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Sample records for supervised self-organizing map

  1. Extending self-organizing maps for supervised classification of remotely sensed data

    Institute of Scientific and Technical Information of China (English)

    CHEN Yongliang

    2009-01-01

    An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors: an input vector and a class codebook vector. When a training sample is input into the model, Kohonens competitive learning rule is applied to selecting the winning neuron from the Kohonen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training samples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.

  2. 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

  3. Soft supervised self-organizing mapping (3SOM) for improving land cover classification with MODIS time-series

    Science.gov (United States)

    Lawawirojwong, Siam

    Classification of remote sensing data has long been a fundamental technique for studying vegetation and land cover. Furthermore, land use and land cover maps are a basic need for environmental science. These maps are important for crop system monitoring and are also valuable resources for decision makers. Therefore, an up-to-date and highly accurate land cover map with detailed and timely information is required for the global environmental change research community to support natural resource management, environmental protection, and policy making. However, there appears to be a number of limitations associated with data utilization such as weather conditions, data availability, cost, and the time needed for acquiring and processing large numbers of images. Additionally, improving the classification accuracy and reducing the classification time have long been the goals of remote sensing research and they still require the further study. To manage these challenges, the primary goal of this research is to improve classification algorithms that utilize MODIS-EVI time-series images. A supervised self-organizing map (SSOM) and a soft supervised self-organizing map (3SOM) are modified and improved to increase classification efficiency and accuracy. To accomplish the main goal, the performance of the proposed methods is investigated using synthetic and real landscape data derived from MODIS-EVI time-series images. Two study areas are selected based on a difference of land cover characteristics: one in Thailand and one in the Midwestern U.S. The results indicate that time-series imagery is a potentially useful input dataset for land cover classification. Moreover, the SSOM with time-series data significantly outperforms the conventional classification techniques of the Gaussian maximum likelihood classifier (GMLC) and backpropagation neural network (BPNN). In addition, the 3SOM employed as a soft classifier delivers a more accurate classification than the SSOM applied as

  4. PARALLEL SELF-ORGANIZING MAP

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A new self-organizing map, parallel self-organizing map (PSOM), was proposed for information parallel processing purpose. In this model, there are two separate layers of neurons connected together,the number of neurons in both layer and connections between them is equal to the number of total elements of input signals, the weight updating is managed through a sequence of operations among some unitary transformation and operation matrixes, so the conventional repeated learning procedure was modified to learn just once and an algorithm was developed to realize this new learning method. With a typical classification example, the performance of PSOM demonstrated convergence results similar to Kohonen's model. Theoretic analysis and proofs also showed some interesting properties of PSOM. As it was pointed out, the contribution of such a network may not be so significant, but its parallel mode may be interesting for quantum computation.

  5. 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, ...

  6. 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 ...

  7. Hybrid Self Organizing Map for Overlapping Clusters

    Directory of Open Access Journals (Sweden)

    M.N.M. Sap

    2008-12-01

    Full Text Available The Kohonen self organizing map is an excellent tool in exploratoryphase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcomethe uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM and reduces the errors.

  8. 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.

    A self-organizing feature map (SOFM), a kind of artificial neural network (ANN) architecture, is used in this work for continental shelf seafloor sediment classification. Echo data are acquired using an echosounding system from three types...

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

    African Journals Online (AJOL)

    selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) ... synthetic mixtures and a real combination product of sulfamethoxazole (SMX) and ... common multivariate method seen in in-process.

  10. Distinguishing volcanic lithology using Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Self-Organizing Map is an unsupervised learning algorithm. It has the ability of self-organization,self-learning and side associative thinking. Based on the principle it can identified the complex volcanic lithology. According to the logging data of the volcanic rock samples, the SOM will be trained, The SOM training results were analyzed in order to choose optimally parameters of the network. Through identifying the logging data of volcanic formations, the result shows that the map can achieve good application effects.

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

    NARCIS (Netherlands)

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

    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 repo

  12. 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.

  13. Java Parallel Implementations of Kohonen Self-Organizing Feature Maps

    Institute of Scientific and Technical Information of China (English)

    YANG Shang-ming; HU Jie

    2004-01-01

    The Kohonen self-organizing map (SOM) is an important tool to find a mapping from high-dimensional space to low dimensional space. The time a SOM requires increases with the number of neurons. A parallel implementation of the algorithm can make it faster. This paper investigates the most recent parallel algorithms on SOMs. Using Java network programming utilities, improved parallel and distributed system are set up to simulate these algorithms. From the simulations, we conclude that those algorithms form good feature maps.

  14. Clustering Similarity Digest Bloom Filters in Self-Organizing Maps

    Science.gov (United States)

    2012-12-01

    Science Foundation. xv THIS PAGE INTENTIONALLY LEFT BLANK xvi CHAPTER 1: Introduction In the late 1980s, IBM’s 3390 Model 1 direct access storage device...information autonomously. From there, we look at a specific type of artificial neural network, the self-organizing map, as a appropriate model to build...training was not thorough enough for significant similarity scor - ing with the untrained document collection. In Section 4.1 we saw that each SOM had a

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

    Institute of Scientific and Technical Information of China (English)

    LIANG Xiao-Ming; L(U) Hua-ping; LIU Zong-Hua

    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 serf-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns.

  16. Self Organizing Maps for use in Deep Inelastic Scattering

    Science.gov (United States)

    Askanazi, Evan

    2015-04-01

    Self Organizing Maps are a type of artificial neural network that has been proven to be particularly useful in solving complex problems in neural biology, engineering, robotics and physics. We are attempting to use the Self Organizing Map to solve problems and probe phenomenological patterns in subatomic physics, specifically in Deep Inelastic Scattering (DIS). In DIS there is a cross section in electron hadron scattering that is dependent on the momentum fraction x of the partons in the hadron and the momentum transfer of the virtual photon exchanged. There is a soft cross part of this cross section that currently can only be found through experimentation; this soft part is comprised of Structure Functions which in turn are comprised of the Parton Distribution Functions (PDFs). We aim to use the Self Organizing Process, or SOP, to take theoretical models of these PDFs and fit it to the previous, known data. The SOP will also be used to probe the behavior of the PDFs in particular at large x values, in order to observe how they congregate. The ability of the SOPto take multidimensional data and convert it into two dimensional output is anticipated to be particularly useful in achieving this aim.

  17. Self organizing maps in urban heat stress projections

    Science.gov (United States)

    Lee, Kyoung

    2016-04-01

    A self organizing map (SOM) is an unsupervised machine learning algorithm well suited for identifying patterns in large datasets. It has been used successfully to classify atmospheric states in climate data and as part of statistical downscaling procedures. This study aims to use SOMs to produce downscaled CMIP5-based projections of wet-bulb temperature in urban areas, taking into account the regional atmospheric state and learned local dynamics. These downscaled projections will be compared to the CMIP5 models as well as to observations and then used to project local extreme heat stress events in the future.

  18. 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.

  19. Application of Self-Organizing Map to Stellar Spectral Classifications

    CERN Document Server

    Mahdi, Bazarghan

    2011-01-01

    We present an automatic, fast, accurate and robust method of classifying astronomical objects. The Self Organizing Map (SOM) as an unsupervised Artificial Neural Network (ANN) algorithm is used for classification of stellar spectra of stars. The SOM is used to make clusters of different spectral classes of Jacoby, Hunter and Christian (JHC) library. This ANN technique needs no training examples and the stellar spectral data sets are directly fed to the network for the classification. The JHC library contains 161 spectra out of which, 158 spectra are selected for the classification. These 158 spectra are input vectors to the network and mapped into a two dimensional output grid. The input vectors close to each other are mapped into the same or neighboring neurons in the output space. So, the similar objects are making clusters in the output map and making it easy to analyze high dimensional data. After running the SOM algorithm on 158 stellar spectra, with 2799 data points each, the output map is analyzed and ...

  20. Self-Organizing Maps-based ocean currents forecasting system

    Science.gov (United States)

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-03-01

    An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

  1. 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.

  2. Coastal Water Quality Assessment by Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    NIU Zhiguang; ZHANG Hongwei; ZHANG Ying

    2005-01-01

    A new approach to coastal water quality assessment was put forward through study on self-organizing map (SOM). Firstly, the water quality data of Bohai Bay from 1999 to 2002 were prepared. Then, a set of software for coastal water quality assessment was developed based on the batch version algorithm of SOM and SOM toolbox in MATLAB environment. Furthermore, the training results of SOM could be analyzed with single water quality indexes, the value of N: P( atomic ratio) and the eutrophication index E so that the data were clustered into five different pollution types using k-means clustering method. Finally, it was realized that the monitoring data serial trajectory could be tracked and the new data be classified and assessed automatically. Through application it is found that this study helps to analyze and assess the coastal water quality by several kinds of graphics, which offers an easy decision support for recognizing pollution status and taking corresponding measures.

  3. Fingerprint Image Segmentation Using Haar Wavelet and Self Organizing Map

    Directory of Open Access Journals (Sweden)

    Sri Suwarno

    2013-10-01

    Full Text Available Fingerprint image segmentation is one of the important preprocessing steps in Automatic Fingerprint Identification Systems (AFIS. Segmentation separates image background from image foreground, removing unnecessary information from the image. This paper proposes a new fingerprint segmentation method using Haar wavelet and Kohonen’s Self Organizing Map (SOM. Fingerprint image was decomposed using 2D Haar wavelet in two levels. To generate features vectors, the decomposed image was divided into nonoverlapping blocks of 2x2 pixels and converted into four elements vectors. These vectors were then fed into SOM network that grouped them into foreground and background clusters. Finally, blocks in the background area were removed based on indexes of blocks in the background cluster. From the research that has been carried out, we conclude that the proposed method is effective to segment background from fingerprint images.

  4. Vector representation of user's view using self-organizing map

    Science.gov (United States)

    Ae, Tadashi; Yamaguchi, Tomohisa; Monden, Eri; Kawabata, Shunji; Kamitani, Motoki

    2004-05-01

    There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. Therefore, we propose a method which acquires a view as a vector, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc.. These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. We demonstrate a city-sequence generation system which reflects user's intension as an application of sequence generation containing user's view. We apply the self-organizing map to this system to represent user's view.

  5. Color Image Segmentation using Kohonen Self-Organizing Map (SOM

    Directory of Open Access Journals (Sweden)

    I Komang Ariana

    2014-05-01

    Full Text Available Color image segmentation using Kohonen Self-Organizing Map (SOM, is proposed in this study. RGB color space is used as input in the process of clustering by SOM. Measurement of the distance between weight vector and input vector in learning and recognition stages in SOM method, uses Normalized Euclidean Distance. Then, the validity of clustering result is tested by Davies-Bouldin Index (DBI and Validity Measure (VM to determine the most optimal number of cluster. The clustering result, according to the most optimal number of cluster, then is processed with spatial operations. Spatial operations are used to eliminate noise and small regions which are formed from the clustering result. This system allows segmentation process become automatic and unsupervised. The segmentation results are close to human perception.

  6. Fast CEUS image segmentation based on self organizing maps

    Science.gov (United States)

    Paire, Julie; Sauvage, Vincent; Albouy-Kissi, Adelaïde; Ladam Marcus, Viviane; Marcus, Claude; Hoeffel, Christine

    2014-03-01

    Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper, we present an interactive segmentation method based on the neural networks, which enables to segment malignant tissue over CEUS sequences. We use Self-Organizing-Maps (SOM), an unsupervised neural network, to project high dimensional data to low dimensional space, named a map of neurons. The algorithm gathers the observations in clusters, respecting the topology of the observations space. This means that a notion of neighborhood between classes is defined. Adjacent observations in variables space belong to the same class or related classes after classification. Thanks to this neighborhood conservation property and associated with suitable feature extraction, this map provides user friendly segmentation tool. It will assist the expert in tumor segmentation with fast and easy intervention. We implement SOM on a Graphics Processing Unit (GPU) to accelerate treatment. This allows a greater number of iterations and the learning process to converge more precisely. We get a better quality of learning so a better classification. Our approach allows us to identify and delineate lesions accurately. Our results show that this method improves markedly the recognition of liver lesions and opens the way for future precise quantification of contrast enhancement.

  7. A Design of Network Intrusion Detection Algorithm Based on HMM and Supervised Self Organize Mapping Net%基于 SOM 网络和 HMM 的入侵检测算法设计

    Institute of Scientific and Technical Information of China (English)

    李志坚

    2016-01-01

    为了有效地保证网络的安全性和弥补传统入侵检测系统无法精确识别攻击类别的问题,设计了一种基于监督 SOM 网络和 HMM 的网络入侵混合检测方法。仿真实验表明:文中方法能有效实现网络入侵检测,在样本数量较少的情况下,仍然具有较高的检测率,较其他方法具有较大的优越性。%The study proposes a network intrusion compound detection method based on supervised SOM network and HMM, in order to guarantee the safety of the network effectively and conquer the problem of traditional intrusion detection system not accurately recognizing the attack type. The simulation experiment shows the method proposed in the experiment is an efficient way of network intrusion detection even with small samples. Compared with other detective methods, this algorithm has great priority.

  8. 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.

  9. Can Self-Organizing Maps accurately predict photometric redshifts?

    CERN Document Server

    Way, M J

    2012-01-01

    We present an unsupervised machine learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization approach called Self--Organizing Mapping (SOM). A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's Main Galaxy Sample, Luminous Red Galaxy, and Quasar samples along with the PHAT0 data set from the PHoto-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root mean square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches such as Artificial Neural Networks and Gaussian Process Regression. SOM RMSE--results (using $\\Delta$z=z$_{phot}$--z$_{spec}$) for the Main Galaxy Sample are 0.023, for the Luminous Red Galaxy sample 0.027, Quasars are 0.418, and PHAT0 synthetic data are 0.022. The results demonstrate th...

  10. Russian Character Recognition using Self-Organizing Map

    Science.gov (United States)

    Gunawan, D.; Arisandi, D.; Ginting, F. M.; Rahmat, R. F.; Amalia, A.

