that there is no right 'recipe' to promote community self-organization but often a mix of some of these six ingredients need to come together for 'success' and that one or two ingredients are not sufficient to ensure success. Also the existence of these six ingredients does not guarantee a great meal - the 'chef's' creativity also is critical. That is

  10. Machine-based mapping of innovation portfolios

    NARCIS (Netherlands)

    de Visser, Matthias; Miao, Shengfa; Englebienne, Gwenn; Sools, Anna Maria; Visscher, Klaasjan

    2017-01-01

    Machine learning techniques show a great promise for improving innovation portfolio management. In this paper we experiment with different methods to classify innovation projects of a high-tech firm as either explorative or exploitative, and compare the results with a manual, theory-based mapping of

  11. Control of self-organizing nonlinear systems

    CERN Document Server

    Klapp, Sabine; Hövel, Philipp

    2016-01-01

    The book summarizes the state-of-the-art of research on control of self-organizing nonlinear systems with contributions from leading international experts in the field. The first focus concerns recent methodological developments including control of networks and of noisy and time-delayed systems. As a second focus, the book features emerging concepts of application including control of quantum systems, soft condensed matter, and biological systems. Special topics reflecting the active research in the field are the analysis and control of chimera states in classical networks and in quantum systems, the mathematical treatment of multiscale systems, the control of colloidal and quantum transport, the control of epidemics and of neural network dynamics.

  12. Feedback, Lineages and Self-Organizing Morphogenesis.

    Directory of Open Access Journals (Sweden)

    Sameeran Kunche

    2016-03-01

    Full Text Available Feedback regulation of cell lineage progression plays an important role in tissue size homeostasis, but whether such feedback also plays an important role in tissue morphogenesis has yet to be explored. Here we use mathematical modeling to show that a particular feedback architecture in which both positive and negative diffusible signals act on stem and/or progenitor cells leads to the appearance of bistable or bi-modal growth behaviors, ultrasensitivity to external growth cues, local growth-driven budding, self-sustaining elongation, and the triggering of self-organization in the form of lamellar fingers. Such behaviors arise not through regulation of cell cycle speeds, but through the control of stem or progenitor self-renewal. Even though the spatial patterns that arise in this setting are the result of interactions between diffusible factors with antagonistic effects, morphogenesis is not the consequence of Turing-type instabilities.

  13. Self-organizing physical fields and gravity

    International Nuclear Information System (INIS)

    Pestov, I.B.

    2009-01-01

    It is shown that the Theory of Self-Organizing Physical Fields provides the adequate and consistent consideration of the gravitational phenomena. The general conclusion lies in the fact that the essence of gravidynamics is the new field concept of time and the general covariant law of energy conservation which in particular means that dark energy is simply the energy of the gravitational field. From the natural geometrical laws of gravidynamics the dynamical equations of the gravitational field are derived. Two exact solutions of these equations are obtained. One of them represents a shock gravitational wave and the other represents the Universe filled up with the gravitational energy only. These solutions are compared with the Schwarzschild and Friedmann solutions in the Einstein general theory of relativity

  14. Feedback, Lineages and Self-Organizing Morphogenesis

    Science.gov (United States)

    Calof, Anne L.; Lowengrub, John S.; Lander, Arthur D.

    2016-01-01

    Feedback regulation of cell lineage progression plays an important role in tissue size homeostasis, but whether such feedback also plays an important role in tissue morphogenesis has yet to be explored. Here we use mathematical modeling to show that a particular feedback architecture in which both positive and negative diffusible signals act on stem and/or progenitor cells leads to the appearance of bistable or bi-modal growth behaviors, ultrasensitivity to external growth cues, local growth-driven budding, self-sustaining elongation, and the triggering of self-organization in the form of lamellar fingers. Such behaviors arise not through regulation of cell cycle speeds, but through the control of stem or progenitor self-renewal. Even though the spatial patterns that arise in this setting are the result of interactions between diffusible factors with antagonistic effects, morphogenesis is not the consequence of Turing-type instabilities. PMID:26989903

  15. Development of objective flow regime identification method using self-organizing neural network

    International Nuclear Information System (INIS)

    Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

    2004-01-01

    Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

  16. 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.

  17. Acoustic seafloor sediment classification using self-organizing feature maps

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Kaustubha, R.; Hegde, A.; Pereira, A.

    formulation as developed by Charbonnier et al. [7]. Manuscript received November 2, 2000; revised June 14, 2001. This work was supported by Consejo Nacional de Ciencia y Tecnología, México. H. Hidalgo, E. Gómez-Treviño, and F. J. Esparza are with the CICESE..., Ensenada 22860, México (e-mail: hugo@cicese.mx; egomez@cicese.mx; fesparz@cicese.mx). J. L. Marroquín is with the CIMAT, Callejón Xalisco S.N. Valenciana, Gua- najuato 36240, México (e-mail: jlm@fractal.cimat.mx). Publisher Item Identifier S 0196...

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

    African Journals Online (AJOL)

    common multivariate method seen in in-process controlin pharmaceutical industry. This work was highlighted to support the analyst's decision when developing new analytical methods. The work also demonstrated the ability and investigated the performance of these two SOM methods for wavelength selection using partial.

  19. CrowdMapping: A Crowdsourcing-Based Terminology Mapping Method for Medical Data Standardization.

    Science.gov (United States)

    Mao, Huajian; Chi, Chenyang; Huang, Boyu; Meng, Haibin; Yu, Jinghui; Zhao, Dongsheng

    2017-01-01

    Standardized terminology is the prerequisite of data exchange in analysis of clinical processes. However, data from different electronic health record systems are based on idiosyncratic terminology systems, especially when the data is from different hospitals and healthcare organizations. Terminology standardization is necessary for the medical data analysis. We propose a crowdsourcing-based terminology mapping method, CrowdMapping, to standardize the terminology in medical data. CrowdMapping uses a confidential model to determine how terminologies are mapped to a standard system, like ICD-10. The model uses mappings from different health care organizations and evaluates the diversity of the mapping to determine a more sophisticated mapping rule. Further, the CrowdMapping model enables users to rate the mapping result and interact with the model evaluation. CrowdMapping is a work-in-progress system, we present initial results mapping terminologies.

  20. Self-organization in cathode boundary layer discharges in xenon

    International Nuclear Information System (INIS)

    Takano, Nobuhiko; Schoenbach, Karl H

    2006-01-01

    Self-organization of direct current xenon microdischarges in cathode boundary layer configuration has been studied for pressures in the range 30-140 Torr and for currents in the range 50 μA-1 mA. Side-on and end-on observations of the discharge have provided information on the structure and spatial arrangement of the plasma filaments. The regularly spaced filaments, which appear in the normal glow mode when the current is lowered, have a length which is determined by the cathode fall. It varies, dependent on pressure and current, between 50 and 70 μm. The minimum diameter is approximately 80 μm, as determined from the radiative emission in the visible. The filaments are sources of extensive excimer emission. Measurements of the cathode fall length have allowed us to determine the secondary emission coefficient for the discharge in the normal glow mode and to estimate the cathode fall voltage at the transition from normal glow mode to filamentary mode. It was found that the cathode fall voltage at this transition decreases, indicating the onset of additional electron gain processes at the cathode. The regular arrangement of the filaments, self-organization, is assumed to be due to Coulomb interactions between the positively charged cathode fall channels and positive space charges on the surface of the surrounding dielectric spacer. Calculations based on these assumptions showed good agreement with experimentally observed filament patterns

  1. 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.

  2. Topographical Hill Shading Map Production Based Tianditu (map World)

    Science.gov (United States)

    Wang, C.; Zha, Z.; Tang, D.; Yang, J.

    2018-04-01

    TIANDITU (Map World) is the public version of National Platform for Common Geospatial Information Service, and the terrain service is an important channel for users on the platform. With the development of TIANDITU, topographical hill shading map production for providing and updating global terrain map on line becomes necessary for the characters of strong intuition, three-dimensional sense and aesthetic effect. As such, the terrain service of TIANDITU focuses on displaying the different scales of topographical data globally. And this paper mainly aims to research the method of topographical hill shading map production globally using DEM (Digital Elevation Model) data between the displaying scales about 1 : 140,000,000 to 1 : 4,000,000, corresponded the display level from 2 to 7 on TIANDITU website.

  3. Hash function based on chaotic map lattices.

    Science.gov (United States)

    Wang, Shihong; Hu, Gang

    2007-06-01

    A new hash function system, based on coupled chaotic map dynamics, is suggested. By combining floating point computation of chaos and some simple algebraic operations, the system reaches very high bit confusion and diffusion rates, and this enables the system to have desired statistical properties and strong collision resistance. The chaos-based hash function has its advantages for high security and fast performance, and it serves as one of the most highly competitive candidates for practical applications of hash function for software realization and secure information communications in computer networks.

  4. 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

  5. Self-organized quantum rings : Physical characterization and theoretical modeling

    NARCIS (Netherlands)

    Fomin, V.M.; Gladilin, V.N.; Devreese, J.T.; Koenraad, P.M.; Fomin, V.M.

    2014-01-01

    An adequate modeling of the self-organized quantum rings is possible only on the basis of the modern characterization of those nanostructures.We discuss an atomic-scale analysis of the indium distribution of self-organized InGaAs quantum rings (QRs). The analysis of the shape, size and composition

  6. 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.)

  7. USGS Imagery Topo Large-scale Base Map Service from The National Map

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The USGS Imagery Topo Large service from The National Map (TNM) is a dynamic topographic base map service that combines the best available data (Boundaries,...

  8. Map-based mobile services design, interaction and usability

    CERN Document Server

    Meng, Liqiu; Winter, Stephan; Popovich, Vasily

    2008-01-01

    This book reports the newest research and technical achievements on the following theme blocks: Design of mobile map services and its constraints; Typology and usability of mobile map services; Visualization solutions on small displays for time-critical tasks; Mobile map users; Interaction and adaptation in mobile environments; and Applications of map-based mobile services.

  9. Natural hazards and self-organized criticality

    International Nuclear Information System (INIS)

    Krenn, R.

    2012-01-01

    Several natural hazards exhibit power-law behavior on their frequency-size distributions. Self-organized criticality has become a promising candidate that could offer a more in-depth understanding of the origin of temporal and spatial scaling in dissipative nonequilibrium systems. The outcomes of this thesis are presented in three scientific papers followed by a concluding summary and an appendix.In paper (A) we present a semi-phenomenological approach to explain the complex scaling behavior of the Drossel-Schwabl forest-fire model (DS-FFM) in two dimensions. We derive the scaling exponent solely from the scaling exponent of the clusters' accessible perimeter. Furthermore, the unusual transition to an exponential decay is explained both qualitatively and quantitatively. The exponential decay itself could be reproduced at least qualitatively. In paper (B) we extend the DS-FFM towards anthropogenic ignition factors. The main outcomes are an increase of the scaling exponent with decreasing lightning probability as well as a splitting of the partial frequency-size distributions of lightning induced and man made fires. Lightning is identified as the dominant mechanism in the regime of the largest fires. The results could be validated through an analysis of the Canadian Large Fire Database.In paper (C) we obtain an almost complete theory of the Olami-Feder-Christensen (OFC) model's complex spatio-temporal behavior. Synchronization pushes the system towards a critical state and generates the Gutenberg-Richter law. Desynchronization prevents the system from becoming overcritical and generates foreshocks and aftershocks. Our approach also provides a simple explanation of Omori's law. Beyond this, it explains the phenomena of foreshock migration and aftershock diffusion and the occurrence of large earthquakes without any foreshocks. A novel integer algorithm for the numerics is presented in appendix (A).(author) [de

  10. Subwavelength Microstructures Fabrication by Self-Organization Processes in Photopolymerizable Nanocomposite

    Directory of Open Access Journals (Sweden)

    I. Yu. Denisyuk

    2012-01-01

    Full Text Available This paper describes our research results on nanometers sizes subwavelength nanostructure fabrication by UV curing of special nanocomposite material with self-organization and light self-focusing effects. For this purpose, special UV curable nanocomposite material with a set of effects was developing: light self-focusing in the photopolymer with positive refractive index change, self-organization based on photo-induced nanoparticles transportation, and oxygen-based polymerization threshold. Both holographic and projection lithography writing methods application for microstructure making shows geometrical optical laws perturbation as result of nanocomposite self-organization effects with formation of nanometers-sized high-aspect-ratio structures. Obtained results will be useful for diffraction limit overcoming in projection lithography as well as for deep lithography technique.

  11. Self-organized plasmonic metasurfaces for all-optical modulation

    Science.gov (United States)

    Della Valle, G.; Polli, D.; Biagioni, P.; Martella, C.; Giordano, M. C.; Finazzi, M.; Longhi, S.; Duò, L.; Cerullo, G.; Buatier de Mongeot, F.

    2015-06-01

    We experimentally demonstrate a self-organized metasurface with a polarization dependent transmittance that can be dynamically controlled by optical means. The configuration consists of tightly packed plasmonic nanowires with a large dispersion of width and height produced by the defocused ion-beam sputtering of a thin gold film supported on a silica glass. Our results are quantitatively interpreted according to a theoretical model based on the thermomodulational nonlinearity of gold and a finite-element numerical analysis of the absorption and scattering cross-sections of the nanowires. We found that the polarization sensitivity of the metasurface can be strongly enhanced by pumping with ultrashort laser pulses, leading to potential applications in ultrafast all-optical modulation and switching of light.