    2017-01-01

    The World Tourism Organization (UNWTO) in 2014 released that there are 28 million visitors who visit Russia. Most of the visitors might have problem in typing Russian word when using digital dictionary. This is caused by the letters, called Cyrillic that used by the Russian and the countries around it, have different shape than Latin letters. The visitors might not familiar with Cyrillic. This research proposes an alternative way to input the Cyrillic words. Instead of typing the Cyrillic words directly, camera can be used to capture image of the words as input. The captured image is cropped, then several pre-processing steps are applied such as noise filtering, binary image processing, segmentation and thinning. Next, the feature extraction process is applied to the image. Cyrillic letters recognition in the image is done by utilizing Self-Organizing Map (SOM) algorithm. SOM successfully recognizes 89.09% Cyrillic letters from the computer-generated images. On the other hand, SOM successfully recognizes 88.89% Cyrillic letters from the image captured by the smartphone’s camera. For the word recognition, SOM successfully recognized 292 words and partially recognized 58 words from the image captured by the smartphone’s camera. Therefore, the accuracy of the word recognition using SOM is 83.42%

  11. Intrusion Detection System using Self Organizing Map: A Survey

    Directory of Open Access Journals (Sweden)

    Kruti Choksi

    2014-12-01

    Full Text Available Due to usage of computer every field, Network Security is the major concerned in today’s scenario. Every year the number of users and speed of network is increasing, along with it online fraud or security threats are also increasing. Every day a new attack is generated to harm the system or network. It is necessary to protect the system or networks from various threats by using Intrusion Detection System which can detect “known” as well as “unknown” attack and generate alerts if any unusual behavior in the traffic. There are various approaches for IDS, but in this paper, survey is focused on IDS using Self Organizing Map. SOM is unsupervised, fast conversion and automatic clustering algorithm which is able to handle novelty detection. The main objective of the survey is to find and address the current challenges of SOM. Our survey shows that the existing IDS based on SOM have poor detection rate for U2R and R2L attacks. To improve it, proper normalization technique should be used. During the survey we also found that HSOM and GHSOM are advance model of SOM which have their own unique feature for better performance of IDS. GHSOM is efficient due to its low computation time. This survey is beneficial to design and develop efficient SOM based IDS having less computation time and better detection rate.

  12. Identifying individual sperm whales acoustically using self-organizing maps

    Science.gov (United States)

    Ioup, Juliette W.; Ioup, George E.

    2005-09-01

    The Littoral Acoustic Demonstration Center (LADC) is a consortium at Stennis Space Center comprising the University of New Orleans, the University of Southern Mississippi, the Naval Research Laboratory, and the University of Louisiana at Lafayette. LADC deployed three Environmental Acoustic Recording System (EARS) buoys in the northern Gulf of Mexico during the summer of 2001 to study ambient noise and marine mammals. Each LADC EARS was an autonomous, self-recording buoy capable of 36 days of continuous recording of a single channel at an 11.7-kHz sampling rate (bandwidth to 5859 Hz). The hydrophone selected for this analysis was approximately 50 m from the bottom in a water depth of 800 m on the continental slope off the Mississippi River delta. This paper contains recent analysis results for sperm whale codas recorded during a 3-min period. Results are presented for the identification of individual sperm whales from their codas, using the acoustic properties of the clicks within each coda. The recorded time series, the Fourier transform magnitude, and the wavelet transform coefficients are each used separately with a self-organizing map procedure for 43 codas. All show the codas as coming from four or five individual whales. [Research supported by ONR.

  13. 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.

  14. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

    Directory of Open Access Journals (Sweden)

    Radoi Emanuel

    2006-01-01

    Full Text Available The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART, which is compared to two standard classifiers, MLP (multilayer perceptron and fuzzy KNN ( nearest neighbors. While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

  15. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

    Science.gov (United States)

    Radoi, Emanuel; Quinquis, André; Totir, Felix

    2006-12-01

    The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN ([InlineEquation not available: see fulltext.] nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

  16. Expression cartography of human tissues using self organizing maps

    Science.gov (United States)

    2011-01-01

    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. Conclusions The SOM technique

  17. 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

  18. Expression cartography of human tissues using self organizing maps.

    Science.gov (United States)

    Wirth, Henry; Löffler, Markus; von Bergen, Martin; Binder, Hans

    2011-07-27

    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. 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. The SOM technique provides a more intuitive and

  19. 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.

  20. 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 ...

  1. 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

  2. Self-organizing map models of language acquisition.

    Science.gov (United States)

    Li, Ping; Zhao, Xiaowei

    2013-11-19

    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.

  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. 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...... for a quality assessment algorithm is to meet the low computational complexity requirement of mobile platforms used in national biometric systems, by military and police forces. We propose a computationally efficient means of predicting biometric performance based on a combination of unsupervised and supervised...... the SOM output and biometric performance. The quantitative evaluation performed demonstrates that our proposed quality assessment algorithm is a reasonable predictor of performance. The open source code of our algorithm will be posted at NIST NFIQ 2.0 website....

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

    Institute of Scientific and Technical Information of China (English)

    Shijun Zhang; Zhongliang Jing; Jianxun Li

    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 realworld 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.

  6. Image Clustering Method Based on Density Maps Derived from Self-Organizing Mapping: SOM

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2012-07-01

    Full Text Available A new method for image clustering with density maps derived from Self-Organizing Maps (SOM is proposed together with a clarification of learning processes during a construction of clusters. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. In accordance with the experimental results with Landsat-5 TM image, it takes more than 20000 of iteration for convergence of the SOM learning processes.

  7. Neighborhoods in Development: Human Development Index and Self-Organizing Maps

    Science.gov (United States)

    Rende, Sevinc; Donduran, Murat

    2013-01-01

    The Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore similarities among countries using the components of the HDI…

  8. Neighborhoods in Development: Human Development Index and Self-Organizing Maps

    Science.gov (United States)

    Rende, Sevinc; Donduran, Murat

    2013-01-01

    The Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore similarities among countries using the components of the HDI…

  9. Patterning exergy of benthic macroinvertebrate communities using self-organizing maps

    NARCIS (Netherlands)

    Park, Y.S.; Lek, S.; Scardi, M.; Verdonschot, P.F.M.; Jørgensen, S.E.

    2006-01-01

    Exergy is a measure of the free energy of a system with contributions from all components including the energy of organisms, and it is used as an ecological indicator. In this study, we implemented a self-organizing map (SOM) for patterning exergy of benthic macroinvertebrate communities. The datase

  10. Monitoring Scientific Developments from a Dynamic Perspective: Self-Organized Structuring To Map Neural Network Research.

    Science.gov (United States)

    Noyons, E. C. M.; van Raan, A. F. J.

    1998-01-01

    Using bibliometric mapping techniques, authors developed a methodology of self-organized structuring of scientific fields which was applied to neural network research. Explores the evolution of a data generated field structure by monitoring the interrelationships between subfields, the internal structure of subfields, and the dynamic features of…

  11. Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps

    Science.gov (United States)

    Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

    2010-09-01

    Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. In addition to manual data inspection, seismogram interpretation requires therefore new processing utilities for event detection, signal classification and data visualization. The use of machine learning techniques automatises decision processes and reveals the statistical properties of data. This approach is becoming more and more important and valuable for large and complex seismic records. Unsupervised learning allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. This study applies an unsupervised pattern recognition approach for the discovery, imaging and interpretation of temporal patterns in seismic array recordings. For this purpose, the data is parameterized by feature vectors, which combine different real-valued wavefield attributes for short time windows. Standard seismic analysis tools are used as feature generation methods, such as frequency-wavenumber, polarization and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure. The application to continuous recordings of seismic signals from an active volcano (Mount Merapi, Java, Indonesia) shows that volcano-tectonic and rockfall events can be detected and distinguished by clustering the feature vectors. Similar results are obtained in terms of correctly classifying events compared to a previously implemented supervised classification system. Furthermore, patterns in the background wavefield, that is the 24-hr cycle due to human activity, are intuitively visualized by means of the SOM representation. Finally, we apply our technique to an ambient seismic vibration record, which has been acquired for local site characterization. Disturbing wavefield patterns are identified which affect the quality of Love wave dispersion

  12. 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.

  13. 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.

  14. Evaluating the Quality of Predictive Geological Maps Produced using Self-Organizing Maps

    Science.gov (United States)

    Carter-McAuslan, Angela; Farquharson, Colin

    2016-04-01

    With increased data collection, extraction of useful information from large, often multi-dimensional (where each dimension is a unique data-type), datasets becomes a challenge. Associated with the problem of extracting usable information is the need to evaluate the information extracted to determine its validity. Traditionally, geophysical data has been interpreted in map or profile form one data-type at a time using primarily visual inspection by the interpreter. This approach become increasingly difficult as the dimensionality (e.g. number of data-types) of the dataset is increased. As such, new methods for discovering patterns in multi-dimensional geophysical datasets need to be investigated. Self-organizing maps (SOMs) are a class of unsupervised artificial neural network algorithm which are used to cluster multi-dimensional data while preserving the overall topology of the original dataset. As geophysical responses measured in the field are closely linked to the local geology it is postulated that SOMs can be employed to cluster multi-dimensional geophysical data in order to produce predictive geological maps. In the development of an effective work flow for creating predictive geological maps using SOMs, synthetic and real world test cases are used so that the predictive maps can be compared to a known geology. This comparison can be done through visual inspection. However, quantitative measures of clustering quality are also desired. In this project three different types of cluster quality measures are investigated: cluster morphology measures (e.g. the Quantization Error and the Dunn Index); class/cluster concatenation measures (e.g. Cluster Purity and Normalized Mutual Information); and decision-based measures (e.g. the Rand Index and F-Measure). SOM predictive mapping was applied to mapping the Baie Verte Peninsula on the north coast of the island of Newfoundland, Canada. The Baie Verte Peninsula is a region of complex geology with good regional

  15. Design of vector quantizer for image compression using self-organizing feature map and surface fitting.

    Science.gov (United States)

    Laha, Arijit; Pal, Nikhil R; Chanda, Bhabatosh

    2004-10-01

    We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization.

  16. Application of self-organizing maps in compounds pattern recognition and combinatorial library design.

    Science.gov (United States)

    Yan, Aixia

    2006-07-01

    In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.

  17. 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.

  18. An efficient approach to the travelling salesman problem using self-organizing maps.

    Science.gov (United States)

    Vieira, Frederico Carvalho; Dória Neto, Adrião Duarte; Costa, José Alfredo Ferreira

    2003-04-01

    This paper presents an approach to the well-known Travelling Salesman Problem (TSP) using Self-Organizing Maps (SOM). The SOM algorithm has interesting topological information about its neurons configuration on cartesian space, which can be used to solve optimization problems. Aspects of initialization, parameters adaptation, and complexity analysis of the proposed SOM based algorithm are discussed. The results show an average deviation of 3.7% from the optimal tour length for a set of 12 TSP instances.

  19. Intrusion Detection Method Based on Improved Growing Hierarchical Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    张亚平; 布文秀; 苏畅; 王璐瑶; 许涵

    2016-01-01

    Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individ-ual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.

  20. Effective palette indexing for image compression using self-organization of Kohonen feature map.

    Science.gov (United States)

    Pei, Soo-Chang; Chuang, Yu-Ting; Chuang, Wei-Hong

    2006-09-01

    The process of limited-color image compression usually involves color quantization followed by palette re-indexing. Palette re-indexing could improve the compression of color-indexed images, but it is still complicated and consumes extra time. Making use of the topology-preserving property of self-organizing Kohonen feature map, we can generate a fairly good color index table to achieve both high image quality and high compression, without re-indexing. Promising experiment results will be presented.

  1. Optimal Mapping of Torus Self-Organizing Map for Human Forearm Motions Discrimination on the Basis of Myoelectric Signals

    Science.gov (United States)

    Kiso, Atsushi; Seki, Hirokazu

    This paper describes an optimal mapping of the torus self-organizing map for a human forearm motion discrimination on the basis of the myoelectric signals. This study uses the torus self-organizing map (Torus-SOM) for the motion discrimination. The normal SOM identify input data into the same feature group by using the all units of map. Then there is a possibility of the misrecognition motion around the boundary lines of the motion groups. Therefore, this study proposes the mapping method of SOM that the learning units of the same motion concentrate on one local range and the learning unit groups of each motion separates enough. As a result, the variance in the same motion group becomes small and the variance between each motion groups becomes big. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

  2. Oscillating Adriatic temperature and salinity regimes mapped using the Self-Organizing Maps method

    Science.gov (United States)

    Matić, Frano; Kovač, Žarko; Vilibić, Ivica; Mihanović, Hrvoje; Morović, Mira; Grbec, Branka; Leder, Nenad; Džoić, Tomislav

    2017-01-01

    This paper aims to document salinity and temperature regimes in the middle and south Adriatic Sea by applying the Self-Organizing Maps (SOM) method to the available long-term temperature and salinity series. The data were collected on a seasonal basis between 1963 and 2011 in two dense water collecting depressions, Jabuka Pit and Southern Adriatic Pit, and over the Palagruža Sill. Seasonality was removed prior to the analyses. Salinity regimes have been found to oscillate rapidly between low-salinity and high-salinity SOM solutions, ascribed to the advection of Western and Eastern Mediterranean waters, respectively. Transient salinity regimes normally lasted less than a season, while temperature transient regimes lasted longer. Salinity regimes prolonged their duration after the major basin-wide event, the Eastern Mediterranean Transient, in the early 1990s. A qualitative relationship between high-salinity regimes and dense water formation and dynamics has been documented. The SOM-based analyses have a large capacity for classifying the oscillating ocean regimes in a basin, which, in the case of the Adriatic Sea, beside climate forcing, is an important driver of biogeochemical changes that impacts trophic relations, appearance and abundance of alien organisms, and fisheries, etc.

  3. 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.

  4. Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database

    Directory of Open Access Journals (Sweden)

    Mohamed Gebri

    2011-05-01

    Full Text Available Data mining is a valuable tool in meteorological applications. Properly selected data mining techniques enable researchers to process and analyze massive amounts of data collected by satellites and other instruments. Large spatial-temporal datasets can be analyzed using different linear and nonlinear methods. The Self-Organizing Map (SOM is a promising tool for clustering and visualizing high dimensional data and mapping spatial-temporal datasets describing nonlinear phenomena. We present results of the application of the SOM technique in regions of interest within the European re-analysis data set. The possibility of detecting climate change signals through the visualization capability of SOM tools is examined.