  12. Modeling financial markets by self-organized criticality

    Science.gov (United States)

    Biondo, Alessio Emanuele; Pluchino, Alessandro; Rapisarda, Andrea

    2015-10-01

    We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to study their possible beneficial role in stabilizing the market, as found in other studies. Finally, we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.

  13. 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.

  14. Spectrally based mapping of riverbed composition

    Science.gov (United States)

    Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

    2016-01-01

    Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader

  15. 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.)

  16. ActionMap: A web-based software that automates loci assignments to framework maps.

    Science.gov (United States)

    Albini, Guillaume; Falque, Matthieu; Joets, Johann

    2003-07-01

    Genetic linkage computation may be a repetitive and time consuming task, especially when numerous loci are assigned to a framework map. We thus developed ActionMap, a web-based software that automates genetic mapping on a fixed framework map without adding the new markers to the map. Using this tool, hundreds of loci may be automatically assigned to the framework in a single process. ActionMap was initially developed to map numerous ESTs with a small plant mapping population and is limited to inbred lines and backcrosses. ActionMap is highly configurable and consists of Perl and PHP scripts that automate command steps for the MapMaker program. A set of web forms were designed for data import and mapping settings. Results of automatic mapping can be displayed as tables or drawings of maps and may be exported. The user may create personal access-restricted projects to store raw data, settings and mapping results. All data may be edited, updated or deleted. ActionMap may be used either online or downloaded for free (http://moulon.inra.fr/~bioinfo/).

  17. Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

    Science.gov (United States)

    Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan

    2018-04-01

    In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.

  18. 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.

  19. Secure eHealth-Care Service on Self-Organizing Software Platform

    Directory of Open Access Journals (Sweden)

    Im Y. Jung

    2014-01-01

    Full Text Available There are several applications connected to IT health devices on the self-organizing software platform (SoSp that allow patients or elderly users to be cared for remotely by their family doctors under normal circumstances or during emergencies. An evaluation of the SoSp applied through PAAR watch/self-organizing software platform router was conducted targeting a simple user interface for aging users, without the existence of extrasettings based on patient movement. On the other hand, like normal medical records, the access to, and transmission of, health information via PAAR watch/self-organizing software platform requires privacy protection. This paper proposes a security framework for health information management of the SoSp. The proposed framework was designed to ensure easy detection of identification information for typical users. In addition, it provides powerful protection of the user’s health information.

  20. 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

  1. Self-organized complex space charge configurations at the origin of flicker noise

    International Nuclear Information System (INIS)

    Popescu, S.; Lozneanu, E.; Sanduloviciu, M.

    2003-01-01

    Based on experimental results obtained from a plasma diode we explain the fluctuations of the voltage supported by a non-linear gaseous conductor by the dynamical behavior of spatiotemporal patterns, in the form of moving double layers, formed after self-organization. Such phenomena appear when the system is subjected to an external constraint that creates and maintains a local gradient of electron kinetic energy. The described phenomenology suggests a plausible explanation for the appearance of flicker noise also in other physical systems, as for example semiconductors and, implicitly, offers a new model for the so-called self-organized criticality concept

  2. Students' use of social software in self-organized learning environment

    DEFF Research Database (Denmark)

    Mathiasen, Helle; Dalsgaard, Christian

    2006-01-01

    The paper will argue that new possibilities of digital media, especially social software, have a potential regarding development of self-organized learning environments and facilitating self-governed activities. Based on a sociological perspective, the paper will clarify the concepts of informal...... and formal learning used in this paper. It is argued that formal and informal conditions of learning can supplement each other within an educational setting. A formal setting of project work forms the basis of informal, selfgoverned activities of students. The paper will argue that social software tools can...... support students' self-governed activities and their development of self-organized learning environments....

  3. 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

  4. PCR-Based EST Mapping in Wheat (Triticum aestivum L.

    Directory of Open Access Journals (Sweden)

    J. PERRY GUSTAFSON

    2009-04-01

    Full Text Available Mapping expressed sequence tags (ESTs to hexaploid wheat is aimed to reveal the structure and function of the hexaploid wheat genome. Sixty eight ESTs representing 26 genes were mapped into all seven homologous chromosome groups of wheat (Triticum aestivum L using a polymerase chain reaction technique. The majority of the ESTs were mapped to homologous chromosome group 2, and the least were mapped to homologous chromosome group 6. Comparative analysis between the EST map from this study and the EST map based on RFLPs showed 14 genes that have been mapped by both approaches were mapped to the same arm of the same homologous chromosome, which indicated that using PCR-based ESTs was a reliable approach in mapping ESTs in hexaploid wheat.

  5. Development of Geospatial Map Based Election Portal

    Science.gov (United States)

    Gupta, A. Kumar Chandra; Kumar, P.; Vasanth Kumar, N.

    2014-11-01

    The Geospatial Delhi Limited (GSDL), a Govt. of NCT of Delhi Company formed in order to provide the geospatial information of National Capital Territory of Delhi (NCTD) to the Government of National Capital Territory of Delhi (GNCTD) and its organs such as DDA, MCD, DJB, State Election Department, DMRC etc., for the benefit of all citizens of Government of National Capital Territory of Delhi (GNCTD). This paper describes the development of Geospatial Map based Election portal (GMEP) of NCT of Delhi. The portal has been developed as a map based spatial decision support system (SDSS) for pertain to planning and management of Department of Chief Electoral Officer, and as an election related information searching tools (Polling Station, Assembly and parliamentary constituency etc.,) for the citizens of NCTD. The GMEP is based on Client-Server architecture model. It has been developed using ArcGIS Server 10.0 with J2EE front-end on Microsoft Windows environment. The GMEP is scalable to enterprise SDSS with enterprise Geo Database & Virtual Private Network (VPN) connectivity. Spatial data to GMEP includes delimited precinct area boundaries of Voters Area of Polling stations, Assembly Constituency, Parliamentary Constituency, Election District, Landmark locations of Polling Stations & basic amenities (Police Stations, Hospitals, Schools and Fire Stations etc.). GMEP could help achieve not only the desired transparency and easiness in planning process but also facilitates through efficient & effective tools for management of elections. It enables a faster response to the changing ground realities in the development planning, owing to its in-built scientific approach and open-ended design.

  6. Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping

    Directory of Open Access Journals (Sweden)

    Firoz Ahmad

    2017-05-01

    Full Text Available Agroforestry has drawn the attention of researchers due to its capacity to reduce the poverty and land degradation, improve food security and mitigate the climate change. However, the progress in promoting agroforestry is held back due to the lack of reliable data sets and appropriate tools to accurately map and to have an adequate decision making system for agroforestry modules. Agroforestry suitability being one special form of land suitability is very pertinent to study in the current times when there is tremendous pressure on the land as it is a limited commodity. The study aims for applying the geo-spatial tools towards visualizing various soil and environmental data to reveal the trends and interrelationships and to achieve a nutrient availability and agroforestry suitability map. Using weight matrix and ranks, individual maps were developed in ArcGIS 10.1 platform to generate nutrient availability map, which was later used to develop agroforestry suitability map. Watersheds were delineated using DEM in some part of the study area and were evaluated for prioritizing it and agroforestry suitability of the watersheds were also done as per the schematic flowchart. Agroforestry suitability regions were delineated based upon the weight and ranks by integrated mapping. The total open area was identified 42.4% out of which 21.6% area was found to have high suitability towards agroforestry. Within the watersheds, 22 village points were generated for creating buffers, which were further evaluated showing its proximity to high suitable agroforestry sites thus generating tremendous opportunity to the villagers to carry out agroforestry projects locally. This research shows the capability of remote sensing in studying agroforestry practices and in estimating the prominent factors for its optimal productivity. The ongoing agroforestry projects can be potentially diverted in the areas of high suitability as an extension. The use of ancillary data in GIS

  7. A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity.

    Science.gov (United States)

    Cerchiari, Alec E; Garbe, James C; Jee, Noel Y; Todhunter, Michael E; Broaders, Kyle E; Peehl, Donna M; Desai, Tejal A; LaBarge, Mark A; Thomson, Matthew; Gartner, Zev J

    2015-02-17

    Developing tissues contain motile populations of cells that can self-organize into spatially ordered tissues based on differences in their interfacial surface energies. However, it is unclear how self-organization by this mechanism remains robust when interfacial energies become heterogeneous in either time or space. The ducts and acini of the human mammary gland are prototypical heterogeneous and dynamic tissues comprising two concentrically arranged cell types. To investigate the consequences of cellular heterogeneity and plasticity on cell positioning in the mammary gland, we reconstituted its self-organization from aggregates of primary cells in vitro. We find that self-organization is dominated by the interfacial energy of the tissue-ECM boundary, rather than by differential homo- and heterotypic energies of cell-cell interaction. Surprisingly, interactions with the tissue-ECM boundary are binary, in that only one cell type interacts appreciably with the boundary. Using mathematical modeling and cell-type-specific knockdown of key regulators of cell-cell cohesion, we show that this strategy of self-organization is robust to severe perturbations affecting cell-cell contact formation. We also find that this mechanism of self-organization is conserved in the human prostate. Therefore, a binary interfacial interaction with the tissue boundary provides a flexible and generalizable strategy for forming and maintaining the structure of two-component tissues that exhibit abundant heterogeneity and plasticity. Our model also predicts that mutations affecting binary cell-ECM interactions are catastrophic and could contribute to loss of tissue architecture in diseases such as breast cancer.

  8. LSOT: A Lightweight Self-Organized Trust Model in VANETs

    Directory of Open Access Journals (Sweden)

    Zhiquan Liu

    2016-01-01

    Full Text Available With the advances in automobile industry and wireless communication technology, Vehicular Ad hoc Networks (VANETs have attracted the attention of a large number of researchers. Trust management plays an important role in VANETs. However, it is still at the preliminary stage and the existing trust models cannot entirely conform to the characteristics of VANETs. This work proposes a novel Lightweight Self-Organized Trust (LSOT model which contains trust certificate-based and recommendation-based trust evaluations. Both the supernodes and trusted third parties are not needed in our model. In addition, we comprehensively consider three factor weights to ease the collusion attack in trust certificate-based trust evaluation, and we utilize the testing interaction method to build and maintain the trust network and propose a maximum local trust (MLT algorithm to identify trustworthy recommenders in recommendation-based trust evaluation. Furthermore, a fully distributed VANET scenario is deployed based on the famous Advogato dataset and a series of simulations and analysis are conducted. The results illustrate that our LSOT model significantly outperforms the excellent experience-based trust (EBT and Lightweight Cross-domain Trust (LCT models in terms of evaluation performance and robustness against the collusion attack.

  9. Investigation on Self-Organization Processes in DC Generators by Synergetic Modeling

    Directory of Open Access Journals (Sweden)

    Ion Voncilă

    2014-09-01

    Full Text Available In this paper is suggested a new mathematical model, based on which it can be justified the self-excitation DC generators, either shunt or series excitation, by self-organization phenomena that appear to overcome threshold values (self-excitation in these generators is an avalanche process, a positive feedback, considered at first glance uncontrollable.

  10. Investigation on Self-Organization Processes in DC Generators by Synergetic Modeling

    OpenAIRE

    Ion Voncilă; Mădălin Costin; Răzvan Buhosu

    2014-01-01

    In this paper is suggested a new mathematical model, based on which it can be justified the self-excitation DC generators, either shunt or series excitation, by self-organization phenomena that appear to overcome threshold values (self-excitation in these generators is an avalanche process, a positive feedback, considered at first glance uncontrollable).

  11. Photoluminescence of self-organized perylene bisimide polymers

    NARCIS (Netherlands)

    Neuteboom, E.E.; Meskers, S.C.J.; Meijer, E.W.; Janssen, R.A.J.

    2004-01-01

    Three polymers consisting of alternating perylene bisimide chromophores and flexible polytetrahydrofuran segments of different length have been studied using absorption and (time-resolved) photoluminescence spectroscopy. In o-dichlorobenzene, the chromophores self organize to form H-like aggregates.

  12. 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

  13. 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

  14. 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...

  15. Modelling the self-organization and collapse of complex networks

    Indian Academy of Sciences (India)

    Modelling the self-organization and collapse of complex networks. Sanjay Jain Department of Physics and Astrophysics, University of Delhi Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Santa Fe Institute, Santa Fe, New Mexico.

  16. Extending Particle Swarm Optimisers with Self-Organized Criticality

    DEFF Research Database (Denmark)

    Løvbjerg, Morten; Krink, Thiemo

    2002-01-01

    Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....

  17. On micro-scale self-organization in a plasma

    International Nuclear Information System (INIS)

    Maluckov, A.; Jovanovic, M.S.; Skoric, M.M.; Sato, T.

    1998-01-01

    We concentrate on a nonlinear saturation of a stimulated Raman backscattering in an open convective weakly confined model in the context of micro-kinetic scale self-organization in plasmas. The results have led to an assertion that a long-time nonlinear saturation in an open SRBS model with phenomenological effects of anomalous dissipation, plasma heating and subsequent entropy expulsion, reveals a generic interrelation of self-organization at wave-fluid (macro) and particle-kinetic (micro) levels. (author)

  18. 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.