  5. SOMz: photometric redshift PDFs with self organizing maps and random atlas

    CERN Document Server

    Kind, M Carrasco

    2013-01-01

    In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two dimensional surface by using a process of competitive learning where neurons compete to more closely resemble the training data multidimensional space. The key feature of a SOM is that it retains the topology of the input set, revealing correlations between the attributes that are not easily identified. We test three different 2D topological mapping: rectangular, hexagonal, and spherical, by using data from the DEEP2 survey. We also explore different implementations and boundary conditions on the map and also introduce the idea of a random atlas where a large number of different maps are created and their individual predictions are aggregated to produce a more robust photometric redshift PDF. We a...

  6. An evolutionary algorithm for global optimization based on self-organizing maps

    Science.gov (United States)

    Barmada, Sami; Raugi, Marco; Tucci, Mauro

    2016-10-01

    In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.

  7. A limit-cycle self-organizing map architecture for stable arm control.

    Science.gov (United States)

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

    2017-01-01

    Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks. We show through computer simulations that our architecture generalizes well, achieves accurate, fast, and smooth arm movements, and is robust in the face of arm perturbations, map damage, and variations of internal timing parameters controlling the flow of activity. A robotic implementation is evaluated successfully without further training, demonstrating for the first time that limit cycle maps can control a physical robot arm. We conclude that architectures based on limit cycle maps can be organized to function effectively as neural controllers. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

    Science.gov (United States)

    Motter, Mark A.

    1998-01-01

    A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are

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

    Directory of Open Access Journals (Sweden)

    André Skupin

    Full Text Available BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: 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.

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

    Science.gov (United States)

    Moya, José M; Araujo, Alvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

    2009-01-01

    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.

  11. 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.

  12. 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.

  13. Reducing topological defects in self-organizing maps using multiple scale neighborhood functions.

    Science.gov (United States)

    Murakoshi, Kazushi; Sato, Yuichi

    2007-01-01

    In this paper, we propose a method of reducing topological defects in self-organizing maps (SOMs) using multiple scale neighborhood functions. The multiple scale neighborhood functions are inspired by multiple scale channels in the human visual system. To evaluate the proposed method, we applied it to the traveling salesman problem (TSP), and examined two indexes: the tour length of the solution and the number of kinks in the solution. Consequently, the two indexes are lower for the proposed method. These results indicate that our proposed method has the ability to reduce topological defects.

  14. 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.

  15. A convolutional recursive modified Self Organizing Map for handwritten digits recognition.

    Science.gov (United States)

    Mohebi, Ehsan; Bagirov, Adil

    2014-12-01

    It is well known that the handwritten digits recognition is a challenging problem. Different classification algorithms have been applied to solve it. Among them, the Self Organizing Maps (SOM) produced promising results. In this paper, first we introduce a Modified SOM for the vector quantization problem with improved initialization process and topology preservation. Then we develop a Convolutional Recursive Modified SOM and apply it to the problem of handwritten digits recognition. The computational results obtained using the well known MNIST dataset demonstrate the superiority of the proposed algorithm over the existing SOM-based algorithms.

  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. Imprecise correlated activity in self-organizing maps of spiking neurons.

    Science.gov (United States)

    Veredas, Francisco J; Mesa, Héctor; Martínez, Luis A

    2008-08-01

    How neurons communicate with each other to form effective circuits providing support to functional features of the nervous system is currently under debate. While many experts argue the existence of sparse neural codes based either on oscillations, neural assemblies or synchronous fire chains, other studies defend the necessity of a precise inter-neural communication to arrange efficient neural codes. As it has been demonstrated in neurophysiological studies, in the visual pathway between the retina and the visual cortex of mammals, the correlated activity among neurons becomes less precise as a direct consequence of an increase in the variability of synaptic transmission latencies. Although it is difficult to measure the influence of this reduction of correlated firing precision on the self-organization of cortical maps, it does not preclude the emergence of receptive fields and orientation selectivity maps. This is in close agreement with authors who consider that codes for neural communication are sparse. In this article, integrate-and-fire neural networks are simulated to analyze how changes in the precision of correlated firing among neurons affect self-organization. We observe how by keeping these changes within biologically realistic ranges, orientation selectivity maps can emerge and the features of neuronal receptive fields are significantly affected.

  18. 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.

  19. Similarity Analysis of EEG Data Based on Self Organizing Map Neural Network

    Directory of Open Access Journals (Sweden)

    Ibrahim Salem Jahan

    2014-01-01

    Full Text Available The Electroencephalography (EEG is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5~Hz to 60~Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly.

  20. 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.

  1. A self-organizing maps classifier structure for brain computer interfaces

    Directory of Open Access Journals (Sweden)

    Leandro Bueno

    Full Text Available AbstractIntroductionBrain Computer Interfaces provide an alternative communication path to severe paralyzed people and uses electrical signals related to brain activity in order to identify the user’s intention. In this paper a classifier based on a Self-Organizing Map is introduced.MethodsElectroencephalography signal is used on this work as a source for the user’s intention. This signal represents the brain activity and is processed in order to extract the frequency features presented to the classifier, which uses a Self-Organizing Map and a series of probability masks in order to identify the correct class.ResultsThe proposed structure was evaluated using a dataset of Electroencephalography with three mental tasks. The system was able to identify the different states of the users intention with an accuracy of 71.21% for a three-class problem using only 25 neurons for one of the users.ConclusionThe classifier proposed in this paper has an accuracy that is around the value of similar works in the literature, using the same data, but using a small time window for the classification, meaning the system can have a better time response for the user.

  2. Assessment of metal contamination in dregded sediments using fractionation and Self-Organizing Maps.

    Science.gov (United States)

    Arias, R; Barona, A; Ibarra-Berastegi, G; Aranguiz, I; Elías, A

    2008-02-28

    Although total metal content is frequently the initial approach for measuring pollution, no information is provided about mobility and environmental risk. In this paper, a metal fractionation (sequential extraction) technique and artificial neural networks (Self-Organizing Maps, SOMs) have been used jointly to evaluate the pollution level of the sediments dredged from the dry dock of a former shipyard in the Bilbao estuary (Bizkaia, Spain). The load pollution index (LPI) for the upper, middle and bottom layers of the sediments was 7.65, 8.22 and 10.01, respectively, for six metals (Cu, Mn, Ni, Cr, Pb and Zn). This showed that upper sediments were less polluted than the lower ones. Consequently, a reduction in the pollution level of metal discharged into the river in recent years was confirmed. According to fractionation results, the most mobile minor elements were Cu, Pb and Zn, as they are mainly associated with the non-residual fractions. The statistical approach of Self-Organizing Maps (SOMs) revealed that Ni, Pb and Zn amounts in the residual fraction followed the same pattern associated with simultaneous discharges of slags into the river. However, other hazardous discharge sources are responsible for the high accumulation of those metals in the non-residual fractions.

  3. Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India.

    Science.gov (United States)

    Murty, Upadhyayula Suryanarayana; Srinivasa Rao, Mutheneni; Misra, Sunil

    2008-09-01

    Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.

  4. 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...... 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...... audio-visual speech percepts and to measure coarticulatory effects....

  5. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  6. Clustering of landforms using self-organizing maps (SOM) in the west of Fars province

    Science.gov (United States)

    Mokarram, Marzieh; Sathyamoorthy, Dinesh

    2016-06-01

    The aim of this study is to cluster landforms in the west of the Fars province, Iran using self-organizing maps (SOM). In SOM, according to qualitative data, the clustering tendencies of landforms were investigated using six morphometric parameters, which were slope, profile, plan, elevation, curvature and aspect. First, topographic position index (TPI) was used to prepare the landform classification map. The results of SOM showed that there were five classes for landform classification in the study area. Cluster 5 corresponds to high slope, high elevation but with different of concavity and convexity that consist of ridge landforms. Cluster 3 corresponds to flat areas, possibly plantation areas, in medium elevation and almost flat terrain. Clusters 1, 2 and 4 correspond to channels with different slope conditions.

  7. Deriving Photometric Redshifts using Fuzzy Archetypes and Self-Organizing Maps. I. Methodology

    CERN Document Server

    Speagle, Joshua S

    2015-01-01

    We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large numbers of galaxy spectral templates into a corrresponding collection of "fuzzy archetypes" using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission line strengths. To bypass widely seperated degeneracies in parameter space (e.g., the redshift-reddening degeneracy), we train Self-Organizing Maps (SOMs) on a large "model catalogs" generated from appropriate Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs) using the associated set of inverse mappings from the SOM to the underlying model parameters. This combined approach enables the multi-modal exploration of known variation among galaxy spectral energy distributions (S...

  8. Self-Organizing Maps. An application to the OGLE data and the Gaia Science Alerts

    CERN Document Server

    Wyrzykowski, Lukasz

    2008-01-01

    Self-Organizing Map (SOM) is a promising tool for exploring large multi-dimensional data sets. It is quick and convenient to train in an unsupervised fashion and, as an outcome, it produces natural clusters of data patterns. An example of application of SOM to the new OGLE-III data set is presented along with some preliminary results. Once tested on OGLE data, the SOM technique will also be implemented within the Gaia mission's photometry and spectrometry analysis, in particular, in so-called classification-based Science Alerts. SOM will be used as a basis of this system as the changes in brightness and spectral behaviour of a star can be easily and quickly traced on a map trained in advance with simulated and/or real data from other surveys.

  9. 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.

  10. Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    J. Pavlovicova

    2007-04-01

    Full Text Available In this contribution, human face as biometric is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron and PCA (principal component analysis. This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and RBF (radial basis function networks, and to a system using MLP as a feature extractor and MLP and RBF networks in the role of classifier. Also a two-stage method for face recognition is presented, in which Kohonen self-organizing map is used as a feature extractor. MLP and RBF network are used as classifiers. In order to obtain deeper insight into presented methods, also visualizations of internal representation of input data obtained by neural networks are presented.

  11. 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.

  12. Static Performance of Wireless Localization Algorithm Exploiting Self-Organizing Maps

    Science.gov (United States)

    Tinh, Pham Doan; Kawai, Makoto

    In wireless communications, determining the physical location of nodes (localization) is very important for many network services and protocols. This paper evaluates the static performance of wireless localization algorithm exploiting Self-Organizing Maps (SOM) to deal with this issue. Our proposed algorithm utilizes only connectivity information and information from some heard anchors in the network to determine the location of nodes. By introducing an efficient two-hop utilization scheme and the multi-hop anchor update, the algorithm has maximized the correlation between neighboring nodes and the global topology in distributed implementation of SOM. From our intensive simulations on various static network deployment scenarios, the results show that the proposed scheme achieves very good localization accuracy. It also reduces the SOM learning steps to around 15 to 30 steps to overcome the huge computational problem of the classical SOM.

  13. Analyzing the Changes in Online Community based on Topic Model and Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    Thanh Ho

    2015-07-01

    Full Text Available In this paper, we propose a new model for two purposes: (1 discovering communities of users on social networks via topics with the temporal factor and (2 analyzing the changes in interested topics and users in communities in each period of time. This model, we use Kohonen network (Self-Organizing Map combining with the topic model. After discovering communities, results are shown on output layers of Kohonen. Based on the output layer of Kohonen, we focus on analyzing the changes in interested topics and users in online communities. Experimenting the proposed model with 194 online users and 20 topics. These topics are detected from a set of Vietnamese texts on social networks in the higher education field.

  14. Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function.

    Science.gov (United States)

    Hikawa, Hiroomi; Maeda, Yutaka

    2015-11-01

    Many self-organizing maps (SOMs) implemented on hardware restrict their neighborhood function values to negative powers of two. In this paper, we propose a novel hardware friendly neighborhood function that is aimed to improve the vector quantization performance of hardware SOM. The quantization performance of the hardware SOM with the proposed neighborhood function is examined by simulations. Simulation results show that the proposed function can improve the hardware SOM's vector quantization capability even though the function value is restricted to negative powers of two. Then, the hardware SOM is implemented on field-programmable gate array to find out the hardware cost and performance speed of the proposed neighborhood function. Experimental results show that the proposed neighborhood function can improve SOM's quantization performance without additional hardware cost or slowing down the operating speed. Due to fully parallel operation, the proposed SOM with 16×16 neurons achieves a performance of 25 344 million connections updates per second.

  15. World Expo 2010 Pavilions Clustering Analysis Based on Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    LI Qianqian; GU Jifa

    2016-01-01

    This paper reports the classification of 90 sample pavilions in Shanghai World Expo.An artificial intelligence based nonlinear clustering method known as Self-Organizing Map (SOM) has been used to classify expo pavilions.SOM is an efficient tool for visualization of multidimensional data.To conduct the classification,four characteristics namely Hurst exponent for queue length,Hurst exponent for waiting time,mean queue length and mean waiting time have been applied.The classification results show that Shanghai World Expo pavilions can be optimally classified into four classes.This result will shed light on further studies that how to manage the queue of World Expo pavilions in the future.

  16. 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).

  17. Clustering analysis of western North Pacific Tropical Cyclone tracks using the Self Organizing Map

    Science.gov (United States)

    Kim, H.; Seo, K.

    2013-12-01

    A cluster analysis using Self Organizing Map (SOM) is used to characterize tropical cyclone (TC) tracks over the western North Pacific. A False Discovery Rate (FDR) method is used to objectively determine an optimum cluster number. For 620 TC tracks over the WNP from June-October during 1979-2010, the five clusters for TC tracks are selected. These can further be categorized into three major patterns: straight-moving track, recurving track, and quasi-random pattern. Each pattern is characterized by land falling regions: near South and East China, East Asia, and off-shore of Japan. In addition, each pattern shows distinctive properties in its traveling distance, lifetime, intensity (mean minimum sea level pressure), and genesis location. It is revealed that these three patterns are associated with the large-scale dynamics such as variability of the western Pacific subtropical high and the Madden-Julian Oscillation. The impacts of El Nino and NAO will be discussed.

  18. Increasing water vapor transport to the Greenland Ice Sheet revealed using self-organizing maps

    Science.gov (United States)

    Mattingly, Kyle S.; Ramseyer, Craig A.; Rosen, Joshua J.; Mote, Thomas L.; Muthyala, Rohi

    2016-09-01

    The Greenland Ice Sheet (GrIS) has been losing mass in recent decades, with an acceleration in mass loss since 2000. In this study, we apply a self-organizing map classification to integrated vapor transport data from the ERA-Interim reanalysis to determine if these GrIS mass loss trends are linked to increases in moisture transport to Greenland. We find that "moist" days (i.e., days featuring anomalously intense water vapor transport to Greenland) were significantly more common during 2000-2015 compared to 1979-1994. Furthermore, the two most intense GrIS melt seasons during the last 36 years were either preceded by a record percentage of moist winter days (2010) or occurred during a summer with a record frequency of moist days (2012). We hypothesize that moisture transport events alter the GrIS energy budget by increasing downwelling longwave radiation and turbulent fluxes of sensible and latent energy.