  19. 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.

  20. Mapping population-based structural connectomes.

    Science.gov (United States)

    Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen; Girard, Gabriel; Chamberland, Maxime; Dunson, David; Srivastava, Anuj; Zhu, Hongtu

    2018-05-15

    Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Particle filter based MAP state estimation: A comparison

    NARCIS (Netherlands)

    Saha, S.; Boers, Y.; Driessen, J.N.; Mandal, Pranab K.; Bagchi, Arunabha

    2009-01-01

    MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi

  2. BEE FORAGE MAPPING BASED ON MULTISPECTRAL IMAGES LANDSAT

    Directory of Open Access Journals (Sweden)

    A. Moskalenko

    2016-10-01

    Full Text Available Possibilities of bee forage identification and mapping based on multispectral images have been shown in the research. Spectral brightness of bee forage has been determined with the use of satellite images. The effectiveness of some methods of image classification for mapping of bee forage is shown. Keywords: bee forage, mapping, multispectral images, image classification.

  3. Effect of prediction on the self-organization of pedestrian counter flow

    International Nuclear Information System (INIS)

    Wang Ziyang; Zhao Hui; Ma Jian; Qin Yong; Jia Limin

    2012-01-01

    Pedestrians may predict the behavior of others and then adjust their movement accordingly to avoid potential conflicts in advance. Motivated by this fact, we propose a predictive control theory-based pedestrian counter flow model, which describes the predictive mechanism underlying pedestrian self-organization phenomena. In this model, a pedestrian will make in-advance-avoid behavior based on the estimation of future moving gain within a given predictive length to reduce potential conflicts. The future gain in the present model is affected by three factors, i.e. the predictive length, the smooth degree of entrance and the influential area of coming pedestrians. Simulation results of the model show that increasing predictive length has a remarkable effect on reducing conflicts, improving pedestrian velocity, smoothing pedestrian movement and stabilizing the self-organized lanes. When enlarging the influential area of coming pedestrians, pedestrians tend to aggregate to the formed self-organized lanes, which makes the lanes wider and the lane number reduced. Interestingly, moderate enlargement (of the influential area) will reduce conflicts significantly, while excessive enlargement will lead to an increase in conflicts. We also discuss the predictive effect toward the smooth degree of entrance. When there are some formed self-organized lanes in the system, the effect is significant, and it will make the lanes more regular and stable, while when the existing lanes are unstable, the effect has little impact on the system. (paper)

  4. 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)

  5. Electronic self-organization in layered transition metal dichalcogenides

    Energy Technology Data Exchange (ETDEWEB)

    Ritschel, Tobias

    2015-10-30

    The interplay between different self-organized electronically ordered states and their relation to unconventional electronic properties like superconductivity constitutes one of the most exciting challenges of modern condensed matter physics. In the present thesis this issue is thoroughly investigated for the prototypical layered material 1T-TaS{sub 2} both experimentally and theoretically. At first the static charge density wave order in 1T-TaS{sub 2} is investigated as a function of pressure and temperature by means of X-ray diffraction. These data indeed reveal that the superconductivity in this material coexists with an inhomogeneous charge density wave on a macroscopic scale in real space. This result is fundamentally different from a previously proposed separation of superconducting and insulating regions in real space. Furthermore, the X-ray diffraction data uncover the important role of interlayer correlations in 1T-TaS{sub 2}. Based on the detailed insights into the charge density wave structure obtained by the X-ray diffraction experiments, density functional theory models are deduced in order to describe the electronic structure of 1T-TaS{sub 2} in the second part of this thesis. As opposed to most previous studies, these calculations take the three-dimensional character of the charge density wave into account. Indeed the electronic structure calculations uncover complex orbital textures, which are interwoven with the charge density wave order and cause dramatic differences in the electronic structure depending on the alignment of the orbitals between neighboring layers. Furthermore, it is demonstrated that these orbital-mediated effects provide a route to drive semiconductor-to-metal transitions with technologically pertinent gaps and on ultrafast timescales. These results are particularly relevant for the ongoing development of novel, miniaturized and ultrafast devices based on layered transition metal dichalcogenides. The discovery of orbital textures

  6. Self-organization processes in field-invasion team sports : implications for leadership.

    Science.gov (United States)

    Passos, Pedro; Araújo, Duarte; Davids, Keith

    2013-01-01

    In nature, the interactions between agents in a complex system (fish schools; colonies of ants) are governed by information that is locally created. Each agent self-organizes (adjusts) its behaviour, not through a central command centre, but based on variables that emerge from the interactions with other system agents in the neighbourhood. Self-organization has been proposed as a mechanism to explain the tendencies for individual performers to interact with each other in field-invasion sports teams, displaying functional co-adaptive behaviours, without the need for central control. The relevance of self-organization as a mechanism that explains pattern-forming dynamics within attacker-defender interactions in field-invasion sports has been sustained in the literature. Nonetheless, other levels of interpersonal coordination, such as intra-team interactions, still raise important questions, particularly with reference to the role of leadership or match strategies that have been prescribed in advance by a coach. The existence of key properties of complex systems, such as system degeneracy, nonlinearity or contextual dependency, suggests that self-organization is a functional mechanism to explain the emergence of interpersonal coordination tendencies within intra-team interactions. In this opinion article we propose how leadership may act as a key constraint on the emergent, self-organizational tendencies of performers in field-invasion sports.

  7. Self-organization theories and environmental management: The case of South Moresby, Canada

    Science.gov (United States)

    Grzybowski, Alex G. S.; Slocombe, D. Scott

    1988-07-01

    This article presents a new approach to the analysis and management of large-scale societal problems with complex ecological, economic, and social dimensions. The approach is based on the theory of self-organizing systems—complex, open, far-from-equilibrium systems with nonlinear dynamics. A brief overview and comparison of different self-organization theories (synergetics, self-organization theory, hypercycles, and autopoiesis) is presented in order to isolate the key characteristics of such systems. The approach is used to develop an analysis of the landuse controversy in the South Moresby area of the Queen Charlotte Islands, British Columbia, Canada. Critical variables are identified for each subsystem and classified by spatial and temporal scale, and discussed in terms of information content and internal/external origin. Eradication of sea otters, introduction of black-tailed deer, impacts of large-scale clearcut logging, sustainability of the coastal forest industry, and changing relations between native peoples and governments are discussed in detail to illustrate the system dynamics of the South Moresby “sociobiophysical” system. Finally, implications of the self-organizing sociobiophysical system view for regional analysis and management are identified.

  8. Order out of Randomness: Self-Organization Processes in Astrophysics

    Science.gov (United States)

    Aschwanden, Markus J.; Scholkmann, Felix; Béthune, William; Schmutz, Werner; Abramenko, Valentina; Cheung, Mark C. M.; Müller, Daniel; Benz, Arnold; Chernov, Guennadi; Kritsuk, Alexei G.; Scargle, Jeffrey D.; Melatos, Andrew; Wagoner, Robert V.; Trimble, Virginia; Green, William H.

    2018-03-01

    Self-organization is a property of dissipative nonlinear processes that are governed by a global driving force and a local positive feedback mechanism, which creates regular geometric and/or temporal patterns, and decreases the entropy locally, in contrast to random processes. Here we investigate for the first time a comprehensive number of (17) self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous " order out of randomness", during the evolution from an initially disordered system to an ordered quasi-stationary system, mostly by quasi-periodic limit-cycle dynamics, but also by harmonic (mechanical or gyromagnetic) resonances. The global driving force can be due to gravity, electromagnetic forces, mechanical forces (e.g., rotation or differential rotation), thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational (Balbus-Hawley) instability, the convective (Rayleigh-Bénard) instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or a loss-cone instability. Physical models of astrophysical self-organization processes require hydrodynamic, magneto-hydrodynamic (MHD), plasma, or N-body simulations. Analytical formulations of self-organizing systems generally involve coupled differential equations with limit-cycle solutions of the Lotka-Volterra or Hopf-bifurcation type.

  9. 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.

  10. Impact of network topology on self-organized criticality

    Science.gov (United States)

    Hoffmann, Heiko

    2018-02-01

    The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.

  11. Surface self-organization in multilayer film coatings

    Science.gov (United States)

    Shuvalov, Gleb M.; Kostyrko, Sergey A.

    2017-12-01

    It is a recognized fact that during film deposition and subsequent thermal processing the film surface evolves into an undulating profile. Surface roughness affects many important aspects in the engineering application of thin film materials such as wetting, heat transfer, mechanical, electromagnetic and optical properties. To accurately control the morphological surface modifications at the micro- and nanoscale and improve manufacturing techniques, we design a mathematical model of the surface self-organization process in multilayer film materials. In this paper, we consider a solid film coating with an arbitrary number of layers under plane strain conditions. The film surface has a small initial perturbation described by a periodic function. It is assumed that the evolution of the surface relief is governed by surface and volume diffusion. Based on Gibbs thermodynamics and linear theory of elasticity, we present a procedure for constructing a governing equation that gives the amplitude change of the surface perturbation with time. A parametric study of the evolution equation leads to the definition of a critical undulation wavelength that stabilizes the surface. As a numerical result, the influence of geometrical and physical parameters on the morphological stability of an isotropic two-layered film coating is analyzed.

  12. Self-organization of social hierarchy on interaction networks

    International Nuclear Information System (INIS)

    Fujie, Ryo; Odagaki, Takashi

    2011-01-01

    In order to examine the effects of interaction network structures on the self-organization of social hierarchy, we introduce the agent-based model: each individual as on a node of a network has its own power and its internal state changes by fighting with its neighbors and relaxation. We adopt three different networks: regular lattice, small-world network and scale-free network. For the regular lattice, we find the emergence of classes distinguished by the internal state. The transition points where each class emerges are determined analytically, and we show that each class is characterized by the local ranking relative to their neighbors. We also find that the antiferromagnetic-like configuration emerges just above the critical point. For the heterogeneous networks, individuals become winners (or losers) in descending order of the number of their links. By using mean-field analysis, we reveal that the transition point is determined by the maximum degree and the degree distribution in its neighbors

  13. Self-organized criticality in developing neuronal networks.

    Directory of Open Access Journals (Sweden)

    Christian Tetzlaff

    Full Text Available Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV of cortical cell cultures (n = 20 and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV is followed by a supercritical (≈20 DIV and then a subcritical one (≈36 DIV until the network finally reaches stable criticality (≈58 DIV. Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.

  14. Benefits of Self-Organizing Networks (SON for Mobile Operators

    Directory of Open Access Journals (Sweden)

    Olav Østerbø

    2012-01-01

    Full Text Available Self-Organizing Networks (SON is a collection of functions for automatic configuration, optimization, diagnostisation and healing of cellular networks. It is considered to be a necessity in future mobile networks and operations due to the increased cost pressure. The main drivers are essentially to reduce CAPEX and OPEX, which would otherwise increase dramatically due to increased number of network parameters that has to be monitored and set, the rapidly increasing numbers of base stations in the network and parallel operation of 2G, 3G and Evolved Packet Core (EPC infrastructures. This paper presents evaluations on the use of some of the most important SON components. Mobile networks are getting more complex to configure, optimize and maintain. Many SON functions will give cost savings and performance benefits from the very beginning of a network deployment and these should be prioritized now. But even if many functions are already available and can give large benefits, the field is still in its infancy and more advanced functions are either not yet implemented or have immature implementations. It is therefore necessary to have a strategy for how and when different SON functions should be introduced in mobile networks.

  15. Thought analysis on self-organization theories of MHD plasma

    International Nuclear Information System (INIS)

    Kondoh, Yoshiomi; Sato, Tetsuya.

    1992-08-01

    A thought analysis on the self-organization theories of dissipative MHD plasma is presented to lead to three groups of theories that lead to the same relaxed state of ∇ x B = λB, in order to find an essential physical picture embedded in the self-organization phenomena due to nonlinear and dissipative processes. The self-organized relaxed state due to the dissipation by the Ohm loss is shown to be formulated generally as the state such that yields the minimum dissipation rate of global auto-and/or cross-correlations between two quantities in j, B, and A for their own instantaneous values of the global correlations. (author)

  16. 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

  17. Self-Organization during Friction in Complex Surface Engineered Tribosystems

    Directory of Open Access Journals (Sweden)

    Ben D. Beake

    2010-02-01

    Full Text Available Self-organization during friction in complex surface engineered tribosystems is investigated. The probability of self-organization in these complex tribosystems is studied on the basis of the theoretical concepts of irreversible thermodynamics. It is shown that a higher number of interrelated processes within the system result in an increased probability of self-organization. The results of this thermodynamic model are confirmed by the investigation of the wear performance of a novel Ti0.2Al0.55Cr0.2Si0.03Y0.02N/Ti0.25Al0.65Cr0.1N (PVD coating with complex nano-multilayered structure under extreme tribological conditions of dry high-speed end milling of hardened H13 tool steel.

  18. The Logic of Self-Organized Criticality

    Directory of Open Access Journals (Sweden)

    Bakhtiyarov Kamil I.

    2015-07-01

    Full Text Available A consideration of non-classical logic in terms of classical one allows us to show a role of designated truth values. In this way we show that our version of non-classical many-valued logic can be based on the structure of genetic code.

  19. 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

  20. 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.

  1. 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.

  2. Self-organized natural roads for predicting traffic flow: a sensitivity study

    International Nuclear Information System (INIS)

    Jiang, Bin; Zhao, Sijian; Yin, Junjun

    2008-01-01

    In this paper, we extended road-based topological analysis to both nationwide and urban road networks, and concentrated on a sensitivity study with respect to the formation of self-organized natural roads based on the Gestalt principle of good continuity. Both annual average daily traffic (AADT) and global positioning system (GPS) data were used to correlate with a series of ranking metrics including five centrality-based metrics and two PageRank metrics. It was found that there exists a tipping point from segment-based to road-based network topology in terms of correlation between ranking metrics and their traffic. To our great surprise, (1) this correlation is significantly improved if a selfish rather than utopian strategy is adopted in forming the self-organized natural roads, and (2) point-based metrics assigned by summation into individual roads tend to have a much better correlation with traffic flow than line-based metrics. These counter-intuitive surprising findings constitute emergent properties of self-organized natural roads, which are intelligent enough for predicting traffic flow, thus shedding substantial light on the understanding of road networks and their traffic from the perspective of complex networks

  3. 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.

  4. Anomalous relaxation and self-organization in nonequilibrium processes

    International Nuclear Information System (INIS)

    Fatkullin, Ibrahim; Kladko, Konstantin; Mitkov, Igor; Bishop, A. R.

    2001-01-01

    We study thermal relaxation in ordered arrays of coupled nonlinear elements with external driving. We find that our model exhibits dynamic self-organization manifested in a universal stretched-exponential form of relaxation. We identify two types of self-organization, cooperative and anticooperative, which lead to fast and slow relaxation, respectively. We give a qualitative explanation for the behavior of the stretched exponent in different parameter ranges. We emphasize that this is a system exhibiting stretched-exponential relaxation without explicit disorder or frustration

  5. 5G heterogeneous networks self-organizing and optimization

    CERN Document Server

    Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing

    2016-01-01

    This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.

  6. Complexity in plasma. A grand view of self-organization

    International Nuclear Information System (INIS)

    Sato, Tetsuya.

    1994-11-01

    The central theme of the Complexity is the inquest of the creation of ordered structure in nature. Extensive computer simulations on plasmas have revealed that self-organization is governed by the three key processes, i.e. energy pumping, entropy expulsion and nonlinearity. A system exhibits characteristically different 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. (author)

  7. TWO CHANNELS OF SELF-ORGANIZATION OF IONIZED GASEOUS MEDIA

    Directory of Open Access Journals (Sweden)

    Benedict Oprescu

    2013-12-01

    Full Text Available The appearance is pointed out, experimentally, of a complex electric charge structure, within an ionized gas, relatively homogeneous at first, under the influence of a number of external constraints. Two different mechanisms of self-organization are presented: the former implying, essentially, long-range interactions, and the latter implying, essentially, short-range quantum interactions. The phenomenological scenarios are presented, which underlie the two mechanisms of self-organization, as well as the broader theoretical frame, currently accepted, concerning the generation of complexity in the material media that are far from the state of thermodynamic equilibrium.

  8. Activity Based Costing in Value Stream Mapping

    Directory of Open Access Journals (Sweden)

    S. S. Abuthakeer

    2010-12-01

    Full Text Available This paper attempts to integrate Value Stream Map (VSM with the cost aspects. A value stream map provides a blueprint for implementing lean manufacturing concepts by illustrating information and materials flow in a value stream. The objective of the present work is to integrate the various cost aspects. The idea is to introduce a cost line, which enhances the clarity in decision making. The redesigned map proves to be effective in highlighting the improvement areas, in terms of quantitative data. TAKT time calculation is carried out to set the pace of production. Target cost is set as a bench mark for product cost. The results of the study indicates that implementing VSM led to reduction in the following areas: processing lead time by 34%, processing cycle time was reduced by 35%, Inventory level by 66% and product cost from Rs 137 to Rs 125. It was found that adopting VSM in a small scale industry can make significant improvements.

  9. Hierarchical self-organization of non-cooperating individuals.

    Directory of Open Access Journals (Sweden)

    Tamás Nepusz

    Full Text Available Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.

  10. Iris Matching Based on Personalized Weight Map.

    Science.gov (United States)

    Dong, Wenbo; Sun, Zhenan; Tan, Tieniu

    2011-09-01

    Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.

  11. Mapping genomic deletions down to the base

    DEFF Research Database (Denmark)

    Dunø, Morten; Hove, Hanne; Kirchhoff, Maria

    2004-01-01

    the breakpoint of the third patient was mapped to a region previously predicted to be prone for rearrangements. One patient also harboured an inversion in connection with the deletion that disrupted the HDAC9 gene. All three patients showed clinical characteristics reminiscent of the hand-foot-genital syndrome...

  12. State Base Map for GIS – New Digital Topographic Map of the Republic of Macedonia

    Directory of Open Access Journals (Sweden)

    Zlatko Srbinoski

    2009-12-01

    Full Text Available The basic aim of the National Spatial Data Infrastructure (NSDI built in accordance with INSPIRE directive is to standardize spatial data infrastructure on national level. In that direction, topographic maps are a basic platform for acquiring spatial data within geoinformation systems and one of the most important  segments of NSDI. This paper presents methodology of establishing the new digital topographic map of the Republic of Macedonia titled “State Base Map for GIS in Macedonia”. This paper analyzes geometrical accuracy of new digital topographic maps. Production of the new digital topographic map has been the most important cartographic project in the Republic of Macedonia since it became independent.

  13. Self-Organization and Annealed Disorder in a Fracturing Process

    DEFF Research Database (Denmark)

    Caldarelli, Guido; Di Tolla, Francesco; Petri, Alberto

    1996-01-01

    We show that a vectorial model for inhomogeneous elastic media self-organizes under external stress. An onset of crack avalanches of every duration and length scale compatible with the lattice size is observed. The behavior is driven by the introduction of annealed disorder, i.e., by lowering...... condition for reproducing the algebraic distribution of the energy released during cracks formation....

  14. Eco-evolutionary feedbacks in self-organized ecosystems

    NARCIS (Netherlands)

    de Jager, M.

    2015-01-01

    Spatial patterns in natural systems may appear amazingly complex. Yet, they can often be explained by a few simple rules. In self-organized ecosystems, complex spatial patterns at the ecosystem scale arise as the consequence of actions of and interactions between organisms at a local scale.

  15. Self-organized criticality in a network of interacting neurons

    NARCIS (Netherlands)

    Cowan, J.D.; Neuman, J.; Kiewiet, B.; van Drongelen, W.

    2013-01-01

    This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway.

  16. Co-operation and Self-Organization

    Directory of Open Access Journals (Sweden)

    Christian Fuchs

    2008-07-01

    structures. This today culminates in global problems such as the ecological crisis, high risk technologies, poverty, unemployment, wars, armed conflicts, terrorism, etc. In order to solve these problems our social systems need re-design in terms of ecological sustainability, alliance technology, participatory economy, participatory democracy, and participatory culture. Participation is an integrated notion that is based on co-operation, selfdetermination, and inclusion in multiple dimensions. A system can be considered as participatory if power in the system is distributed in such a way that all members and concerned individuals can own the system co-operatively and can produce, decide and live in the system co-operatively. Participation is frequently understood in the very narrow sense of concerned people taking somehow part in decision processes. Such an understanding is limited to the political dimension and says nothing about the scope and dimension of participation. There are several dimensions of participation in a social system or in society: producing, owning, consuming (economic dimension, deciding, goal-setting, evaluating (political dimension, forming knowledge/norms/values/images/visions, communicating, networking, self-realizing (cultural dimension. Participation in each of these ten dimensions can be low, medium or high/full. The participation matrix describes the degree of participation in an organization/society with the help of the three dimensions of economy, politics and culture and an analysis of the scope of participation (economic, political, cultural.

  17. Generalized logistic map and its application in chaos based cryptography

    Science.gov (United States)

    Lawnik, M.

    2017-12-01

    The logistic map is commonly used in, for example, chaos based cryptography. However, its properties do not render a safe construction of encryption algorithms. Thus, the scope of the paper is a proposal of generalization of the logistic map by means of a wellrecognized family of chaotic maps. In the next step, an analysis of Lyapunov exponent and the distribution of the iterative variable are studied. The obtained results confirm that the analyzed model can safely and effectively replace a classic logistic map for applications involving chaotic cryptography.

  18. Method for Stereo Mapping Based on Objectarx and Pipeline Technology

    Science.gov (United States)

    Liu, F.; Chen, T.; Lin, Z.; Yang, Y.

    2012-07-01

    Stereo mapping is an important way to acquire 4D production. Based on the development of the stereo mapping and the characteristics of ObjectARX and pipeline technology, a new stereo mapping scheme which can realize the interaction between the AutoCAD and digital photogrammetry system is offered by ObjectARX and pipeline technology. An experiment is made in order to make sure the feasibility with the example of the software MAP-AT (Modern Aerial Photogrammetry Automatic Triangulation), the experimental results show that this scheme is feasible and it has very important meaning for the realization of the acquisition and edit integration.

  19. On the nature and shape of tubulin trails: implications on microtubule self-organization.

    Science.gov (United States)

    Glade, Nicolas

    2012-06-01

    Microtubules, major elements of the cell skeleton are, most of the time, well organized in vivo, but they can also show self-organizing behaviors in time and/or space in purified solutions in vitro. Theoretical studies and models based on the concepts of collective dynamics in complex systems, reaction-diffusion processes and emergent phenomena were proposed to explain some of these behaviors. In the particular case of microtubule spatial self-organization, it has been advanced that microtubules could behave like ants, self-organizing by 'talking to each other' by way of hypothetic (because never observed) concentrated chemical trails of tubulin that are expected to be released by their disassembling ends. Deterministic models based on this idea yielded indeed like-looking spatio-temporal self-organizing behaviors. Nevertheless the question remains of whether microscopic tubulin trails produced by individual or bundles of several microtubules are intense enough to allow microtubule self-organization at a macroscopic level. In the present work, by simulating the diffusion of tubulin in microtubule solutions at the microscopic scale, we measure the shape and intensity of tubulin trails and discuss about the assumption of microtubule self-organization due to the production of chemical trails by disassembling microtubules. We show that the tubulin trails produced by individual microtubules or small microtubule arrays are very weak and not elongated even at very high reactive rates. Although the variations of concentration due to such trails are not significant compared to natural fluctuations of the concentration of tubuline in the chemical environment, the study shows that heterogeneities of biochemical composition can form due to microtubule disassembly. They could become significant when produced by numerous microtubule ends located in the same place. Their possible formation could play a role in certain conditions of reaction. In particular, it gives a mesoscopic

  20. Broadband illusion optical devices based on conformal mappings

    Science.gov (United States)

    Xiong, Zhan; Xu, Lin; Xu, Ya-Dong; Chen, Huan-Yang

    2017-10-01

    In this paper, we propose a simple method of illusion optics based on conformal mappings. By carefully developing designs with specific conformal mappings, one can make an object look like another with a significantly different shape. In addition, the illusion optical devices can work in a broadband of frequencies.

  1. Self-organization in the tornado: the new approach in the tornado description

    OpenAIRE

    Bystrai, G. P.; Lykov, I. A

    2012-01-01

    For the mathematical modeling of highly non-equilibrium and nonlinear processes in a tornado in this paper a new approach based on nonlinear equations of momentum transfer with function of sources and sinks is suggested. In constructing the model thermodynamic description is used, which is not entered before and allows discovering new principles of self-organization in a tornado. This approach gives fairly consistent physical results. This is an attempt to answer some fundamental questions co...

  2. Crossover to self-organized criticality in an inertial sandpile model

    OpenAIRE

    Head, DA; Rodgers, GJ

    1996-01-01

    We introduce a one-dimensional sandpile model which incorporates particle inertia. The inertial dynamics are governed by a new parameter which, as it passes through a threshold value, alters the toppling dynamics in such a way that the system no longer evolves to a self-organized critical state. A range of mean-field theories based on a kinetic equation approach is presented which confirm the numerical findings. We conclude by considering the physical applications of this model, particularly ...

  3. The microscopic origin of self-organized nanostripe pattern formation on an electropolished aluminium surface

    International Nuclear Information System (INIS)

    Sarkar, Jaya; Basumallick, A; Khan, Gobinda Gopal

    2009-01-01

    By correlating the experimental evidence obtained from atomic force microscopy, conventional x-ray diffraction, and a surface sensitive modified x-ray diffraction technique with the results of density functional theory based computations, we demonstrate that self-organized nanostripe patterns formed on the electropolished surface of aluminium originate as a consequence of relaxation and reconstruction of the new surfaces exposed and textural changes at the surface caused by the dissolution during polishing.

  4. Measurable realistic image-based 3D mapping

    Science.gov (United States)

    Liu, W.; Wang, J.; Wang, J. J.; Ding, W.; Almagbile, A.