  19. Combining Self-organizing Feature Map with Support Vector Regression Based on Expert System

    Institute of Scientific and Technical Information of China (English)

    WANGLing; MUZhi-Chun; GUOHui

    2005-01-01

    A new approach is proposed to model nonlinear dynamic systems by combining SOM(self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalization performance in comparison with the single SVR model.

  20. An approach to the analysis of SDSS spectroscopic outliers based on Self-Organizing Maps

    CERN Document Server

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

    2013-01-01

    Aims. A new method is 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 DPAC (Data Processing and Analysis Consortium) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in Gaia final 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 (SOM) networks are used to organize the information in clusters of objects, as homogeneous as possible, according to their spectral energy distributions (SED), and to project them onto a 2-D 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 spectro...

  1. A Three-layered Self-Organizing Map Neural Network for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Sheng-Chai Chi

    2003-12-01

    Full Text Available In the commercial world today, holding the effective information through information technology (IT and the internet is a very important indicator of whether an enterprise has competitive advantage in business. Clustering analysis, a technique for data mining or data analysis in databases, has been widely applied in various areas. Its purpose is to segment the individuals in the same population according to their characteristics. In this research, an enhanced three-layered self-organizing map neural network, called 3LSOM, is developed to overcome the drawback of the conventional two-layered SOM through sight-inspection after the mapping process. To further verify its feasibility, the proposed model is applied to two common problems: the identification of four given groups of work-part images and the clustering of a machine/part incidence matrix. The experimental results prove that the data that belong to the same group can be mapped to the same neuron on the output layer of the 3LSOM. Its performance in clustering accuracy is good and is also comparable with that of the FSOM, FCM and k-Means.

  2. Visualized analysis of mixed numeric and categorical data via extended self-organizing map.

    Science.gov (United States)

    Hsu, Chung-Chian; Lin, Shu-Han

    2012-01-01

    Many real-world datasets are of mixed types, having numeric and categorical attributes. Even though difficult, analyzing mixed-type datasets is important. In this paper, we propose an extended self-organizing map (SOM), called MixSOM, which utilizes a data structure distance hierarchy to facilitate the handling of numeric and categorical values in a direct, unified manner. Moreover, the extended model regularizes the prototype distance between neighboring neurons in proportion to their map distance so that structures of the clusters can be portrayed better on the map. Extensive experiments on several synthetic and real-world datasets are conducted to demonstrate the capability of the model and to compare MixSOM with several existing models including Kohonen's SOM, the generalized SOM and visualization-induced SOM. The results show that MixSOM is superior to the other models in reflecting the structure of the mixed-type data and facilitates further analysis of the data such as exploration at various levels of granularity.

  3. 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.

  4. Improved Cluster Identification and Visualization in High-Dimensional Data Using Self-Organizing Maps

    Science.gov (United States)

    Manukyan, N.; Eppstein, M. J.; Rizzo, D. M.

    2011-12-01

    A Kohonen self-organizing map (SOM) is a type of unsupervised artificial neural network that results in a self-organized projection of high-dimensional data onto a low-dimensional feature map, wherein vector similarity is implicitly translated into topological closeness, enabling clusters to be identified. In recently published work [1], 209 microbial variables from 22 monitoring wells around the leaking Schuyler Falls Landfill in Clinton, NY [2] were analyzed using a multi-stage non-parametric process to explore how microbial communities may act as indicators for the gradient of contamination in groundwater. The final stage of their analysis used a weighted SOM to identify microbial signatures in this high dimensionality data set that correspond to clean, fringe, and contaminated soils. Resulting clusters were visualized with the standard unified distance matrix (U-matrix). However, while the results of this analysis were very promising, visualized boundaries between clusters in the SOM were indistinct and required manual and somewhat arbitrary identification. In this contribution, we introduce (i) a new cluster reinforcement (CR) phase to be run subsequent to traditional SOM training for automatic sharpening of cluster boundaries, and (ii) a new boundary matrix (B-matrix) approach for visualization of the resulting cluster boundaries. The CR-phase differs from standard SOM training in several ways, most notably by using a feature-based neighborhood function rather than a topologically-based neighborhood function. In contrast to the U-matrix, the B-matrix can be directly superimposed on heat maps of the individual features (as output by the SOM) using grid lines whose thickness corresponds to inter-cluster distances. By thresholding the displayed lines, one obtains hierarchical control of the visual level of cluster resolution. We first illustrate the advantages of these methods on a small synthetic test case, and then apply them to the Schuyler Falls landfill

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

    Directory of Open Access Journals (Sweden)

    Farid García-Lamont

    2016-08-01

    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.

  6. Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps

    Science.gov (United States)

    Glisan, Justin M.; Gutowski, William J.; Cassano, John J.; Cassano, Elizabeth N.; Seefeldt, Mark W.

    2016-07-01

    We analyze daily precipitation extremes from simulations of a polar-optimized version of the Weather Research and Forecasting (WRF) model. Simulations cover 19 years and use the Regional Arctic System Model (RASM) domain. We focus on Alaska because of its proximity to the Pacific and Arctic oceans; both provide large moisture fetch inland. Alaska's topography also has important impacts on orographically forced precipitation. We use self-organizing maps (SOMs) to understand circulation characteristics conducive for extreme precipitation events. The SOM algorithm employs an artificial neural network that uses an unsupervised training process, which results in finding general patterns of circulation behavior. The SOM is trained with mean sea level pressure (MSLP) anomalies. Widespread extreme events, defined as at least 25 grid points experiencing 99th percentile precipitation, are examined using SOMs. Widespread extreme days are mapped onto the SOM of MSLP anomalies, indicating circulation patterns. SOMs aid in determining high-frequency nodes, and hence, circulations are conducive to extremes. Multiple circulation patterns are responsible for extreme days, which are differentiated by where extreme events occur in Alaska. Additionally, several meteorological fields are composited for nodes accessed by extreme and nonextreme events to determine specific conditions necessary for a widespread extreme event. Individual and adjacent node composites produce more physically reasonable circulations as opposed to composites of all extremes, which include multiple synoptic regimes. Temporal evolution of extreme events is also traced through SOM space. Thus, this analysis lays the groundwork for diagnosing differences in atmospheric circulations and their associated widespread, extreme precipitation events.

  7. Reconstruction of Sub-Surface Velocities from Satellite Observations Using Iterative Self-Organizing Maps

    CERN Document Server

    Chapman, Christopher

    2016-01-01

    In this letter a new method based on modified self-organizing maps is presented for the reconstruction of deep ocean current velocities from surface information provided by satellites. This method takes advantage of local correlations in the data-space to improve the accuracy of the reconstructed deep velocities. Unlike previous attempts to reconstruct deep velocities from surface data, our method makes no assumptions regarding the structure of the water column, nor the underlying dynamics of the flow field. Using satellite observations of surface velocity, sea-surface height and sea-surface temperature, as well as observations of the deep current velocity from autonomous Argo floats to train the map, we are able to reconstruct realistic high--resolution velocity fields at a depth of 1000m. Validation reveals extremely promising results, with a speed root mean squared error of ~2.8cm/s, a factor more than a factor of two smaller than competing methods, and direction errors consistently smaller than 30 degrees...

  8. 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.

  9. Multi-dimensional coordination in cross-country skiing analyzed using self-organizing maps.

    Science.gov (United States)

    Lamb, Peter F; Bartlett, Roger; Lindinger, Stefan; Kennedy, Gavin

    2014-02-01

    This study sought to ascertain how multi-dimensional coordination patterns changed with five poling speeds for 12 National Standard cross-country skiers during roller skiing on a treadmill. Self-organizing maps (SOMs), a type of artificial neural network, were used to map the multi-dimensional time series data on to a two-dimensional output grid. The trajectories of the best-matching nodes of the output were then used as a collective variable to train a second SOM to produce attractor diagrams and attractor surfaces to study coordination stability. Although four skiers had uni-modal basins of attraction that evolved gradually with changing speed, the other eight had two or three basins of attraction as poling speed changed. Two skiers showed bi-modal basins of attraction at some speeds, an example of degeneracy. What was most clearly evident was that different skiers showed different coordination dynamics for this skill as poling speed changed: inter-skier variability was the rule rather than an exception. The SOM analysis showed that coordination was much more variable in response to changing speeds compared to outcome variables such as poling frequency and cycle length.

  10. 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

  11. 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

  12. 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

  13. 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 Netherla

  14. 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

  15. Self-Organizing Neural Network Map for the Purpose of Visualizing the Concept Images of Students on Angles

    Science.gov (United States)

    Kaya, Deniz

    2017-01-01

    The purpose of the study is to perform a less-dimensional thorough visualization process for the purpose of determining the images of the students on the concept of angle. The Ward clustering analysis combined with Self-Organizing Neural Network Map (SOM) has been used for the dimension process. The Conceptual Understanding Tool, which consisted…

  16. Assessment of hydrothermal processes associated with Proterozoic mineral systems in Finland using self-organizing maps.

    Science.gov (United States)

    Lerssi, J.; Sorjonen-Ward, P.; Fraser, S. J.; Ruotsalainen, A.

    2009-04-01

    An increasingly urgent challenge in mineral system analysis is to extract relevant information from diverse datasets, and to effectively discriminate between "hydrothermal noise" and alteration and structures that may relate to significant mineralization potential. The interpretation of geophysical data is notorious for the problem of ambiguity in defining source dimensions and geometry. An additional issue, which also applies to geochemical and hyperspectral datasets, in terrain that has been overprinted by several tectonic, metamorphic and hydrothermal events, is that while anomalies represent the sum of geological processes affecting an area, we are usually interesting in extracting the signals diagnostic of a mineralizing event. Spatial analysis using weights of evidence, fuzzy logic and neural networks have been widely applied to mineral prospectivity assessment in recent years. Here however, we present an alternative, albeit complementary approach, based on the concept of self-organizing maps [1], in which natural patterns in large, unstructured datasets are derived, correlated and readily visualized, provides an alternative approach to analysis of geophysical and geochemical anomalies and integration with other geological data. We have applied SiroSOM software to airborne and ground magnetic, EM and radiometric data for two mutually adjacent areas in eastern Finland that have superficially similar structural architecture and geophysical expression, yet differ significantly in terms of mineral system character: (1) the Outokumpu Cu-Co-Zn-Ni system, hosted by metamorphosed serpentinites and their hydrothermal derivatives, which are usually highly magnetic due to both magnetite and pyrrhotite; (2) the Hammaslahti Cu-Zn system, hosted by coarse-clastic turbidites intercalated with mafic volcanics and graphitic pelites having characteristically intense magnetic and EM responses. Although the initial stage of the analysis is unsupervised, ongoing iteration and

  17. Using self-organizing maps to detail synoptic connections between climate indices and Alaska weather

    Science.gov (United States)

    Winnan, Reynir C.

    Seasonal forecasts for Alaska strongly depend on the phases of Pacific Decadal Oscillation (PDO), El Nino-Southern Oscillation (ENSO), and warm water in the North Pacific called the North Pacific Mode or more popularly the "Pacific blob." The canonical descriptions of these climate indices are based on seasonal averages, and anomalies that are based on a long-term mean. The patterns highlight general geographical placement and display a sharp contrast between opposing phases, but this may be misleading since seasonal averages hide much of the synoptic variability. Self-organizing maps (SOMs) are a way of grouping daily sea level pressure (SLP) patterns, over many time realizations into a specified set of maps (e.g. 35 maps) that describe commonly occurring patterns. This study uses the SOMs in the context of climate indices to describe the range of synoptic patterns that are relevant for Alaska. This study found that the patterns common during a given phase of the PDO include subtle differences that would result in Alaska weather that is very different from what is expected from the canonical PDO description, thus providing some explanation for recent studies that find the PDO link to Alaska climate is weakening. SOMs analysis is consistent with recent studies suggesting that the pattern responsible for the 2014 Pacific warm blob is linked to tropical sea-surface temperature (SST) forcing. An analysis of the summer SLP SOMs in the context of Alaska wildland fires was also conducted. This analysis identified several commonly occurring patterns during summers with large areas burned. These patterns are characterized by low pressure in the Bering Sea, which would be consistent with increased storm activity and thus an ignition source for the fires. Identifying synoptic patterns that occur during a particular phase of a teleconnection index contributes towards understanding the mechanisms of how these indices influence the weather and climate of Alaska.

  18. Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation

    Science.gov (United States)

    Tamayo, Pablo; Slonim, Donna; Mesirov, Jill; Zhu, Qing; Kitareewan, Sutisak; Dmitrovsky, Ethan; Lander, Eric S.; Golub, Todd R.

    1999-03-01

    Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation--for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

  19. Self Organizing Map of Artificial Neural Network for Defining Level of Service Criteria of Urban Streets

    Directory of Open Access Journals (Sweden)

    Smruti Sourava Mohapatra

    2012-09-01

    Full Text Available In India, Level of Service (LOS is not well defined for urban streets. The analysis procedure followed in India is that developed by HCM 2000. Speed ranges of LOS categories for various urban Street Classes defined by HCM are appropriate for developed countries having homogenous type of traffic flow. India being a developing country its traffic is very much heterogeneous having vehicles of different operational characteristics. Therefore, LOS criteria in Indian context should be defined correctly considering the traffic and geometric characteristics of urban streets. Defining LOS is basically a classification problem and application of cluster analysis is found to be a suitable technique to solve the problem. Self Organizing Map (SOM a type of Artificial Neural Network (ANN used to solve this classification problem. For this study, lot of speed data is required for which GPS is found to be the most suitable method of data collection and hence extensively used. Free flow speed (FFS and average travel speed during peak and off peak hours inventory of road segments are used in this study. FFS ranges for different urban Street Classes and speed ranges of LOS categories found to be lower than that mentioned in HCM-2000.

  20. 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.