    2011-12-01

    Maps with 3D visual models are becoming a remarkable feature of 3D map services. High-resolution image data is obtained for the construction of 3D visualized models.The3D map not only provides the capabilities of 3D measurements and knowledge mining, but also provides the virtual experienceof places of interest, such as demonstrated in the Google Earth. Applications of 3D maps are expanding into the areas of architecture, property management, and urban environment monitoring. However, the reconstruction of high quality 3D models is time consuming, and requires robust hardware and powerful software to handle the enormous amount of data. This is especially for automatic implementation of 3D models and the representation of complicated surfacesthat still need improvements with in the visualisation techniques. The shortcoming of 3D model-based maps is the limitation of detailed coverage since a user can only view and measure objects that are already modelled in the virtual environment. This paper proposes and demonstrates a 3D map concept that is realistic and image-based, that enables geometric measurements and geo-location services. Additionally, image-based 3D maps provide more detailed information of the real world than 3D model-based maps. The image-based 3D maps use geo-referenced stereo images or panoramic images. The geometric relationships between objects in the images can be resolved from the geometric model of stereo images. The panoramic function makes 3D maps more interactive with users but also creates an interesting immersive circumstance. Actually, unmeasurable image-based 3D maps already exist, such as Google street view, but only provide virtual experiences in terms of photos. The topographic and terrain attributes, such as shapes and heights though are omitted. This paper also discusses the potential for using a low cost land Mobile Mapping System (MMS) to implement realistic image 3D mapping, and evaluates the positioning accuracy that a measureable

  5. Approach of simultaneous localization and mapping based on local maps for robot

    Institute of Scientific and Technical Information of China (English)

    CHEN Bai-fan; CAI Zi-xing; HU De-wen

    2006-01-01

    An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps.A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.

  6. 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.

  7. 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.

  8. Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory

    Directory of Open Access Journals (Sweden)

    William A. Phillips

    2012-01-01

    Full Text Available This paper discusses concepts of self-organized complexity and the theory of Coherent Infomax in the light of Jaynes’s probability theory. Coherent Infomax, shows, in principle, how adaptively self-organized complexity can be preserved and improved by using probabilistic inference that is context-sensitive. It argues that neural systems do this by combining local reliability with flexible, holistic, context-sensitivity. Jaynes argued that the logic of probabilistic inference shows it to be based upon Bayesian and Maximum Entropy methods or special cases of them. He presented his probability theory as the logic of science; here it is considered as the logic of life. It is concluded that the theory of Coherent Infomax specifies a general objective for probabilistic inference, and that contextual interactions in neural systems perform functions required of the scientist within Jaynes’s theory.

  9. Self-organization processes and nanocluster formation in crystal lattices by low-energy ion irradiation

    International Nuclear Information System (INIS)

    Tereshko, I.; Abidzina, V.; Tereshko, A.; Glushchenko, V.; Elkin, I.

    2007-01-01

    The goal of this paper is to study self-organization processes that cause nanostructural evolution in nonlinear crystal media. The subjects of the investigation were nonlinear homogeneous and heterogeneous atom chains. The method of computer simulation was used to investigate the interaction between low-energy ions and crystal lattices. It was based on the conception of three-dimensional lattice as a nonlinear atom chain system. We showed that that in homogeneous atom chains critical energy needed for self-organization processes development is less than for nonlinear atom chain with already embedded clusters. The possibility of nanostructure formation was studied by a molecular dynamics method of nonlinear oscillations in atomic oscillator systems of crystal lattices after their low-energy ion irradiation. (authors)

  10. Self-organization principles result in robust control of flexible manufacturing systems

    DEFF Research Database (Denmark)

    Nature shows us in our daily life how robust, flexible and optimal self-organized modular constructions work in complex physical, chemical and biological systems, which successfully adapt to new and unexpected situations. A promising strategy is therefore to use such self-organization and pattern...... problems with several autonomous robots and several targets are considered as model of flexible manufacturing systems. Each manufacturing target has to be served in a given time interval by one and only one robot and the total working costs have to be minimized (or total winnings maximized). A specifically...... constructed dynamical system approach (coupled selection equations) is used which is based on pattern formation principles and results in fault resistant and robust behaviour. An important feature is that this type of control also guarantees feasiblitiy of the assignment solutions. In previous work...

  11. 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

  12. Information and Self-Organization A Macroscopic Approach to Complex Systems

    CERN Document Server

    Haken, Hermann

    2006-01-01

    This book presents the concepts needed to deal with self-organizing complex systems from a unifying point of view that uses macroscopic data. The various meanings of the concept "information" are discussed and a general formulation of the maximum information (entropy) principle is used. With the aid of results from synergetics, adequate objective constraints for a large class of self-organizing systems are formulated and examples are given from physics, life and computer science. The relationship to chaos theory is examined and it is further shown that, based on possibly scarce and noisy data, unbiased guesses about processes of complex systems can be made and the underlying deterministic and random forces determined. This allows for probabilistic predictions of processes, with applications to numerous fields in science, technology, medicine and economics. The extensions of the third edition are essentially devoted to an introduction to the meaning of information in the quantum context. Indeed, quantum inform...

  13. Pseudo-random bit generator based on Chebyshev map

    Science.gov (United States)

    Stoyanov, B. P.

    2013-10-01

    In this paper, we study a pseudo-random bit generator based on two Chebyshev polynomial maps. The novel derivative algorithm shows perfect statistical properties established by number of statistical tests.

  14. Object-based Landslide Mapping: Examples, Challenges and Opportunities

    Science.gov (United States)

    Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

    2016-04-01

    Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

  15. A Lithology Based Map Unit Schema For Onegeology Regional Geologic Map Integration

    Science.gov (United States)

    Moosdorf, N.; Richard, S. M.

    2012-12-01

    A system of lithogenetic categories for a global lithological map (GLiM, http://www.ifbm.zmaw.de/index.php?id=6460&L=3) has been compiled based on analysis of lithology/genesis categories for regional geologic maps for the entire globe. The scheme is presented for discussion and comment. Analysis of units on a variety of regional geologic maps indicates that units are defined based on assemblages of rock types, as well as their genetic type. In this compilation of continental geology, outcropping surface materials are dominantly sediment/sedimentary rock; major subdivisions of the sedimentary category include clastic sediment, carbonate sedimentary rocks, clastic sedimentary rocks, mixed carbonate and clastic sedimentary rock, colluvium and residuum. Significant areas of mixed igneous and metamorphic rock are also present. A system of global categories to characterize the lithology of regional geologic units is important for Earth System models of matter fluxes to soils, ecosystems, rivers and oceans, and for regional analysis of Earth surface processes at global scale. Because different applications of the classification scheme will focus on different lithologic constituents in mixed units, an ontology-type representation of the scheme that assigns properties to the units in an analyzable manner will be pursued. The OneGeology project is promoting deployment of geologic map services at million scale for all nations. Although initial efforts are commonly simple scanned map WMS services, the intention is to move towards data-based map services that categorize map units with standard vocabularies to allow use of a common map legend for better visual integration of the maps (e.g. see OneGeology Europe, http://onegeology-europe.brgm.fr/ geoportal/ viewer.jsp). Current categorization of regional units with a single lithology from the CGI SimpleLithology (http://resource.geosciml.org/201202/ Vocab2012html/ SimpleLithology201012.html) vocabulary poorly captures the

  16. Self-organized lattice of ordered quantum dot molecules

    International Nuclear Information System (INIS)

    Lippen, T. von; Noetzel, R.; Hamhuis, G.J.; Wolter, J.H.

    2004-01-01

    Ordered groups of InAs quantum dots (QDs), lateral QD molecules, are created by self-organized anisotropic strain engineering of a (In,Ga)As/GaAs superlattice (SL) template on GaAs (311)B in molecular-beam epitaxy. During stacking, the SL template self-organizes into a two-dimensionally ordered strain modulated network on a mesoscopic length scale. InAs QDs preferentially grow on top of the nodes of the network due to local strain recognition. The QDs form a lattice of separated groups of closely spaced ordered QDs whose number can be controlled by the GaAs separation layer thickness on top of the SL template. The QD groups exhibit excellent optical properties up to room temperature

  17. SOUNET: Self-Organized Underwater Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hee-won Kim

    2017-02-01

    Full Text Available In this paper, we propose an underwater wireless sensor network (UWSN named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the timevarying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR, and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.

  18. SOUNET: Self-Organized Underwater Wireless Sensor Network.

    Science.gov (United States)

    Kim, Hee-Won; Cho, Ho-Shin

    2017-02-02

    In this paper, we propose an underwater wireless sensor network (UWSN) named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the timevarying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.

  19. How nature works the science of self-organized criticality

    CERN Document Server

    Bak, Per

    1996-01-01

    This is an acclaimed book intended for the general reader who is interested in science. The author is a physicist who is well-known for his development of the property called "self-organized criticality", a property or phenomenon that lies at the heart of large dynamical systems. It can be used to analyse systems that are complicated, and which are part of the new science of complexity. It is a unifying concept that can be used to study phenomena in fields as diverse as economics, astronomy, the earth sciences, and physics. The author discusses his discovery of self-organized criticality; its relation to the world of classical physics; computer simulations and experiments which aid scientists' understanding of the property; and the relation of the subject to popular areas such as fractal geometry and power laws; cellular automata, and a wide range of practical applications.

  20. 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.

  1. Energy sources, self-organization, and the origin of life.

    Science.gov (United States)

    Boiteau, Laurent; Pascal, Robert

    2011-02-01

    The emergence and early developments of life are considered from the point of view that contingent events that inevitably marked evolution were accompanied by deterministic driving forces governing the selection between different alternatives. Accordingly, potential energy sources are considered for their propensity to induce self-organization within the scope of the chemical approach to the origin of life. Requirements in terms of quality of energy locate thermal or photochemical activation in the atmosphere as highly likely processes for the formation of activated low-molecular weight organic compounds prone to induce biomolecular self-organization through their ability to deliver quanta of energy matching the needs of early biochemical pathways or the reproduction of self-replicating entities. These lines of reasoning suggest the existence of a direct connection between the free energy content of intermediates of early pathways and the quanta of energy delivered by available sources of energy.

  2. Self-Organized Criticality of Rainfall in Central China

    Directory of Open Access Journals (Sweden)

    Zhiliang Wang

    2012-01-01

    Full Text Available Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China.

  3. Mimosa Origami: A nanostructure-enabled directional self-organization regime of materials

    Science.gov (United States)

    Wong, William S. Y.; Li, Minfei; Nisbet, David R.; Craig, Vincent S. J.; Wang, Zuankai; Tricoli, Antonio

    2016-01-01

    One of the innate fundamentals of living systems is their ability to respond toward distinct stimuli by various self-organization behaviors. Despite extensive progress, the engineering of spontaneous motion in man-made inorganic materials still lacks the directionality and scale observed in nature. We report the directional self-organization of soft materials into three-dimensional geometries by the rapid propagation of a folding stimulus along a predetermined path. We engineer a unique Janus bilayer architecture with superior chemical and mechanical properties that enables the efficient transformation of surface energy into directional kinetic and elastic energies. This Janus bilayer can respond to pinpoint water stimuli by a rapid, several-centimeters-long self-assembly that is reminiscent of the Mimosa pudica’s leaflet folding. The Janus bilayers also shuttle water at flow rates up to two orders of magnitude higher than traditional wicking-based devices, reaching velocities of 8 cm/s and flow rates of 4.7 μl/s. This self-organization regime enables the ease of fabricating curved, bent, and split flexible channels with lengths greater than 10 cm, demonstrating immense potential for microfluidics, biosensors, and water purification applications. PMID:28861471

  4. Discerning Thermodynamic Basis of Self-Organization in Critical Zone Structure and Function

    Science.gov (United States)

    Richardson, M.; Kumar, P.

    2017-12-01

    Self-organization characterizes the spontaneous emergence of order. Self-organization in the Critical Zone, the region of Earth's skin from below the groundwater table to the top of the vegetation canopy, involves the interaction of biotic and abiotic processes occurring through a hierarchy of temporal and spatial scales. The self-organization is sustained through input of energy and material in an open system framework, and the resulting formations are called dissipative structures. Why do these local states of organization form and how are they thermodynamically favorable? We hypothesize that structure formation is linked to energy conversion and matter throughput rates across driving gradients. Furthermore, we predict that structures in the Critical Zone evolve based on local availability of nutrients, water, and energy. By considering ecosystems as open thermodynamic systems, we model and study the throughput signatures on short times scales to determine origins and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of matter and energy to understand the thermodynamic drivers of the system. By classifying the fluxes and dynamics in a system, we can identify patterns to determine the thermodynamic drivers for organized states. Additionally, studying the partitioning of nutrients, water, and energy throughout ecosystems through dissipative structures will help identify reasons for structure shapes and how these shapes impact major Critical Zone functions.

  5. a Mapping Method of Slam Based on Look up Table

    Science.gov (United States)

    Wang, Z.; Li, J.; Wang, A.; Wang, J.

    2017-09-01

    In the last years several V-SLAM(Visual Simultaneous Localization and Mapping) approaches have appeared showing impressive reconstructions of the world. However these maps are built with far more than the required information. This limitation comes from the whole process of each key-frame. In this paper we present for the first time a mapping method based on the LOOK UP TABLE(LUT) for visual SLAM that can improve the mapping effectively. As this method relies on extracting features in each cell divided from image, it can get the pose of camera that is more representative of the whole key-frame. The tracking direction of key-frames is obtained by counting the number of parallax directions of feature points. LUT stored all mapping needs the number of cell corresponding to the tracking direction which can reduce the redundant information in the key-frame, and is more efficient to mapping. The result shows that a better map with less noise is build using less than one-third of the time. We believe that the capacity of LUT efficiently building maps makes it a good choice for the community to investigate in the scene reconstruction problems.