  1. The Automatic Method of EEG State Classification by Using Self-Organizing Map

    Science.gov (United States)

    Tamura, Kazuhiro; Shimada, Takamasa; Saito, Yoichi

    In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, when doctor diagnose the sleep stage, much labor and skill are required, and a quantitative and objective method is required for more accurate diagnosis. For this reason, an automatic diagnosis system must be developed. In this paper, we propose an automatic sleep stage diagnosis method by using Self Organizing Maps (SOM). Neighborhood learning of SOM makes input data which has similar feature output closely. This function is effective to understandable classifying of complex input data automatically. We applied Elman-type feedback SOM to EEG of not only normal subjects but also subjects suffer from disease. The spectrum of characteristic waves in EEG of disease subjects is often different from it of normal subjects. So, it is difficult to classifying EEG of disease subjects with the rule for normal subjects. On the other hand, Elman-type feedback SOM Classifies the EEG with features which data include and classifying rule is made automatically, so even the EEG with disease subjects is able to be classified automatically. And this Elman-type feedback SOM has context units for diagnosing sleep stages considering contextual information of EEG. Experimental results indicate that the proposed method is able to achieve sleep stage judgment along with doctor's diagnosis.

  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

    2017-08-29

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

    Directory of Open Access Journals (Sweden)

    Smith Terry J

    2004-03-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  4. 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.

  5. Applications of self-organizing maps for ecomorphological investigations through early ontogeny of fish.

    Directory of Open Access Journals (Sweden)

    Tommaso Russo

    Full Text Available 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.

  6. 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.

  7. Deriving photometric redshifts using fuzzy archetypes and self-organizing maps - I. Methodology

    Science.gov (United States)

    Speagle, Joshua S.; Eisenstein, Daniel J.

    2017-07-01

    We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large number of galaxy spectral templates into a corresponding collection of 'fuzzy archetypes' using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission-line strengths. To bypass widely separated degeneracies in parameter space (e.g. the redshift-reddening degeneracy), we train self-organizing maps (SOMs) on large 'model catalogues' generated from Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs). This combined approach enables the multimodal exploration of known variation among galaxy spectral energy distributions with minimal modelling assumptions. We demonstrate the power of this approach to recover full redshift PDFs using discrete Markov chain Monte Carlo sampling methods combined with SOMs constructed from Large Synoptic Survey Telescope ugrizY and Euclid YJH mock photometry.

  8. Hepatitis B Diagnosis Using Logical Inference and Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    G. S. Uttreshwar

    2008-01-01

    Full Text Available Despite all the standardization efforts made, medical diagnosis is still regarded as an art owing to the fact that that medical diagnosis requires an expertise in handling the uncertainty which is unavailable in today's computing machinery. Though artificial intelligence is not a new concept it has been widely recognized as a new technology in computer science. Numerous areas such as education, business, medical and manufacturing have made use of artificial intelligence. Problem statement: The proposed study investigated the potential of artificial intelligence techniques principally for medical applications. Neural network algorithms could possible provide an enhanced solution for medical problems. This study analyzed the application of artificial intelligence in conventional hepatitis B diagnosis. Approach: In this research, an intelligent system that worked on basis of logical inference utilized to make a decision on the type of hepatitis that is likely to appear for a patient, if it is hepatitis B or not. Then kohonen's self-organizing map network was applied to hepatitis data for predictions regarding the Hepatitis B which gives severity level on the patient. Results: SOM which is a class of unsupervised network was used as a classifier to predict the accuracy of Hepatitis B. Conclusion: We concluded that the proposed model gives faster and more accurate prediction of hepatitis B and it works as promising tool for predicting of routine hepatitis B from the clinical laboratory data.

  9. Multi-Dimensional Traffic Flow Time Series Analysis with Self-Organizing Maps

    Institute of Scientific and Technical Information of China (English)

    CHEN Yudong; ZHANG Yi; HU Jianming

    2008-01-01

    The two important features of self-organizing maps (SOM), topological preservation and easy visualization, give it great potential for analyzing multi-dimensional time series, specifically traffic flow time series in an urban traffic network. This paper investigates the application of SOM in the representation and prediction of multi-dimensional traffic time series. First, SOMs are applied to cluster the time series and to project each multi-dimensional vector onto a two-dimensional SOM plane while preserving the topological relationships of the original data. Then, the easy visualization of the SOMs is utilized and several explora-tory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vec-tors evolve with time. Finally, the k-nearest neighbor (kNN) algorithm is applied to the clustering result to perform short-term predictions of the traffic flow vectors. Analysis of real world traffic data shows the effec-tiveness of these methods for traffic flow predictions, for they can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously.

  10. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    Science.gov (United States)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  11. Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps

    Science.gov (United States)

    Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike

    2016-04-01

    Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997-2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014).

  12. 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.

  13. 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.

  14. Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

    Science.gov (United States)

    Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

    2017-04-01

    The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

  15. Classifying content-based Images using Self Organizing Map Neural Networks Based on Nonlinear Features

    Directory of Open Access Journals (Sweden)

    Ebrahim Parcham

    2014-07-01

    Full Text Available Classifying similar images is one of the most interesting and essential image processing operations. Presented methods have some disadvantages like: low accuracy in analysis step and low speed in feature extraction process. In this paper, a new method for image classification is proposed in which similarity weight is revised by means of information in related and unrelated images. Based on researchers’ idea, most of real world similarity measurement systems are nonlinear. Thus, traditional linear methods are not capable of recognizing nonlinear relationship and correlation in such systems. Undoubtedly, Self Organizing Map neural networks are strongest networks for data mining and nonlinear analysis of sophisticated spaces purposes. In our proposed method, we obtain images with the most similarity measure by extracting features of our target image and comparing them with the features of other images. We took advantage of NLPCA algorithm for feature extraction which is a nonlinear algorithm that has the ability to recognize the smallest variations even in noisy images. Finally, we compare the run time and efficiency of our proposed method with previous proposed methods.

  16. Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization

    Science.gov (United States)

    Silva, Leandro A.; Del-Moral-Hernandez, Emilio; Moreno, Ramon A.; Furuie, Sérgio S.

    2011-10-01

    Images are fundamental sources of information in modern medicine. The images stored in a database and divided in categories are an important step for image retrieval. For an automatic categorization process, detailed analysis is done regarding image representation and generalization method. The baseline method for this process, in the medical image context, is using thumbnails and K-nearest neighbor (KNN), which is easily implemented and has had satisfactory results in literature. This work addresses an alternative method for automatic categorization, which jointly uses discrete wavelet transform with Hu's moments for image representation and self-organizing maps (SOM) neural networks combined with the KNN classifier (SOM-KNN), for medical image categorization. Furthermore, extensive experiments are conducted, to define the best wavelet family and to select the best coefficients set, to consider the remaining wavelet coefficients set (not selected as the best ones) through their Hu's moments, and to carry out a contrastive study with other successful approaches for categorization. The categorization result from a database with 10,000 images in 116 categories yielded 81.8% of correct rate, which is much better than the 67.9% obtained by the baseline method; and the time consumed in classification processing with SOM-KNN is 100 times shorter than KNN.

  17. An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective.

    Science.gov (United States)

    Faigl, Jan

    2016-01-01

    In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

  18. An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

    Directory of Open Access Journals (Sweden)

    Jan Faigl

    2016-01-01

    Full Text Available In this paper, Self-Organizing Map (SOM for the Multiple Traveling Salesman Problem (MTSP with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots’ workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

  19. Classification of a set of vectors using self-organizing map- and rule-based technique

    Science.gov (United States)

    Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

    2005-02-01

    There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

  20. 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)

  1. The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System

    CERN Document Server

    Pratiwi, Dian

    2012-01-01

    This paper presents a technique in classifying the images into a number of classes or clusters desired by means of Self Organizing Map (SOM) Artificial Neural Network method. A number of 250 color images to be classified as previously done some processing, such as RGB to grayscale color conversion, color histogram, feature vector selection, and then classifying by the SOM Feature vector selection in this paper will use two methods, namely by PCA (Principal Component Analysis) and LSA (Latent Semantic Analysis) in which each of these methods would have taken the characteristic vector of 50, 100, and 150 from 256 initial feature vector into the process of color histogram. Then the selection will be processed into the SOM network to be classified into five classes using a learning rate of 0.5 and calculated accuracy. Classification of some of the test results showed that the highest percentage of accuracy obtained when using PCA and the selection of 100 feature vector that is equal to 88%, compared to when using...

  2. Self-organizing map and its application in the analysis of ambient noise characteristics

    Science.gov (United States)

    Meng, Chunxia; Li, Guijuan; Che, Shuwei; Bai, Jin

    2017-01-01

    The Self-organizing map (SOM) is an unsupervised neural network based on competitive learning, and can solve the problem that the center of clustering is unknown. SOM's theory and the implementation of algorithm are studied in this paper. Simulating example is given to approve the feasibility of SOM in characteristic assessment for multivariate sample. The Ambient sea noise measurement is made in August 2014 on some sea of China. The total source level was forecasted using "ROSS formula" and the sailing information. The statistical variability of broadband ambient noise at frequencies between 20Hz and 31.5 kHz is obtained using SOM. The comparison between measured sound pressure and forecasting pressure is given, and the preliminary analysis of the relationship between ambient noise level and vessels is carried out. The results provide the technical reference to understand the temporal and spatial statistical variability of ambient noise, and are an efficient tool in assessing the potential effect of shipping noise on marine mammals in the special sea area.

  3. 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)

  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. Cube Kohonen self-organizing map (CKSOM) model with new equations in organizing unstructured data.

    Science.gov (United States)

    Lim, Seng Poh; Haron, Habibollah

    2013-09-01

    Surface reconstruction by using 3-D data is used to represent the surface of an object and perform important tasks. The type of data used is important and can be described as either structured or unstructured. For unstructured data, there is no connectivity information between data points. As a result, incorrect shapes will be obtained during the imaging process. Therefore, the data should be reorganized by finding the correct topology so that the correct shape can be obtained. Previous studies have shown that the Kohonen self-organizing map (KSOM) could be used to solve data organizing problems. However, 2-D Kohonen maps are limited because they are unable to cover the whole surface of closed 3-D surface data. Furthermore, the neurons inside the 3-D KSOM structure should be removed in order to create a correct wireframe model. This is because only the outside neurons are used to represent the surface of an object. The aim of this paper is to use KSOM to organize unstructured data for closed surfaces. KSOM isused in this paper by testing its ability to organize medical image data because KSOM is mostly used in constructing engineering field data. Enhancements are added to the model by introducing class number and the index vector, and new equations are created. Various grid sizes and maximum iterations are tested in the experiments. Based on the results, the number of redundancies is found to be directly proportional to the grid size. When we increase the maximum iterations, the surface of the image becomes smoother. An area formula is used and manual calculations are performed to validate the results. This model is implemented and images are created using Dev C++ and GNUPlot.

  6. Using Self-Organizing Maps in Creation of an Ocean Forecasting System

    Science.gov (United States)

    Vilibic, I.; Zagar, N.; Cosoli, S.; Dadic, V.; Ivankovic, D.; Jesenko, B.; Kalinic, H.; Mihanovic, H.; Sepic, J.; Tudor, M.

    2014-12-01

    We present the first results of the NEURAL project (www.izor.hr/neural), which is dedicated to creation of an efficient and reliable ocean surface current forecasting system. This system is based on high-frequency (HF) radar measurements, numerical weather prediction (NWP) models and neural network algorithms (Self-Organizing Maps, SOM). Joint mapping of mesoscale ground winds and HF radars in a coastal area points to a high correlation between two sets, indicating that wind forecast may be used as a basis for forecasting ocean surface currents. NEURAL project consists of three modules: (i) the technological module which covers installation of new HF radars in the coastal area of the middle Adriatic, and implementation of data management procedures; (ii) the research module which deals with an assessment of different combinations of input variables (radial vs. Cartesian vectors, original vs. detided vs. filtered series, WRF-ARW vs. Aladin meteorological model), all in order to get the best hindcasted surface currents; and finally (iii) the operational module in which NWP operational products will be used for short-term forecasting of ocean surface currents. Both historical and newly observed HF radar data, as well as reanalysis and operational NWP model runs will be used within the (ii) and (iii) modules of the project. Finally, the observed, hindcasted and forecasted ocean current will be compared to the operational ROMS model outputs to compare skill reliability of the forecasting system based on neural network approach to the skill and reliability of numerical ocean models. We expect the forecasting system based on neural network approach to be more reliable than the one based on numerical ocean model as it is more exclusively based on measurements. Disadvantages of such a system are that it can be applied only in areas where long series surface currents measurements exist and where the recognized patterns can be properly ascribed to a forcing field.

  7. Recent Changes in Blocking Characteristics Assessed Using Self-Organizing Maps

    Science.gov (United States)

    Francis, J. A.; Skific, N.; Vavrus, S. J.; Cassano, J. J.; Cassano, E.

    2015-12-01

    Blocking anticyclones are known to be associated with persistent weather patterns that often lead to extreme weather events. An outstanding question, however, is whether the frequency and/or intensity of these dynamical features are changing in response to human-caused climate change, and in particular, to a disproportionately warming Arctic. In this presentation we describe a study using a pattern-recognition/clustering tool called Self-Organizing Maps (SOMs) to investigate the temporal behavior of blocks over recent decades, and attribute any changes to either frequency shifts in characteristic atmospheric patterns or to cluster-mean changes in a blocking characteristic for a given pattern. In this application, we use single contours of 500-hPa heights from reanalyses to identify characteristic ridge/trough patterns in the upper-level flow in the northern hemisphere. By mapping daily assessments of blocking occurrence and intensity to the SOM-derived patterns, we investigate temporal and regional changes in blocking. We find that the relative frequency of blocking days - defined as the number of blocked days in a particular pattern relative to the total number of days in that pattern - has increased significantly (> 95% confidence) in all regions: 60% of the patterns in the Atlantic, 80% in the Pacific, and 30% over continents. While the increases over oceans occur in all seasons, the higher occurrence of blocks over continents is confined to the warm season. Blocking intensity, however, has generally decreased, as expected with a weaker poleward temperature gradient. Blocking frequency and Arctic amplification are positively correlated over the Pacific and continental sectors, implying that as the Arctic continues to warm faster than mid-latitudes, the number of days with blocks should continue to increase. Better understanding the mechanisms for changes in blocks and other high-amplitude jet-stream patterns in a warming world will enhance predictability and

  8. Topological Maps of Kohonen Self-Organization (SOM Applied To the Study of Sediments Contaminated With Heavy Metals

    Directory of Open Access Journals (Sweden)

    Naoual Monyr, Abdelaziz Abdallaoui

    2016-04-01

    Full Text Available This work aims to apprehend the history of the metallic pollution in the retaining of the Sidi Chahed dam since its inception in 1997 through the sediments of 4 carrots levied the level the embouchures the main wadis (Wadi Mikkes, embouchure Wadi Mikkes, embouchure Wadi Lmallah and embouchure Wadi Jajouiyne. The classification of data by self-organizing maps Kohonen allowed understanding and visualizing the spatial and temporal distribution of samples. Principal component analysis (PCA and hierarchical classification of SOM maps (SOM AHC were also used for validating the obtained results. Correlations and relationships between the samples and the variables can be easily visualized using the viewing of planes of components of the self-organizing map. The results have highlighted the dependencies between the different metal elements and the classification of studied sediments into four classes into function of four stations coring and their pollution levels

  9. 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.

  10. 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.

  11. Atmospheric Drivers of Greenland Surface Melt Revealed by Self-Organizing Maps

    Science.gov (United States)

    Mioduszewski, J. R.; Rennermalm, A. K.; Hammann, A.; Tedesco, M.; Noble, E. U.; Stroeve, J. C.; Mote, T. L.

    2016-01-01

    Recent acceleration in surface melt on the Greenland ice sheet (GrIS) has occurred concurrently with a rapidly warming Arctic and has been connected to persistent, anomalous atmospheric circulation patterns over Greenland. To identify synoptic setups favoring enhanced GrIS surface melt and their decadal changes, we develop a summer Arctic synoptic climatology by employing self-organizing maps. These are applied to daily 500 hPa geopotential height fields obtained from the Modern Era Retrospective Analysis for Research and Applications reanalysis, 1979-2014. Particular circulation regimes are related to meteorological conditions and GrIS surface melt estimated with outputs from the Modèle Atmosphérique Régional. Our results demonstrate that the largest positive melt anomalies occur in concert with positive height anomalies near Greenland associated with wind, temperature, and humidity patterns indicative of strong meridional transport of heat and moisture. We find an increased frequency in a 500 hPa ridge over Greenland coinciding with a 63% increase in GrIS melt between the 1979-1988 and 2005-2014 periods, with 75.0% of surface melt changes attributed to thermodynamics, 17% to dynamics, and 8.0% to a combination. We also confirm that the 2007-2012 time period has the largest dynamic forcing relative of any period but also demonstrate that increased surface energy fluxes, temperature, and moisture separate from dynamic changes contributed more to melt even during this period. This implies that GrIS surface melt is likely to continue to increase in response to an ever warmer future Arctic, regardless of future atmospheric circulation patterns.

  12. Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

    Institute of Scientific and Technical Information of China (English)

    HASI Bagan; MA Jianwen; LI Qiqing; HAN Xiuzhen; LIU Zhili

    2004-01-01

    Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. However, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classification. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Municipality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likelihood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.

  13. Kohonen self-organizing map estimator for the reference crop evapotranspiration

    Science.gov (United States)

    Adeloye, Adebayo J.; Rustum, Rabee; Kariyama, Ibrahim D.

    2011-08-01

    Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman-Monteith benchmark model, are time-consuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self-organizing map (KSOM), unsupervised artificial neural networks, to predict the ETo. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ETo (based on the Food and Agricultural Organization (FAO) Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM-based ETo estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM-based estimates were also found to be significantly superior to those estimated using currently recommended empirical ETo methods for data scarce situations such as those in developing countries.

  14. Self-organizing maps application for the clustering of the provinces of Poland according to the construction industry activity

    Science.gov (United States)

    Juszczyk, Michał

    2017-07-01

    The self-organizing maps (SOM) are useful tools for the purposes of the data exploration. Their ability to transform n-dimensional signal pattern into two dimensional map is used in this paper to cluster provinces of Poland. Main assumption was to perform the clustering on the basis of statistical information concerning characteristics of construction industry. Output of construction industry and number of completed construction objects ordered by provinces was presented to the number of SOM neural networks. As a result of the computations and neural simulations two dimensional topologically ordered feature map of groups of provinces was proposed.

  15. A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

    Science.gov (United States)

    Philips, Ryan T.; Chakravarthy, V. Srinivasa

    2017-01-01

    A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation (rc). The rc between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps.

  16. A Pattern Analysis of Using Self-Organizing-Maps in a Unspoken Vowel Recognition System Based on Surface Electromyogram

    Science.gov (United States)

    Fukumoto, Hisao; Noguchi, Yusuke; Ohchi, Masashi; Furukawa, Tatsuya

    In this paper, we present some results of analysis on surface electromyogram (SEMG) using Self-Organizing -Maps (SOM) algorithm, which is one of the neural network algorithm, for unspoken vowel recognition system. Three pairs of electrodes were placed on facial muscles and SEMG signals were recorded. We have examined the classification of three pairs of the values of activity for each muscle using SOM algorithm. The SOM algorithm is also able to translate the multi-dimensional vectors of RMS values of SEMG signal into the two-dimensional map.

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

    Directory of Open Access Journals (Sweden)

    Luciana Scotti

    2012-01-01

    Full Text Available 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 13C NMR data of diterpenes related with their botanical occurrence were used to generate self-organizing maps (SOM. 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.

  18. Representation of Molecular Electrostatic Potentials of Biopolymer by Self-organizing Feature Map

    Institute of Scientific and Technical Information of China (English)

    QIAO,Xue-Bin(乔学斌); JIANG,Bo(姜波); HOU,Ting-Jun(侯廷军); XU,Xiao-Jie(徐筱杰)

    2001-01-01

    The Kohonen serf-organizing map was introduced to map theprotein molecular surface features.The protein or polypeptideproperties,such as shape and molecular electrostatic poten-rial,can be visualized by seff-organizing map,which wastrained by the 3D surface coordinates.Such maps allow thevisual comparison of molecular properties between proteinshaving common topological or chemical features.``

  19. Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

    Directory of Open Access Journals (Sweden)

    Karlovsky Petr

    2008-06-01

    Full Text Available Abstract Background One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters. Results We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers. Conclusion Our specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown.

  20. Hazard Detection for Motorcycles via Accelerometers: A Self-Organizing Map Approach.

    Science.gov (United States)

    Selmanaj, Donald; Corno, Matteo; Savaresi, Sergio M

    2016-06-09

    This paper deals with collision and hazard detection for motorcycles via inertial measurements. For this kind of vehicles, the most difficult challenge is to distinguish road's anomalies from real hazards. This is usually done by setting absolute thresholds on the accelerometer measurements. These thresholds are heuristically tuned from expensive crash tests. This empirical method is expensive and not intuitive when the number of signals to deal with grows. We propose a method based on self-organized neural networks that can deal with a large number of inputs from different types of sensors. The method uses accelerometer and gyro measurements. The proposed approach is capable of recognizing dangerous conditions although no crash test is needed for training. The method is tested in a simulation environment; the comparison with a benchmark method shows the advantages of the proposed approach.

  1. Letter to the editor: Generation of self organized critical connectivity network map (SOCCNM) of randomly situated water bodies during flooding process

    OpenAIRE

    B. S. Daya Sagar

    2001-01-01

    This letter presents a brief framework based on nonlinear morphological transformations to generate a self organized critical connectivity network map (SOCCNM) in 2-dimensional space. This simple and elegant framework is implemented on a section that contains a few simulated water bodies to generate SOCCNM. This is based on a postulate that the randomly situated surface water bodies of various sizes and shapes self organize during flooding process.

  2. Letter to the editor: Generation of self organized critical connectivity network map (SOCCNM of randomly situated water bodies during flooding process

    Directory of Open Access Journals (Sweden)

    B. S. Daya Sagar

    2001-01-01

    Full Text Available This letter presents a brief framework based on nonlinear morphological transformations to generate a self organized critical connectivity network map (SOCCNM in 2-dimensional space. This simple and elegant framework is implemented on a section that contains a few simulated water bodies to generate SOCCNM. This is based on a postulate that the randomly situated surface water bodies of various sizes and shapes self organize during flooding process.

  3. 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.

  4. Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps

    Science.gov (United States)

    Obermayer, K.; Blasdel, G. G.; Schulten, K.

    1992-05-01

    We report a detailed analytical and numerical model study of pattern formation during the development of visual maps, namely, the formation of topographic maps and orientation and ocular dominance columns in the striate cortex. Pattern formation is described by a stimulus-driven Markovian process, the self-organizing feature map. This algorithm generates topologically correct maps between a space of (visual) input signals and an array of formal ``neurons,'' which in our model represents the cortex. We define order parameters that are a function of the set of visual stimuli an animal perceives, and we demonstrate that the formation of orientation and ocular dominance columns is the result of a global instability of the retinoptic projection above a critical value of these order parameters. We characterize the spatial structure of the emerging patterns by power spectra, correlation functions, and Gabor transforms, and we compare model predictions with experimental data obtained from the striate cortex of the macaque monkey with optical imaging. Above the critical value of the order parameters the model predicts a lateral segregation of the striate cortex into (i) binocular regions with linear changes in orientation preference, where iso-orientation slabs run perpendicular to the ocular dominance bands, and (ii) monocular regions with low orientation specificity, which contain the singularities of the orientation map. Some of these predictions have already been verified by experiments.

  5. Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor

    Science.gov (United States)

    Hao, Yanling; Sun, Genyun

    2016-10-01

    Seismic image classification is of vital importance for extracting damage information and evaluating disaster losses. With the increasing availability of high resolution remote sensing images, automatic image classification offers a unique opportunity to accommodate the rapid damage mapping requirements. However, the diversity of disaster types and the lack of uniform statistical characteristics in seismic images increase the complexity of automated image classification. This paper presents a novel automatic seismic image classification approach by integrating an adaptive spectral-textural descriptor into gravitational self-organizing map (gSOM). In this approach, seismic image is first segmented into several objects based on mean shift (MS) method. These objects are then characterized explicitly by spectral and textural feature quantization histograms. To objectify the image object delineation adapt to various disaster types, an adaptive spectral-textural descriptor is developed by integrating the histograms automatically. Subsequently, these objects as classification units are represented by neurons in a self-organizing map and clustered by adjacency gravitation. By moving the neurons around the gravitational space and merging them according to the gravitation, the object-based gSOM is able to find arbitrary shape and determine the class number automatically. Taking advantage of the diversity of gSOM results, consensus function is then conducted to discover the most suitable classification result. To confirm the validity of the presented approach, three aerial seismic images in Wenchuan covering several disaster types are utilized. The obtained quantitative and qualitative experimental results demonstrated the feasibility and accuracy of the proposed seismic image classification method.

  6. 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.

  7. A Robust Approach for the Background Subtraction Based on Multi-Layered Self-Organizing Maps.

    Science.gov (United States)

    Gemignani, Giorgio; Rozza, Alessandro

    2016-11-01

    Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization. According to the winner-take-all rule, each layer of the network models an independent state of the background scene, in response to external disturbing conditions, such as illumination variations, moving backgrounds, and jittering. As a result, our background subtraction method exhibits high generalization capabilities that in combination with a post-processing filtering schema allow to produce accurate motion segmentation. Moreover, we propose an approach to detect anomalous events (such as camera motion) that require background model re-initialization. We describe our method in full details and we compare it against the most recent background subtraction approaches. Experimental results for video sequences from the 2012 and 2014 CVPR Change Detection data sets demonstrate how our methodology outperforms many state-of-the-art methods in terms of detection rate.

  8. 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.

  9. 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.

  10. Examining moisture pathways and extreme precipitation in the U.S. Intermountain West using self-organizing maps

    Science.gov (United States)

    Swales, Dustin; Alexander, Mike; Hughes, Mimi

    2016-02-01

    Self-organizing maps (SOMs) were used to explore relationships between large-scale synoptic conditions, especially vertically integrated water vapor transport (IVT), and extreme precipitation events in the U.S. Intermountain West (IMW). By examining spatial patterns in the IVT, pathways are identified where moisture can penetrate into the IMW. A substantial number of extreme precipitation events in the IMW are associated with infrequently occurring synoptic patterns. The transition frequency between each of the SOM nodes, which indicate temporal relationships between the patterns, identified two synoptic settings associated with extreme precipitation in the IMW: (1) a landfalling, zonally propagating trough that results in a concentrated IVT band that moves southward as the system moves inland and (2) a southwesterly storm track associated with strong ridging over the coast that results in persistent IVT transport into the Pacific Northwest that can last for several days.

  11. A COMPARISON STUDY FOR INTRUSION DATABASE (KDD99, NSL-KDD BASED ON SELF ORGANIZATION MAP (SOM ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    LAHEEB M. IBRAHIM

    2013-02-01

    Full Text Available Detecting anomalous traffic on the internet has remained an issue of concern for the community of security researchers over the years. The advances in the area of computing performance, in terms of processing power and storage, have fostered their ability to host resource-intensive intelligent algorithms, to detect intrusive activity, in a timely manner. As part of this project, we study and analyse the performance of Self Organization Map (SOM Artificial Neural Network, when implemented as part of an Intrusion Detection System, to detect anomalies on acknowledge Discovery in Databases KDD 99 and NSL-KDD datasets of internet traffic activity simulation. Results obtained are compared and analysed based on several performance metrics, where the detection rate for KDD 99 dataset is 92.37%, while detection rate for NSL-KDD dataset is 75.49%.

  12. Modelling the interannual variability of extreme wave climate combining a time-dependent GEV model and Self-Organizing Maps

    Science.gov (United States)

    Izaguirre, Cristina; Mendez, Fernando J.; Camus, Paula; Minguez, Roberto; Menendez, Melisa; Losada, Iñigo J.

    2010-05-01

    It is well known that the seasonal-to-interannual variability of extreme wave climate is linked to the anomalies of the atmosphere circulation. In this work, we analyze the relationships between extreme significant wave height at a particular site and the synoptic-scale weather type. We combine a time-dependent Generalized Extreme Value (GEV) model for monthly maxima and self-organizing maps (SOM) applied to monthly mean sea level pressure field (SLP) anomalies. These time-varying SLP anomalies are encoded using principal component analysis, obtaining the corresponding spatial patterns (Empirical Orthogonal Functions, EOFs) and the temporal modes (PC, principal components). The location, scale and shape parameters of the GEV distribution are parameterized in terms of harmonic functions (seasonality) and linear covariates for the PCs (interannual variability) and the model is fitted using standard likelihood theory and an automatic parameter selection procedure, which avoids overparameterization. Thus, the resulting anomalies of the location and scale parameters with respect to the seasonality are projected to the SOM lattice obtaining the influence of every weather type on the extreme wave height probability distribution (and subsequently, return-level quantiles). The use of Self-organizing maps allows an easy visualization of the results. The application of the method to different areas in the North Atlantic Ocean helps us to quantify the importance of the North Atlantic Oscillation and the East Atlantic pattern in the location and scale parameters of the GEV probability distribution. Additionally, this work opens new forecasting possibilities for the probabilities of extreme events based on synoptic-scale patterns.