  6. Self-Organized Criticality and $1/f$ Noise in Traffic

    OpenAIRE

    Paczuski, Maya; Nagel, Kai

    1996-01-01

    Phantom traffic jams may emerge ``out of nowhere'' from small fluctuations rather than being triggered by large, exceptional events. We show how phantom jams arise in a model of single lane highway traffic, which mimics human driving behavior. Surprisingly, the optimal state of highest efficiency, with the largest throughput, is a critical state with traffic jams of all sizes. We demonstrate that open systems self-organize to the most efficient state. In the model we study, this critical stat...

  7. Self-organization analysis for a nonlocal convective Fisher equation

    Energy Technology Data Exchange (ETDEWEB)

    Cunha, J.A.R. da [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Penna, A.L.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)], E-mail: penna.andre@gmail.com; Vainstein, M.H. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Morgado, R. [International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Departamento de Matematica, Universidade de Brasilia, 70910-900 Brasilia DF (Brazil); Oliveira, F.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)

    2009-02-02

    Using both an analytical method and a numerical approach we have investigated pattern formation for a nonlocal convective Fisher equation with constant and spatial velocity fields. We analyze the limits of the influence function due to nonlocal interaction and we obtain the phase diagram of critical velocities v{sub c} as function of the width {mu} of the influence function, which characterize the self-organization of a finite system.

  8. General fluid theories, variational principles and self-organization

    International Nuclear Information System (INIS)

    Mahajan, S.M.

    2002-01-01

    This paper reports two distinct but related advances: (1) The development and application of fluid theories that transcend conventional magnetohydrodynamics (MHD), in particular, theories that are valid in the long-mean-free-path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently. (2) The discovery of new pressure-confining plasma configurations that are self-organized relaxed states. (author)

  9. Structures formation through self-organized accretion on cosmic strings

    International Nuclear Information System (INIS)

    Murdzek, R.

    2009-01-01

    In this paper, we shall show that the formation of structures through accretion by a cosmic string is driven by a natural feed-back mechanism: a part of the energy radiated by accretions creates a pressure on the accretion disk itself. This phenomenon leads to a nonlinear evolution of the accretion process. Thus, the formation of structures results as a consequence of a self-organized growth of the accreting central object.

  10. Self-organized vortex multiplets in swirling flow

    DEFF Research Database (Denmark)

    Okulov, Valery; Naumov, Igor; Sørensen, Jens Nørkær

    2008-01-01

    The possibility of double vortex multiplet formation at the center of an intensively swirling cocurrent flow generated in a cylindrical container by its rotating lid is reported for the first time. The boundary of the transition to unsteady flow regimes, which arise as a result of the equilibrium...... rotation of self-organized vortex multiplets (triplet, double triplet, double doublet, and quadruplet), has been experimentally determined for cylinders with the aspect (height to radius) ratios in a wider interval than that studied previously....

  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. Self-organization of polymerizable bolaamphiphiles bearing diacetylene mesogenic group.

    Science.gov (United States)

    Yin, Shouchun; Song, Bo; Liu, Guanqing; Wang, Zhiqiang; Zhang, Xi

    2007-05-22

    We report herein the synthesis of a series of polymerizable bolaamphiphiles containing a diacetylene group and mesogenic unit and their self-organization behaviors in bulk and at interface. The polymerizable bolaamphiphiles are noted as DPDA-n, where n refers to the spacer length of alkyl chain. DPDA-10 with suitable spacer length can self-organize into stable cylindrical micellar nanostructures, and these nanostructures have preferred orientation regionally when adsorbed at the mica/water interface. It is confirmed that the micellar nanostructure of DPDA-10 can be polymerized both in the bulk solution and in the film by UV irradiation. The emission property of DPDA-10 after UV irradiation has been significantly enhanced in comparison to that before polymerization, which may be due to the extension of the conjugated system arising from the transformation of the diacetylene group into polydiacetylene upon polymerization. In addition, the self-organization of DPDA-n is dependent on the spacer length. DPDA-7 with a short spacer length forms an irregular flat sheet structure with many defects; DPDA-15 with a long spacer length forms rodlike micellar structures. Thus, this work may provide a new approach for designing and fabricating organic functional nanostructured materials.

  13. A Game Map Complexity Measure Based on Hamming Distance

    Science.gov (United States)

    Li, Yan; Su, Pan; Li, Wenliang

    With the booming of PC game market, Game AI has attracted more and more researches. The interesting and difficulty of a game are relative with the map used in game scenarios. Besides, the path-finding efficiency in a game is also impacted by the complexity of the used map. In this paper, a novel complexity measure based on Hamming distance, called the Hamming complexity, is introduced. This measure is able to estimate the complexity of binary tileworld. We experimentally demonstrated that Hamming complexity is highly relative with the efficiency of A* algorithm, and therefore it is a useful reference to the designer when developing a game map.

  14. Map-based model of the cardiac action potential

    International Nuclear Information System (INIS)

    Pavlov, Evgeny A.; Osipov, Grigory V.; Chan, C.K.; Suykens, Johan A.K.

    2011-01-01

    A simple computationally efficient model which is capable of replicating the basic features of cardiac cell action potential is proposed. The model is a four-dimensional map and demonstrates good correspondence with real cardiac cells. Various regimes of cardiac activity, which can be reproduced by the proposed model, are shown. Bifurcation mechanisms of these regimes transitions are explained using phase space analysis. The dynamics of 1D and 2D lattices of coupled maps which model the behavior of electrically connected cells is discussed in the context of synchronization theory. -- Highlights: → Recent experimental-data based models are complicated for analysis and simulation. → The simplified map-based model of the cardiac cell is constructed. → The model is capable for replication of different types of cardiac activity. → The spatio-temporal dynamics of ensembles of coupled maps are investigated. → Received data are analyzed in context of biophysical processes in the myocardium.

  15. Map-based model of the cardiac action potential

    Energy Technology Data Exchange (ETDEWEB)

    Pavlov, Evgeny A., E-mail: genie.pavlov@gmail.com [Department of Computational Mathematics and Cybernetics, Nizhny Novgorod State University, 23, Gagarin Avenue, 603950 Nizhny Novgorod (Russian Federation); Osipov, Grigory V. [Department of Computational Mathematics and Cybernetics, Nizhny Novgorod State University, 23, Gagarin Avenue, 603950 Nizhny Novgorod (Russian Federation); Chan, C.K. [Institute of Physics, Academia Sinica, 128 Sec. 2, Academia Road, Nankang, Taipei 115, Taiwan (China); Suykens, Johan A.K. [K.U. Leuven, ESAT-SCD/SISTA, Kasteelpark Arenberg 10, B-3001 Leuven (Heverlee) (Belgium)

    2011-07-25

    A simple computationally efficient model which is capable of replicating the basic features of cardiac cell action potential is proposed. The model is a four-dimensional map and demonstrates good correspondence with real cardiac cells. Various regimes of cardiac activity, which can be reproduced by the proposed model, are shown. Bifurcation mechanisms of these regimes transitions are explained using phase space analysis. The dynamics of 1D and 2D lattices of coupled maps which model the behavior of electrically connected cells is discussed in the context of synchronization theory. -- Highlights: → Recent experimental-data based models are complicated for analysis and simulation. → The simplified map-based model of the cardiac cell is constructed. → The model is capable for replication of different types of cardiac activity. → The spatio-temporal dynamics of ensembles of coupled maps are investigated. → Received data are analyzed in context of biophysical processes in the myocardium.

  16. Base Closure: A Road Map for Completion

    Science.gov (United States)

    1991-03-22

    leadership . At the same time, he should issue written press releases. Selected senior leaders from the base should be briefed separately from key community...they will hold with other agencies on base. These matters should be the commander’s call; they will reflect his style of leadership and preference for...base closure. He alone must foresee what can most likely go wrong as well as what should go right in this arduous process. DCNSIZING AN ORGANIZATIN The

  17. Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems

    Science.gov (United States)

    Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

    2017-12-01

    Multiagent systems (MAS) provide a useful tool for exploring the complex dynamics and behavior of financial markets and now MAS approach has been widely implemented and documented in the empirical literature. This paper introduces the implementation of an innovative multi-scale mathematical model for a computational agent-based financial market. The paper develops a method to quantify the degree of self-organization which emerges in the system and shows that the capacity of self-organization is maximized when the agent behaviors are heterogeneous. Numerical results are presented and analyzed, showing how the global market behavior emerges from specific individual behavior interactions.

  18. Color encryption scheme based on adapted quantum logistic map

    Science.gov (United States)

    Zaghloul, Alaa; Zhang, Tiejun; Amin, Mohamed; Abd El-Latif, Ahmed A.

    2014-04-01

    This paper presents a new color image encryption scheme based on quantum chaotic system. In this scheme, a new encryption scheme is accomplished by generating an intermediate chaotic key stream with the help of quantum chaotic logistic map. Then, each pixel is encrypted by the cipher value of the previous pixel and the adapted quantum logistic map. The results show that the proposed scheme has adequate security for the confidentiality of color images.

  19. Pemanfaatan Google Maps Api Untuk Visualisasi Data Base Transceiver Station

    OpenAIRE

    Rani, Septia

    2016-01-01

    This paper discusses the use of the Google Maps API to perform data visualization for Base Transceiver Station (BTS) data. BTS are typically used by telecommunications companies to facilitate wireless communication between communication devices with the network operator. Each BTS has important information such as it’s location, it’s transaction traffic, as well as information about revenue. With the implementation of BTS data visualization using the Google Maps API, key information owned by e...

  20. A novel block cryptosystem based on iterating a chaotic map

    International Nuclear Information System (INIS)

    Xiang Tao; Liao Xiaofeng; Tang Guoping; Chen Yong; Wong, Kwok-wo

    2006-01-01

    A block cryptographic scheme based on iterating a chaotic map is proposed. With random binary sequences generated from the real-valued chaotic map, the plaintext block is permuted by a key-dependent shift approach and then encrypted by the classical chaotic masking technique. Simulation results show that performance and security of the proposed cryptographic scheme are better than those of existing algorithms. Advantages and security of our scheme are also discussed in detail

  1. Pemanfaatan Google Maps Api Untuk Visualisasi Data Base Transceiver Station

    OpenAIRE

    Rani, Septia

    2016-01-01

    This paper discusses the use of the Google Maps API to perform data visualization for Base Transceiver Station (BTS) data. BTS are typically used by telecommunications companies to facilitate wireless communication between communication devices with the network operator. Each BTS has important information such as it's location, it's transaction traffic, as well as information about revenue. With the implementation of BTS data visualization using the Google Maps API, key information owned by e...

  2. PEMANFAATAN GOOGLE MAPS API UNTUK VISUALISASI DATA BASE TRANSCEIVER STATION

    OpenAIRE

    Rani, Septia

    2016-01-01

    This paper discusses the use of the Google Maps API to perform data visualization for Base Transceiver Station (BTS) data. BTS are typically used by telecommunications companies to facilitate wireless communication between communication devices with the network operator. Each BTS has important information such as it’s location, it’s transaction traffic, as well as information about revenue. With the implementation of BTS data visualization using the Google Maps API, key information owned by e...

  3. Templated dewetting: designing entirely self-organized platforms for photocatalysis.

    Science.gov (United States)

    Altomare, Marco; Nguyen, Nhat Truong; Schmuki, Patrik

    2016-12-01

    Formation and dispersion of metal nanoparticles on oxide surfaces in site-specific or even arrayed configuration are key in various technological processes such as catalysis, photonics, electrochemistry and for fabricating electrodes, sensors, memory devices, and magnetic, optical, and plasmonic platforms. A crucial aspect towards an efficient performance of many of these metal/metal oxide arrangements is a reliable fabrication approach. Since the early works on graphoepitaxy in the 70s, solid state dewetting of metal films on patterned surfaces has been much explored and regarded as a most effective tool to form defined arrays of ordered metal particles on a desired substrate. While templated dewetting has been studied in detail, particularly from a mechanistic perspective on lithographically patterned Si surfaces, the resulting outstanding potential of its applications on metal oxide semiconductors, such as titania, has received only limited attention. In this perspective we illustrate how dewetting and particularly templated dewetting can be used to fabricate highly efficient metal/TiO 2 photocatalyst assemblies e.g. for green hydrogen evolution. A remarkable advantage is that the synthesis of such photocatalysts is completely based on self-ordering principles: anodic self-organized TiO 2 nanotube arrays that self-align to a highest degree of hexagonal ordering are an ideal topographical substrate for a second self-ordering process, that is, templated-dewetting of sputter-deposited metal thin films. The controllable metal/semiconductor coupling delivers intriguing features and functionalities. We review concepts inherent to dewetting and particularly templated dewetting, and outline a series of effective tools that can be synergistically interlaced to reach fine control with nanoscopic precision over the resulting metal/TiO 2 structures (in terms of e.g. high ordering, size distribution, site specific placement, alloy formation) to maximize their photocatalytic

  4. Evolutionary Cell Computing: From Protocells to Self-Organized Computing

    Science.gov (United States)

    Colombano, Silvano; New, Michael H.; Pohorille, Andrew; Scargle, Jeffrey; Stassinopoulos, Dimitris; Pearson, Mark; Warren, James

    2000-01-01

    On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well

  5. Modeling, Designing, and Implementing an Avatar-based Interactive Map

    Directory of Open Access Journals (Sweden)

    Stefan Andrei

    2016-03-01

    Full Text Available Designing interactive maps has always been a challenge due to the geographical complexity of the earth’s landscape and the difficulty of resolving details to a high resolution. In the past decade or so, one of the most impressive map-based software application, the Global Positioning System (GPS, has probably the highest level of interaction with the user. This article describes an innovative technique for designing an avatar-based virtual interactive map for the Lamar University Campus, which will entail the buildings’ exterior as well as their interiors. Many universities provide 2D or 3D maps and even interactive maps. However, these maps do not provide a complete interaction with the user. To the best of our knowledge, this project is the first avatar-based interaction game that allows 100% interaction with the user. This work provides tremendous help to the freshman students and visitors of Lamar University. As an important marketing tool, the main objective is to get better visibility of the campus worldwide and to increase the number of students attending Lamar University.