  13. Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

    Science.gov (United States)

    Cibrián, Rosa; Soria, Emilio; Serrano, Antonio-José; Aguiló, Luz; Paredes, Vanessa; Gandía, Jose-Luis

    2017-01-01

    Background Displaced maxillary permanent canine is one of the more frequent findings in canine eruption process and it’s easy to be outlined and early diagnosed by means of x-ray images. Late diagnosis frequently needs surgery to rescue the impacted permanent canine. In many cases, interceptive treatment to redirect canine eruption is needed. However, some patients treated by interceptive means end up requiring fenestration to orthodontically guide the canine to its normal occlusal position. It would be interesting, therefore, to discover the dental characteristics of patients who will need additional surgical treatment to interceptive treatment. Material and Methods To study the dental characteristics associated with canine impaction, conventional statistics have traditionally been used. This approach, although serving to illustrate many features of this problem, has not provided a satisfactory response or not provided an overall idea of the characteristics of these types of patients, each one of them with their own particular set of variables. Faced with this situation, and in order to analyze the problem of impaction despite interceptive treatment, we have used an alternative method for representing the variables that have an influence on this syndrome. This method is known as Self-Organizing Maps (SOM), a method used for analyzing problems with multiple variables. Results We analyzed 78 patients with a PMC angulation higher than 100º. All of them were subject to interceptive treatment and in 21 cases it was necessary to undertake the above-mentioned fenestration to achieve the final eruption of the canine. Conclusions In this study, we describe the process of debugging variables and selecting the appropriate number of cells in SOM so as to adequately visualize the problem posed and the dental characteristics of patients with regard to a greater or lesser probability of the need for fenestration. Key words:Interceptive orthodontic treatment, altered

  14. Dynamic Self-Organizing Landmark Extraction Method Based on 2-Dimensional Growing Dynamic Self-Organizing Feature Map%基于二维GDSOM的路标动态自组织提取方法

    Institute of Scientific and Technical Information of China (English)

    王作为; 张汝波

    2012-01-01

    A dynamic self-organizing structural feature extraction method is presented based on distance sensor. The procedure consists of three parts; design of active exploration behavior, dimensionality reduction process of spatio-temporal information and self-organizing landmark extraction method. In this paper, active exploration behavior based on follow-wall is designed to obtain high correlative spatio-temporal sequence information. Activity neurons based on variety detection and activation intensity are used to reduce the dimensionality of spatio-temporal sequence. Finally, a method of 2-Dimensional growing dynamic self-organizing feature map (2-Dimensional GDSOM) is proposed to achieve self-organizing extraction and identification of environmental landmarks. The experimental results demonstrate the effectiveness of the method.%提出一种基于距离传感器的结构化特征的动态、自组织提取方法.该方法由3个部分组成:主动感知行为的设计,时空信息的降维处理及路标的自组织提取.设计基于沿墙走的“主动感知行为”来获得高相关性的感知时空序列信息;给出基于变化检测和激活强度的活性神经元来对时空序列信息降维;最后提出一种二维动态增长自组织特征图方法,实现环境路标的自组织提取和识别.实验结果验证该方法的有效性.

  15. Analysis of short single rest/activation epoch fMRI by self-organizing map neural network

    Science.gov (United States)

    Erberich, Stephan G.; Dietrich, Thomas; Kemeny, Stefan; Krings, Timo; Willmes, Klaus; Thron, Armin; Oberschelp, Walter

    2000-04-01

    Functional magnet resonance imaging (fMRI) has become a standard non invasive brain imaging technique delivering high spatial resolution. Brain activation is determined by magnetic susceptibility of the blood oxygen level (BOLD effect) during an activation task, e.g. motor, auditory and visual tasks. Usually box-car paradigms have 2 - 4 rest/activation epochs with at least an overall of 50 volumes per scan in the time domain. Statistical test based analysis methods need a large amount of repetitively acquired brain volumes to gain statistical power, like Student's t-test. The introduced technique based on a self-organizing neural network (SOM) makes use of the intrinsic features of the condition change between rest and activation epoch and demonstrated to differentiate between the conditions with less time points having only one rest and one activation epoch. The method reduces scan and analysis time and the probability of possible motion artifacts from the relaxation of the patients head. Functional magnet resonance imaging (fMRI) of patients for pre-surgical evaluation and volunteers were acquired with motor (hand clenching and finger tapping), sensory (ice application), auditory (phonological and semantic word recognition task) and visual paradigms (mental rotation). For imaging we used different BOLD contrast sensitive Gradient Echo Planar Imaging (GE-EPI) single-shot pulse sequences (TR 2000 and 4000, 64 X 64 and 128 X 128, 15 - 40 slices) on a Philips Gyroscan NT 1.5 Tesla MR imager. All paradigms were RARARA (R equals rest, A equals activation) with an epoch width of 11 time points each. We used the self-organizing neural network implementation described by T. Kohonen with a 4 X 2 2D neuron map. The presented time course vectors were clustered by similar features in the 2D neuron map. Three neural networks were trained and used for labeling with the time course vectors of one, two and all three on/off epochs. The results were also compared by using a

  16. 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.

  17. Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data

    Science.gov (United States)

    Nanda, Trushnamayee; Sahoo, Bhabagrahi; Chatterjee, Chandranath

    2017-06-01

    The Kohonen Self-Organizing Map (KSOM) estimator is prescribed as a useful tool for infilling the missing data in hydrometeorology. However, in this study, when the performance of the KSOM estimator is tested for gap-filling in the streamflow, rainfall, evapotranspiration (ET), and temperature timeseries data, collected from 30 gauging stations in India under missing data situations, it is felt that the KSOM modeling performance could be further improved. Consequently, this study tries to answer the research questions as to whether the length of record of the historical data and its variability has any effect on the performance of the KSOM? Whether inclusion of temporal distribution of timeseries data and the nature of outliers in the KSOM framework enhances its performance further? Subsequently, it is established that the KSOM framework should include the coefficient of variation of the datasets for determination of the number of map units, without considering it as a single value function of the sample data size. This could help to upscale and generalize the applicability of KSOM for varied hydrometeorological data types.

  18. Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

    CERN Document Server

    Chattopadhyay, Manojit; Dan, Pranab K; 10.1007/s00170-010-2802-4

    2011-01-01

    Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in t...

  19. Application of Self-organizing Maps to Observed and Simulated Daily Precipitation over the Tropical and Southern Pacific Ocean

    Science.gov (United States)

    Pike, M.; Ward, A. D.; Lintner, B. R.; Niznik, M. J.

    2014-12-01

    Self-organizing maps (SOMs) comprise a class of artificial neural networks that aim to organize complex input data through computation of a set of M x N representative maps. Here we use an SOM routine to isolate the spatial patterns inherent in daily austral summer (December-January-February or DJF) rainfall over the tropical and southern Pacific Ocean basins from Tropical Rainfall Measuring Mission (TRMM) satellite observations as well from an ensemble of models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5). Applying a 2x2 SOM to all available DJFs from TRMM yields two maps that may be regarded as Intertropical Convergence Zone (ITCZ)-active, in which precipitation is more intense over the ITCZ region compared to the South Pacific Convergence Zone (SPCZ) region, while the remaining maps are SPCZ-dominant. The latter reflect a spatial translation of the SPCZ consistent with the previously described impact of the El Niño/Southern Oscillation (ENSO) or analogous low-frequency modes of variability on the SPCZ. Comparing the CMIP5-based SOMs to TRMM reveals some broad similarities in the orientation and extent of large-scale features, as well as spurious features, which point to errors or biases in the models. Because of the pronounced impact of ENSO, we further consider SOMs computed separately for each of the El Niño and La Niña phases. This analysis suggests that while the overall position of the SPCZ is sensitive to the phase of ENSO, within each phase, similar high-frequency changes in SPCZ slope occur. Thus, while the mean position of the SPCZ may be dominantly controlled by ENSO phase, the distinct orientations within the same ENSO phase point to additional controls on SPCZ slope.

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

    DEFF Research Database (Denmark)

    Bothe, Hans-Heinrich

    . 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...... visual lip features is used. Phoneme-related receptive fields result on the SOM basis; they are speaker dependent and show individual locations and strain. Overlapping main slopes indicate a high similarity of respective units; distortion or extra peaks originate from the influence of other units...... 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...

  1. 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...

  2. 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...

  3. 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.

  4. Patterns of upper layer circulation variability in the South China Sea from satellite altimetry using the self-organizing map

    Institute of Scientific and Technical Information of China (English)

    LIU Yonggang; WEISBERG Robert H; YUAN Yaochu

    2008-01-01

    Patterns of the South China Sea (SCS) circulation variability are extracted from merged satellite altimetry data from October 1992 through August 2004 by using the self-organizing map (SOM). The annual cycle, seasonal and inter-annual variations of the SCS surface circulation are identified through the evolution of the characteristic circulation patterns. The annual cycle of the SCS gener- al circulation patterns is described as a change between two opposite basin-scale SW-NE oriented gyres embedded with eddies: low sea surface height anomaly (SSHA) (cyclonic) in winter and high SSHA (anticyclonic) in summer half year. The transition starts from July--August (January--February) with a high (low) SSHA tongue east of Vietnam around 12°~14° N, which de- velopa into a big anticyclonic (cyclonic) gyre while moving eastward to the deep basin. During the transitions, a dipole structure, cyclonic (anticyclonic) in the north and anticyclonic (cyclonic) in the south, may be formed southeast off Vietnam with a strong zonal jet around 10°~12° N. The seasonal variation is modulated by the interannual variations. Besides the strong 1997/1998 e- vent in response to the peak Pacific El Nino in 1997, the overall SCS sea level is found to have a significant rise during 1999~ 2001, however, in summer 2004 the overall SCS sea level is lower and the basin-wide anticyclonic gyre becomes weaker than the other years.

  5. Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

    Science.gov (United States)

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

  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.

  7. On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study.

    Science.gov (United States)

    Pacella, Massimo; Grieco, Antonio; Blaco, Marzia

    2016-01-01

    In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

  8. Representation of multi-target activity landscapes through target pair-based compound encoding in self-organizing maps.

    Science.gov (United States)

    Iyer, Preeti; Bajorath, Jürgen

    2011-11-01

    Activity landscape representations provide access to structure-activity relationships information in compound data sets. In general, activity landscape models integrate molecular similarity relationships with biological activity data. Typically, activity against a single target is monitored. However, for steadily increasing numbers of compounds, activity against multiple targets is reported, resulting in an opportunity, and often a need, to explore multi-target structure-activity relationships. It would be attractive to utilize activity landscape representations to aid in this process, but the design of activity landscapes for multiple targets is a complicated task. Only recently has a first multi-target landscape model been introduced, consisting of an annotated compound network focused on the systematic detection of activity cliffs. Herein, we report a conceptually different multi-target activity landscape design that is based on a 2D projection of chemical reference space using self-organizing maps and encodes compounds as arrays of pair-wise target activity relationships. In this context, we introduce the concept of discontinuity in multi-target activity space. The well-ordered activity landscape model highlights centers of discontinuity in activity space and is straightforward to interpret. It has been applied to analyze compound data sets with three, four, and five target annotations and identify multi-target structure-activity relationships determinants in analog series.

  9. Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps[S

    Science.gov (United States)

    Kumpula, Linda S.; Mäkelä, Sanna M.; Mäkinen, Ville-Petteri; Karjalainen, Anna; Liinamaa, Johanna M.; Kaski, Kimmo; Savolainen, Markku J.; Hannuksela, Minna L.; Ala-Korpela, Mika

    2010-01-01

    Plasma lipid concentrations cannot properly account for the complex interactions prevailing in lipoprotein (patho)physiology. Sequential ultracentrifugation (UCF) is the gold standard for physical lipoprotein isolations allowing for subsequent analyses of the molecular composition of the particles. Due to labor and cost issues, however, the UCF-based isolations are usually done only for VLDL, LDL, and HDL fractions; sometimes with the addition of intermediate density lipoprotein (IDL) particles and the fractionation of HDL into HDL2 and HDL3 (as done here; n = 302). We demonstrate via these data, with the lipoprotein lipid concentration and composition information combined, that the self-organizing map (SOM) analysis reveals a novel data-driven in silico phenotyping of lipoprotein metabolism beyond the experimentally available classifications. The SOM-based findings are biologically consistent with several well-known metabolic characteristics and also explain some apparent contradictions. The novelty is the inherent emergence of complex lipoprotein associations; e.g., the metabolic subgrouping of the associations between plasma LDL cholesterol concentrations and the structural subtypes of LDL particles. Importantly, lipoprotein concentrations cannot pinpoint lipoprotein phenotypes. It would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated here. Particularly, the compositional variations within the lipoprotein particles appear to be a fundamental issue with metabolic and clinical corollaries. PMID:19734566

  10. Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

    Science.gov (United States)

    Higuera, Clara; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2015-01-01

    Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets. PMID:26111164

  11. Identifying Local Scale Climate Zones of Urban Heat Island from HJ-1B Satellite Data Using Self-Organizing Maps

    Science.gov (United States)

    Wei, C. Z.; Blaschke, T.

    2016-10-01

    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.

  12. 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.

  13. On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study

    Science.gov (United States)

    Grieco, Antonio

    2016-01-01

    In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation. PMID:28044072

  14. Deriving Photometric Redshifts using Fuzzy Archetypes and Self-Organizing Maps. II. Comparing Sampling Techniques Using Mock Data

    CERN Document Server

    Speagle, Joshua S

    2015-01-01

    In a companion paper, we proposed combining large numbers of "fuzzy archetypes" with Self-Organizing Maps (SOMs) to derive photometric redshifts in a data-driven way. In this paper, we investigate the performance of several sampling approaches that build on this general idea using a mock catalog designed to approximately simulate LSST ($ugrizY$) and Euclid ($YJH$) data from $z=0-6$ at fixed LSST $Y=24$ mag. We test eight different approaches: two brute-force methods, two Markov Chain Monte Carlo (MCMC)-based methods, two hierarchical sampling methods, and two "quick-search" methods based on quantities derived during the initial SOM training process. We find most methods perform reasonably well with small catastrophic outlier fractions and are able to robustly identify redshift probability distribution functions that are multi-modal and/or poorly constrained. Once these insecure objects are removed, the results are generally in good agreement with the strict accuracy requirements necessary to meet Euclid weak ...