  6. BAC-end sequence-based SNPs and Bin mapping for rapid integration of physical and genetic maps in apple.

    Science.gov (United States)

    Han, Yuepeng; Chagné, David; Gasic, Ksenija; Rikkerink, Erik H A; Beever, Jonathan E; Gardiner, Susan E; Korban, Schuyler S

    2009-03-01

    A genome-wide BAC physical map of the apple, Malus x domestica Borkh., has been recently developed. Here, we report on integrating the physical and genetic maps of the apple using a SNP-based approach in conjunction with bin mapping. Briefly, BAC clones located at ends of BAC contigs were selected, and sequenced at both ends. The BAC end sequences (BESs) were used to identify candidate SNPs. Subsequently, these candidate SNPs were genetically mapped using a bin mapping strategy for the purpose of mapping the physical onto the genetic map. Using this approach, 52 (23%) out of 228 BESs tested were successfully exploited to develop SNPs. These SNPs anchored 51 contigs, spanning approximately 37 Mb in cumulative physical length, onto 14 linkage groups. The reliability of the integration of the physical and genetic maps using this SNP-based strategy is described, and the results confirm the feasibility of this approach to construct an integrated physical and genetic maps for apple.

  7. 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.

  8. A principle of fractal-stochastic dualism and Gompertzian dynamics of growth and self-organization.

    Science.gov (United States)

    Waliszewski, Przemyslaw

    2005-10-01

    The emergence of Gompertzian dynamics at the macroscopic, tissue level during growth and self-organization is determined by the existence of fractal-stochastic dualism at the microscopic level of supramolecular, cellular system. On one hand, Gompertzian dynamics results from the complex coupling of at least two antagonistic, stochastic processes at the molecular cellular level. It is shown that the Gompertz function is a probability function, its derivative is a probability density function, and the Gompertzian distribution of probability is of non-Gaussian type. On the other hand, the Gompertz function is a contraction mapping and defines fractal dynamics in time-space; a prerequisite condition for the coupling of processes. Furthermore, the Gompertz function is a solution of the operator differential equation with the Morse-like anharmonic potential. This relationship indicates that distribution of intrasystemic forces is both non-linear and asymmetric. The anharmonic potential is a measure of the intrasystemic interactions. It attains a point of the minimum (U(0), t(0)) along with a change of both complexity and connectivity during growth and self-organization. It can also be modified by certain factors, such as retinoids.

  9. 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.

  10. 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

  11. Managing mapping data using commercial data base management software.

    Science.gov (United States)

    Elassal, A.A.

    1985-01-01

    Electronic computers are involved in almost every aspect of the map making process. This involvement has become so thorough that it is practically impossible to find a recently developed process or device in the mapping field which does not employ digital processing in some form or another. This trend, which has been evolving over two decades, is accelerated by the significant improvements in capility, reliability, and cost-effectiveness of electronic devices. Computerized mapping processes and devices share a common need for machine readable data. Integrating groups of these components into automated mapping systems requires careful planning for data flow amongst them. Exploring the utility of commercial data base management software to assist in this task is the subject of this paper. -Author

  12. Intergenerational Exchange of Knowledge, Skills, Values and Practices between Self-Organized Active Citizens in Maribor, Slovenia

    Science.gov (United States)

    Krašovec, Sabina Jelenc; Gregorcic, Marta

    2017-01-01

    Our paper deals with intergenerational informal learning developed by participatory democracy process in the Self-organized District Communities (SDC) in Maribor, the second largest city in Slovenia. It is based on the assumption that SDC assemblies, being safe and trustworthy, are very powerful spaces for behavioural and values exchange between…

  13. Controlled growth of epitaxial CeO2 thin films with self-organized nanostructure by chemical solution method

    DEFF Research Database (Denmark)

    Yue, Zhao; Grivel, Jean-Claude

    2013-01-01

    Chemical solution deposition is a versatile technique to grow oxide thin films with self-organized nanostructures. Morphology and crystallographic orientation control of CeO2 thin films grown on technical NiW substrates by a chemical solution deposition method are achieved in this work. Based...

  14. Creating soil moisture maps based on radar satellite imagery

    Science.gov (United States)

    Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

    2017-10-01

    The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

  15. Hash function based on piecewise nonlinear chaotic map

    International Nuclear Information System (INIS)

    Akhavan, A.; Samsudin, A.; Akhshani, A.

    2009-01-01

    Chaos-based cryptography appeared recently in the early 1990s as an original application of nonlinear dynamics in the chaotic regime. In this paper, an algorithm for one-way hash function construction based on piecewise nonlinear chaotic map with a variant probability parameter is proposed. Also the proposed algorithm is an attempt to present a new chaotic hash function based on multithreaded programming. In this chaotic scheme, the message is connected to the chaotic map using probability parameter and other parameters of chaotic map such as control parameter and initial condition, so that the generated hash value is highly sensitive to the message. Simulation results indicate that the proposed algorithm presented several interesting features, such as high flexibility, good statistical properties, high key sensitivity and message sensitivity. These properties make the scheme a suitable choice for practical applications.

  16. Cognitive mapping based on synthetic vision?

    Science.gov (United States)

    Helmetag, Arnd; Halbig, Christian; Kubbat, Wolfgang; Schmidt, Rainer

    1999-07-01

    The analysis of accidents focused our work on the avoidance of 'Controlled Flight Into Terrain' caused by insufficient situation awareness. Analysis of safety concepts led us to the design of the proposed synthetic vision system that will be described. Since most information on these 3D-Displays is shown in a graphical way, it can intuitively be understood by the pilot. What are the new possibilities using SVS enhancing situation awareness? First, detection of ground collision hazard is possible by monitoring a perspective Primary Flight Display. Under the psychological point of view it is based on the perception of expanding objects in the visual flow field. Supported by a Navigation Display a local conflict resolution can be mentally worked out very fast. Secondly, it is possible to follow a 3D flight path visualized as a 'Tunnel in the sky.' This can further be improved by using a flight path prediction. These are the prerequisites for a safe and adequate movement in any kind of spatial environment. However situation awareness requires the ability of navigation and spatial problem solving. Both abilities are based on higher cognitive functions in real as well as in a synthetic environment. In this paper the current training concept will be analyzed. Advantages resulting from the integration of a SVS concerning pilot training will be discussed and necessary requirements in terrain depiction will be pinpointed. Finally a modified Computer Based Training for the familiarization with Salzburg Airport for a SVS equipped aircraft will be presented. It is developed by Darmstadt University of Technology in co-operation with Lufthansa Flight Training.

  17. Structures in plasmas and their self-organizations

    International Nuclear Information System (INIS)

    Yoshida, Zensho

    1989-01-01

    This paper is a concise review of the physics of structures. The progress of the structure theory was motivated by the appearances of many different ordered structures that are self-organized through spontaneous dynamics. For typical examples in plasma physics, cited are the MHD equilibria (Taylor relaxed state), the ion acoustic solitons, and the van Kampen modes of continuous-spectrum Langmuir waves. A static theory for the intrinsic structures is developed to clarify the basic difference between the classical orders and the self-organized structures. In linear models, an intrinsic structure is characterized by a singular spectrum of a certain eigenvalue problem. The Taylor relaxed state is characterized by the continuum of the point spectra of the rotational operator. The general MHD equilibrium is related to a nonlinear eigenvalue problem. The soliton is a nonlinear eigenfunction of the Helmholtz-type Bohm equation. The variational expression of an intrinsic structure is characterized by restrictive functionals, which in a dynamical theory, is related to selective conservations. The Taylor relaxed state is obtained by minimizing the magnetic-field energy with conserving the magnetic helicity. This selective dissipation occurs in the fluctuations of kink modes. The soliton is self-organized by the dissipation of the Hamiltonian with keeping the energy approximately constant. The principle of the selective dissipation is logically a generalization of the ergodic hypothesis for the classical order and could be proved in a rigorous way by analyzing the attractor of the dynamical systems, just as the proof the ergodic theorem is obtained by the time-asymptotic analysis of a class of semigroups. (J.P.N.) 85 refs

  18. Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

    Science.gov (United States)

    Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V.; Alvarez-Santos, Victor; Pardo, Xose Manuel

    2013-01-01

    To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal. PMID:23271604

  19. Self-organized multi-camera network for a fast and easy deployment of ubiquitous robots in unknown environments.

    Science.gov (United States)

    Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V; Alvarez-Santos, Victor; Pardo, Xose Manuel

    2012-12-27

    To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.

  20. Self-organized critical behavior in pinned flux lattices

    International Nuclear Information System (INIS)

    Pla, O.; Nori, F.

    1991-01-01

    We study the response of pinned fluxed lattices, under small perturbations in the driving force, below and close to the pinning-depinning transition. For driving Lorentz forces below F c (the depinning force at which the whole flux lattice slides), the system has instabilities against small force increases, with a power-law distribution characteristic of self-organized criticality. Specifically, D(d)∼d -1,3 , where d is the displacement of a flux line after a very small force increase. We also study the initial stages of the motion of the lattice once the driving force overcomes the pinning forces

  1. Simple lecture demonstrations of instability and self-organization

    International Nuclear Information System (INIS)

    Mayer, V V; Varaksina, E I; Saranin, V A

    2014-01-01

    A dielectric liquid layer with an electric field created inside it is proposed as a means for demonstrating the phenomenon of self-organization. The field is produced by the distributed charge transferred by a corona discharge from the tip to the liquid surface. The theory of the phenomenon is presented. An analogy with the Rayleigh – Taylor instability is drawn and a comparison with the Benard instability is given. The practicality of the method for both natural sciences and the humanities is discussed. (methodological notes)

  2. Turbulence and Self-Organization Modeling Astrophysical Objects

    CERN Document Server

    Marov, Mikhail Ya

    2013-01-01

    This book focuses on the development of continuum models of natural turbulent media. It provides a theoretical approach to the solutions of different problems related to the formation, structure and evolution of astrophysical and geophysical objects. A stochastic modeling approach is used in the mathematical treatment of these problems, which reflects self-organization processes in open dissipative systems. The authors also consider examples of ordering for various objects in space throughout their evolutionary processes. This volume is aimed at graduate students and researchers in the fields of mechanics, astrophysics, geophysics, planetary and space science.

  3. Contour Detection for UAV-Based Cadastral Mapping

    Directory of Open Access Journals (Sweden)

    Sophie Crommelinck

    2017-02-01

    Full Text Available Unmanned aerial vehicles (UAVs provide a flexible and low-cost solution for the acquisition of high-resolution data. The potential of high-resolution UAV imagery to create and update cadastral maps is being increasingly investigated. Existing procedures generally involve substantial fieldwork and many manual processes. Arguably, multiple parts of UAV-based cadastral mapping workflows could be automated. Specifically, as many cadastral boundaries coincide with visible boundaries, they could be extracted automatically using image analysis methods. This study investigates the transferability of gPb contour detection, a state-of-the-art computer vision method, to remotely sensed UAV images and UAV-based cadastral mapping. Results show that the approach is transferable to UAV data and automated cadastral mapping: object contours are comprehensively detected at completeness and correctness rates of up to 80%. The detection quality is optimal when the entire scene is covered with one orthoimage, due to the global optimization of gPb contour detection. However, a balance between high completeness and correctness is hard to achieve, so a combination with area-based segmentation and further object knowledge is proposed. The localization quality exhibits the usual dependency on ground resolution. The approach has the potential to accelerate the process of general boundary delineation during the creation and updating of cadastral maps.

  4. 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)

  5. Stochastic Wheel-Slip Compensation Based Robot Localization and Mapping

    Directory of Open Access Journals (Sweden)

    SIDHARTHAN, R. K.

    2016-05-01

    Full Text Available Wheel slip compensation is vital for building accurate and reliable dead reckoning based robot localization and mapping algorithms. This investigation presents stochastic slip compensation scheme for robot localization and mapping. Main idea of the slip compensation technique is to use wheel-slip data obtained from experiments to model the variations in slip velocity as Gaussian distributions. This leads to a family of models that are switched depending on the input command. To obtain the wheel-slip measurements, experiments are conducted on a wheeled mobile robot and the measurements thus obtained are used to build the Gaussian models. Then the localization and mapping algorithm is tested on an experimental terrain and a new metric called the map spread factor is used to evaluate the ability of the slip compensation technique. Our results clearly indicate that the proposed methodology improves the accuracy by 72.55% for rotation and 66.67% for translation motion as against an uncompensated mapping system. The proposed compensation technique eliminates the need for extro receptive sensors for slip compensation, complex feature extraction and association algorithms. As a result, we obtain a simple slip compensation scheme for localization and mapping.

  6. Machine learning-based dual-energy CT parametric mapping.

    Science.gov (United States)

    Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

    2018-05-22

    The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρe), mean excitation energy (Ix), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 seconds. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. . © 2018 Institute of Physics and Engineering in

  7. Global trends in satellite-based emergency mapping

    Science.gov (United States)

    Voigt, Stefan; Giulio-Tonolo, Fabio; Lyons, Josh; Kučera, Jan; Jones, Brenda; Schneiderhan, Tobias; Platzeck, Gabriel; Kaku, Kazuya; Hazarika, Manzul Kumar; Czaran, Lorant; Li, Suju; Pedersen, Wendi; James, Godstime Kadiri; Proy, Catherine; Muthike, Denis Macharia; Bequignon, Jerome; Guha-Sapir, Debarati

    2016-01-01

    Over the past 15 years, scientists and disaster responders have increasingly used satellite-based Earth observations for global rapid assessment of disaster situations. We review global trends in satellite rapid response and emergency mapping from 2000 to 2014, analyzing more than 1000 incidents in which satellite monitoring was used for assessing major disaster situations. We provide a synthesis of spatial patterns and temporal trends in global satellite emergency mapping efforts and show that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters. We present an outlook on the future use of Earth observation technology for disaster response and mitigation by putting past and current developments into context and perspective.

  8. Dipole-magnet field models based on a conformal map

    Directory of Open Access Journals (Sweden)

    P. L. Walstrom

    2012-10-01

    Full Text Available In general, generation of charged-particle transfer maps for conventional iron-pole-piece dipole magnets to third and higher order requires a model for the midplane field profile and its transverse derivatives (soft-edge model to high order and numerical integration of map coefficients. An exact treatment of the problem for a particular magnet requires use of measured magnetic data. However, in initial design of beam transport systems, users of charged-particle optics codes generally rely on magnet models built into the codes. Indeed, if maps to third order are adequate for the problem, an approximate analytic field model together with numerical map coefficient integration can capture the important features of the transfer map. The model described in this paper is based on the fact that, except at very large distances from the magnet, the magnetic field for parallel pole-face magnets with constant pole gap height and wide pole faces is basically two dimensional (2D. The field for all space outside of the pole pieces is given by a single (complex analytic expression and includes a parameter that controls the rate of falloff of the fringe field. Since the field function is analytic in the complex plane outside of the pole pieces, it satisfies two basic requirements of a field model for higher-order map codes: it is infinitely differentiable at the midplane and also a solution of the Laplace equation. It is apparently the only simple model available that combines an exponential approach to the central field with an inverse cubic falloff of field at large distances from the magnet in a single expression. The model is not intended for detailed fitting of magnetic field data, but for use in numerical map-generating codes for studying the effect of extended fringe fields on higher-order transfer maps. It is based on conformally mapping the area between the pole pieces to the upper half plane, and placing current filaments on the pole faces. An

  9. Hyperbolic mapping of complex networks based on community information

    Science.gov (United States)

    Wang, Zuxi; Li, Qingguang; Jin, Fengdong; Xiong, Wei; Wu, Yao

    2016-08-01

    To improve the hyperbolic mapping methods both in terms of accuracy and running time, a novel mapping method called Community and Hyperbolic Mapping (CHM) is proposed based on community information in this paper. Firstly, an index called Community Intimacy (CI) is presented to measure the adjacency relationship between the communities, based on which a community ordering algorithm is introduced. According to the proposed Community-Sector hypothesis, which supposes that most nodes of one community gather in a same sector in hyperbolic space, CHM maps the ordered communities into hyperbolic space, and then the angular coordinates of nodes are randomly initialized within the sector that they belong to. Therefore, all the network nodes are so far mapped to hyperbolic space, and then the initialized angular coordinates can be optimized by employing the information of all nodes, which can greatly improve the algorithm precision. By applying the proposed dual-layer angle sampling method in the optimization procedure, CHM reduces the time complexity to O(n2) . The experiments show that our algorithm outperforms the state-of-the-art methods.

  10. Optimality and self-organization in river deltas

    Science.gov (United States)

    Tejedor, A.; Longjas, A.; Edmonds, D. A.; Zaliapin, I. V.; Georgiou, T. T.; Rinaldo, A.; Foufoula-Georgiou, E.

    2017-12-01

    Deltas are nourished by channel networks, whose connectivity constrains, if not drives, the evolution, functionality and resilience of these systems. Understanding the coevolution of deltaic channels and their flux organization is crucial for guiding maintenance strategies of these highly stressed systems from a range of anthropogenic activities. However, in contrast to tributary channel networks, to date, no theory has been proposed to explain how deltas self-organize to distribute water and sediment to the delta top and the shoreline. Here, we hypothesize the existence of an optimality principle underlying the self-organized partition of fluxes in delta channel networks. Specifically, we hypothesize that deltas distribute water and sediment fluxes on a given delta topology such as to maximize the diversity of flux delivery to the shoreline. By introducing the concept of nonlocal Entropy Rate (nER) and analyzing ten field deltas in diverse environments, we present evidence that supports our hypothesis, suggesting that delta networks achieve dynamically accessible maxima of their nER. Furthermore, by analyzing six simulated deltas using the Delf3D model and following their topologic and flux re-organization before and after major avulsions, we further study the evolution of nER and confirm our hypothesis. We discuss how optimal flux distributions in terms of nER, when interpreted in terms of resilience, are configurations that reflect an increased ability to withstand perturbations.

  11. Self-organizing magnetic beads for biomedical applications

    International Nuclear Information System (INIS)

    Gusenbauer, Markus; Kovacs, Alexander; Reichel, Franz; Exl, Lukas; Bance, Simon; Özelt, Harald; Schrefl, Thomas

    2012-01-01

    In the field of biomedicine magnetic beads are used for drug delivery and to treat hyperthermia. Here we propose to use self-organized bead structures to isolate circulating tumor cells using lab-on-chip technologies. Typically blood flows past microposts functionalized with antibodies for circulating tumor cells. Creating these microposts with interacting magnetic beads makes it possible to tune the geometry in size, position and shape. We developed a simulation tool that combines micromagnetics and discrete particle dynamics, in order to design micropost arrays made of interacting beads. The simulation takes into account the viscous drag of the blood flow, magnetostatic interactions between the magnetic beads and gradient forces from external aligned magnets. We developed a particle–particle particle–mesh method for effective computation of the magnetic force and torque acting on the particles. - Highlights: ► We propose to use self-organized bead structures to isolate circulating tumor cells. ► Flexible ways are important to get a high probability of catching cancer cells. ► The beads make it possible to tune the geometry in size position and shape.

  12. On self-organized criticality in nonconserving systems

    International Nuclear Information System (INIS)

    Socolar, J.E.S.; Grinstein, G.; Jayaprakash, C.

    1993-01-01

    Two models with nonconserving dynamics and slow continuous deterministic driving, a stick-slip model (SSM) of earthquake dynamics and a toy forest-fire model (FFM), have recently been argued to show numerical evidence of self-organized criticality (generic, scale-invariant steady states). To determine whether the observed criticality is indeed generic, we study these models as a function of a parameter γ which was implicitly tuned to a special value, γ=1, in their original definitions. In both cases, the maximum Lyapunov exponent vanishes at γ=1. We find that the FFM does not exhibit self-organized criticality for any γ, including γ=1; nor does the SSM with periodic boundary conditions. Both models show evidence of macroscopic periodic oscillations in time for some range of γ values. We suggest that such oscillations may provide a mechanism for the generation of scale-invariant structure in nonconserving systems, and, in particular, that they underlie the criticality previously observed in the SSM with open boundary conditions

  13. SELF-ORGANIZATION OF LEAD SULFIDE QUANTUM DOTS INTO SUPERSTRUCTURES

    Directory of Open Access Journals (Sweden)

    Elena V. Ushakova

    2014-11-01

    Full Text Available The method of X-ray structural analysis (X-ray scattering at small angles is used to show that the structures obtained by self-organization on a substrate of lead sulfide (PbS quantum dots are ordered arrays. Self-organization of quantum dots occurs at slow evaporation of solvent from a cuvette. The cuvette is a thin layer of mica with teflon ring on it. The positions of peaks in SAXS pattern are used to calculate crystal lattice of obtained ordered structures. Such structures have a primitive orthorhombic crystal lattice. Calculated lattice parameters are: a = 21,1 (nm; b = 36,2 (nm; c = 62,5 (nm. Dimensions of structures are tens of micrometers. The spectral properties of PbS QDs superstructures and kinetic parameters of their luminescence are investigated. Absorption band of superstructures is broadened as compared to the absorption band of the quantum dots in solution; the luminescence band is slightly shifted to the red region of the spectrum, while its bandwidth is not changed much. Luminescence lifetime of obtained structures has been significantly decreased in comparison with the isolated quantum dots in solution, but remained the same for the lead sulfide quantum dots close-packed ensembles. Such superstructures can be used to produce solar cells with improved characteristics.

  14. Informational temperature concept and the nature of self-organization

    International Nuclear Information System (INIS)

    Lin, Shu-Kun

    1996-01-01

    Self-organization phenomena are spontaneous processes. Their behavior should be governed by the second law of thermodynamics. The dissipative structure theory of the Prigogine school of thermodynamics claims that open-quotes order out of chaosclose quotes through open-quotes self-organizationclose quotes and challenges the validity of the second law of thermodynamics. Unfortunately this theory is questionable. Therefore we have to reconsider the related fundamental theoretical problems. Informational entropy (S) and information (I) are related by S = S max - I, where S max is the maximum informational entropy. This conforms with the broadly accepted definition that entropy is the information loss. As informational entropy concept has been proved to be useful, it will be convenient to define an informational temperature, T I . This can be related to energy E and the informational entropy S. Information registration is a process of ΔI > 0, or ΔS 0). Therefore, T I is negative, and has the opposite sign of the conventional thermodynamic temperature, T. This concept is useful for clarifying the concepts of open-quotes orderclose quotes and open-quotes disorderclose quotes of static structures and characterizing many typical information loss processes of self-organization

  15. Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model

    International Nuclear Information System (INIS)

    Lin Min; Gang, Zhao; Chen Tianlun

    2009-01-01

    A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)

  16. Demonstration for novel self-organization theory by three-dimensional magnetohydrodynamic simulation

    International Nuclear Information System (INIS)

    Kondoh, Yoshiomi; Hosaka, Yasuo; Liang, Jia-Ling.

    1993-03-01

    It is demonstrated by three-dimensional simulations for resistive magnetohydrodynamic (MHD) plasmas with both 'spatially nonuniform resistivity η' and 'uniformη' that the attractor of the dissipative structure in the resistive MHD plasmas is given by ∇ x (ηj) = (α/2)B which is derived from a novel self-organization theory based on the minimum dissipation rate profile. It is shown by the simulations that the attractor is reduced to ∇ x B = λB in the special case with the 'uniformη' and no pressure gradient. (author)

  17. Self-organization of turbulence. A brief review of self-organization with particular reference to hydrodynamic and magnetohydrodynamic turbulence

    Energy Technology Data Exchange (ETDEWEB)

    Hasegawa, A [Bell Labs., Murray Hill, NJ (USA)

    1982-02-01

    Theoretical treatments of turbulence in fluids and plasmas often assume that the turbulence is isotropic and homogeneous. It is also often considered that turbulence produces uniformly distributed chaos, even when starting with a coherent initial condition. Recently, however, phenomena which do not obey these classic concepts have emerged. For example, in two-dimensional Navier-Stokes turbulence, an organized flow or structure is found to appear even from a chaotic initial condition. The author attempts to review some of the recent developments of a phenomenon called self-organization in the field of hydrodynamics and plasma physics.

  18. The ETLMR MapReduce-Based ETL Framework

    DEFF Research Database (Denmark)

    Xiufeng, Liu; Thomsen, Christian; Pedersen, Torben Bach

    2011-01-01

    This paper presents ETLMR, a parallel Extract--Transform--Load (ETL) programming framework based on MapReduce. It has built-in support for high-level ETL-specific constructs including star schemas, snowflake schemas, and slowly changing dimensions (SCDs). ETLMR gives both high programming...

  19. Tissue-based map of the human proteome

    DEFF Research Database (Denmark)

    Uhlén, Mathias; Fagerberg, Linn; Hallström, Björn M.

    2015-01-01

    Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transc...

  20. Rule-based Test Generation with Mind Maps

    Directory of Open Access Journals (Sweden)

    Dimitry Polivaev

    2012-02-01

    Full Text Available This paper introduces basic concepts of rule based test generation with mind maps, and reports experiences learned from industrial application of this technique in the domain of smart card testing by Giesecke & Devrient GmbH over the last years. It describes the formalization of test selection criteria used by our test generator, our test generation architecture and test generation framework.