  15. Self-organizing maps classification of epidemiological data and toenail selenium content monitored on cancer and healthy patients from Poland.

    Science.gov (United States)

    Tsakovski, Stefan L; Zukowska, Joanna; Bode, Peter; Bizuk, Marek K; Kowalczyk, Anna

    2010-01-01

    This paper deals with epidemiological multivariate statistical analysis of cancer and health patients from Pomeranian and Lubuskie Voivodships, Poland. The anthropometric and epidemiologic data include 8 parameters: toenail selenium concentration, sex, age, body mass index (BMI), smoking status, taking of Se supplements, health state, and family history of cancer. The self-organizing maps (SOM) are used for simultaneous classification of parameters and patients with relation to cancer diagnosis. Three different patterns (groups) of patients with cancer diagnosis are outlined: (i) older, smoking men with low toenail selenium concentration; (ii) older smoking women with family relation to cancer and toenail selenium deficiency; (iii) middle, aged nonsmokers with high level of selenium toenail concentration. The simultaneous classification of parameters and patients makes it possible to determine discriminating parameters for each pattern and relations between parameters. The relation of each parameter to cancer disease is discussed as special attention is paid to toenail selenium deficiency. More than 80% of patients with cancer diagnosis possess toenail selenium deficiency, accompanied by old age and smoking.

  16. Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature

    Science.gov (United States)

    Rivera, Diego; Lillo, Mario; Uvo, Cintia B.; Billib, Max; Arumí, José Luis

    2012-01-01

    Western South America is subject to considerable inter-annual variability due to El Niño-Southern Oscillation (ENSO) so forecasting inter-annual variations associated with ENSO would provide an opportunity to tailor management decisions more appropriately to the season. On one hand, the self-organizing maps (SOM) method is a suitable technique to explore the association between sea surface temperature and precipitation fields. On the other hand, Wavelet transform is a filtering technique, which allows the identification of relevant frequencies in signals, and also allows localization on time. Taking advantage of both methods, we present a method to forecast monthly precipitation using the SOM trained with filtered SST anomalies. The use of the SOM to forecast precipitation for Chillan showed good agreement between forecasted and measured values, with correlation coefficients ( r 2) ranging from 0.72 to 0.91, making the combined use filtered SST fields and SOM a suitable tool to assist water management, for example in agricultural water management. The method can be easily tailored to be applied in other stations or to other variables.

  17. Transit shapes and self-organizing maps as a tool for ranking planetary candidates: application to Kepler and K2

    Science.gov (United States)

    Armstrong, D. J.; Pollacco, D.; Santerne, A.

    2017-03-01

    A crucial step in planet hunting surveys is to select the best candidates for follow-up observations, given limited telescope resources. This is often performed by human 'eyeballing', a time consuming and statistically awkward process. Here, we present a new, fast machine learning technique to separate true planet signals from astrophysical false positives. We use self-organizing maps (SOMs) to study the transit shapes of Kepler and K2 known and candidate planets. We find that SOMs are capable of distinguishing known planets from known false positives with a success rate of 87.0 per cent, using the transit shape alone. Furthermore, they do not require any candidate to be dispositioned prior to use, meaning that they can be used early in a mission's lifetime. A method for classifying candidates using a SOM is developed, and applied to previously unclassified members of the Kepler Objects of Interest (KOI) list as well as candidates from the K2 mission. The method is extremely fast, taking minutes to run the entire KOI list on a typical laptop. We make PYTHON code for performing classifications publicly available, using either new SOMs or those created in this work. The SOM technique represents a novel method for ranking planetary candidate lists, and can be used both alone or as part of a larger autovetting code.

  18. 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.

  19. 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

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

    Science.gov (United States)

    Bernard, Jessica A; Seidler, Rachael D; Hassevoort, Kelsey M; Benson, Bryan L; Welsh, Robert C; Wiggins, Jillian Lee; Jaeggi, Susanne M; Buschkuehl, Martin; Monk, Christopher S; Jonides, John; Peltier, Scott J

    2012-01-01

    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; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular 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 connectivity atlas of the cerebellar lobules. 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 (SOM) 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 SOM 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 necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

  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. 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.

  3. Analysis of liver damage from radon, X-ray, or alcohol treatments in mice using a self-organizing map.

    Science.gov (United States)

    Kanzaki, Norie; Kataoka, Takahiro; Etani, Reo; Sasaoka, Kaori; Kanagawa, Akihiro; Yamaoka, Kiyonori

    2017-01-01

    In our previous studies, we found that low-dose radiation inhibits oxidative stress-induced diseases due to increased antioxidants. Although these effects of low-dose radiation were demonstrated, further research was needed to clarify the effects. However, the analysis of oxidative stress is challenging, especially that of low levels of oxidative stress, because antioxidative substances are intricately involved. Thus, we proposed an approach for analysing oxidative liver damage via use of a self-organizing map (SOM)-a novel and comprehensive technique for evaluating hepatic and antioxidative function. Mice were treated with radon inhalation, irradiated with X-rays, or subjected to intraperitoneal injection of alcohol. We evaluated the oxidative damage levels in the liver from the SOM results for hepatic function and antioxidative substances. The results showed that the effects of low-dose irradiation (radon inhalation at a concentration of up to 2000 Bq/m(3), or X-irradiation at a dose of up to 2.0 Gy) were comparable with the effect of alcohol administration at 0.5 g/kg bodyweight. Analysis using the SOM to discriminate small changes was made possible by its ability to 'learn' to adapt to unexpected changes. Moreover, when using a spherical SOM, the method comprehensively examined liver damage by radon, X-ray, and alcohol. We found that the types of liver damage caused by radon, X-rays, and alcohol have different characteristics. Therefore, our approaches would be useful as a method for evaluating oxidative liver damage caused by radon, X-rays and alcohol. © The Author 2016. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

  4. Self-organizing maps in geothermal exploration-A new approach for understanding geochemical processes and fluid evolution

    Science.gov (United States)

    Brehme, Maren; Bauer, Klaus; Nukman, Mochamad; Regenspurg, Simona

    2017-04-01

    Understanding geochemical processes is an important part of geothermal exploration to get information about the source and evolution of geothermal fluids. However, in most cases knowledge of fluid properties is based on few parameters determined in samples from the shallow subsurface. This study presents a new approach that allows to conclude from the combination of a variety of these data on processes occurring at depth in a geothermal reservoir. The neural network clustering technique called ;self-organizing maps; (SOMs) successfully distinguished two different geothermal settings based on a hydrochemical database and disclosed the source, evolution and flow pathways of geothermal fluids. Scatter plots, as shown in this study, are appropriate presentations of element concentrations and the chemical interaction of water and rock at depth. One geological setting presented here is marked by fault dominated fluid pathways and minor influence of volcanic affected fluids with high concentrations of HCO3, Ca and Sr. The second is a magmatically dominated setting showing strong alteration features in volcanic rocks and accommodates acidic fluids with high SO4 and Si concentrations. Former studies, i.e., Giggenbach (1988), suggested Cl, HCO3 and SO4 to be generally the most important elements for understanding hydrochemical processes in geothermal reservoirs. Their relation has been widely used to classify different water types in geothermal fields. However, this study showed that non-standard elements are at least of same importance to reveal different fluid types in geothermal systems. Therefore, this study is an extended water classification approach using SOM for element correlations. SOM have been proven to be a successful method for analyzing even relatively small hydrochemical datasets in geothermal applications.

  5. 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.

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

    Directory of Open Access Journals (Sweden)

    Rocio Chavez-Alvarez

    Full Text Available 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.

  7. Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

    Science.gov (United States)

    Unglert, K.; Radić, V.; Jellinek, A. M.

    2016-06-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

  8. 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.

  9. 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

  10. An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

    Directory of Open Access Journals (Sweden)

    Jonathan T. Barge

    2016-06-01

    Full Text Available This study focused on employing Linear Genetic Programming (LGP, Ensemble Empirical Mode Decomposition (EEMD, and the Self-Organizing Map (SOM in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN results over the same dataset, posting an average absolute relative error (AARE of 17.118 and Nash-Sutcliff (E of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.

  11. Patterning of impoundment impact on chironomid assemblages and their environment with use of the self-organizing map (SOM)

    Science.gov (United States)

    Penczak, Tadeusz; Kruk, Andrzej; Grzybkowska, Maria; Dukowska, Małgorzata

    2006-11-01

    The paper assesses the impact of the Jeziorsko dam reservoir on chironomid assemblages and selected environmental factors in the Warta River, Poland, by means of patterns recognized with the self-organizing map (SOM, Kohonen unsupervised artificial neural network). Over 1988-1996, in four annual cycles, a total of 233 monthly samples were collected in a seven order section of the river at two sites: WAA (backwater) located about 2 km upstream from the Jeziorsko Reservoir, and WAB (tailwater) located about 1.5 km downstream from the reservoir's dam. At each site three habitats were selected: H 1, H 2 and H 3 at WAA, and H 11, H 12 and H 13 at WAB. H 1 and H 11 were located in the depositional area close to the banks, H 2 and H 12 about 6-7 m towards the mid-river and H 3 and H 13 in the mid-river. SOM effectively vertically separated H 1 and H 11 (bank habitats) from H 3 and H 13 (the mid-river zone of both sites) and H 2 (the transition zone of the upstream site). The H 12 samples were scattered all over SOM but still exhibited a slight temporal gradient. At the end of the study the water discharge, especially in summers, stabilized at WAB at a level lower than natural and as a result submerged macrophytes appeared at H 12 making the abundance of macroinvertebrates increase very quickly. Moreover, a weaker horizontal grouping of samples by season and by site of collection (upstream or downstream from the reservoir) was observed over SOM: 1) bank upstream habitat H 1, with hydrological regime resembling natural, was separated from the downstream H 11, which enlarged and contracted in response to dam operation, 2) deeper habitats were less dependent on water level and this is why they underwent seasonal fluctuations. To sum up, the deepest habitats were most resistant to water level fluctuations, while the formerly most productive habitat at the tailwater WAB site, H 11, became the most negatively impacted. Nevertheless, the reservoir has not negatively influenced

  12. 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

  13. USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT

    NARCIS (Netherlands)

    SCHOMAKER, L

    1993-01-01

    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

  14. 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

  15. 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)

  16. Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

    Science.gov (United States)

    Fahimi, F.; El-Shafie, A. H.

    2014-07-01

    Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

  17. Function Clustering Self-Organization Maps (FCSOMs) for mining differentially expressed genes in Drosophila and its correlation with the growth medium.

    Science.gov (United States)

    Liu, L L; Liu, M J; Ma, M

    2015-09-28

    The central task of this study was to mine the gene-to-medium relationship. Adequate knowledge of this relationship could potentially improve the accuracy of differentially expressed gene mining. One of the approaches to differentially expressed gene mining uses conventional clustering algorithms to identify the gene-to-medium relationship. Compared to conventional clustering algorithms, self-organization maps (SOMs) identify the nonlinear aspects of the gene-to-medium relationships by mapping the input space into another higher dimensional feature space. However, SOMs are not suitable for huge datasets consisting of millions of samples. Therefore, a new computational model, the Function Clustering Self-Organization Maps (FCSOMs), was developed. FCSOMs take advantage of the theory of granular computing as well as advanced statistical learning methodologies, and are built specifically for each information granule (a function cluster of genes), which are intelligently partitioned by the clustering algorithm provided by the DAVID_6.7 software platform. However, only the gene functions, and not their expression values, are considered in the fuzzy clustering algorithm of DAVID. Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be easily parallelized.

  18. Sistem Untuk Mengklasifikasikan Bentuk Sel Darah Merah Normal Dan Abnormal Dengan Metode Self-Organizing Map (SOM)

    OpenAIRE

    Wulandari, Fanny Sari

    2014-01-01

    Blood is an essential component in the vascular space of living creature. The identification of a disease can be tested through a blood test. By seeing shape of the red blood cell is one of the methods to identify a disease. Normal and abnormal morphology of red blood cell of a patient really help doctors to diagnose a disease. Advances in technology of digital image processing give many advantages to identification normal and abnormal red blood cell of a patient. This research use Self-organ...

  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. 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

  1. Sensitivity of Self-Organizing Map surface current patterns to the use of radial vs. Cartesian input vectors measured by high-frequency radars

    Science.gov (United States)

    Kalinić, Hrvoje; Mihanović, Hrvoje; Cosoli, Simone; Vilibić, Ivica

    2015-11-01

    In this paper, the Self-Organizing Map (SOM) method was applied to the surface currents data obtained between February and November 2008 by a network of high-frequency (HF) radars in the northern Adriatic. The sensitivity of the derived SOM solutions was tested in respect to the change of coordinate system of the data introduced to the SOM. In one experiment the original radial data measurements were used, and in the other experiment the Cartesian (total) current vectors derived from original radar data were analyzed. Although the computation of SOM solutions was not a demanding task, comparing both neural lattices yielded the nondeterministic polynomial time (NP) problem for which is difficult to propose a solution that will be globally optimal. Thus, we suggested utilizing the greedy algorithm with underlying assumption of 1-to-1 mapping between lattices. The results suggested that such solution could be local, but not global optimum and that the latter assumption could lower the obtained correlations between the patterns. However, without the assumption of 1-to-1 mapping between lattices, correlation between the derived SOM patterns was quite high, indicating that SOM mapping introduced to the radial current vectors and subsequent transformation into Cartesian coordinate system does not significantly affect obtained patterns in comparison to the SOM mapping done on the derived Cartesian current vectors. The documented similarity corroborates the use of total current vectors in various oceanographic studies, as being representative derivative of original radial measurements.

  2. Concept mapping: A supervision strategy for introducing case conceptualization skills to novice therapists.

    Science.gov (United States)

    Liese, Bruce S; Esterline, Kate M

    2015-06-01

    Case conceptualization, a term synonymous with case formulation, is an essential psychotherapy skill. Novice therapists enter into the practice of psychotherapy with limited case conceptualization skills. Hence, an important goal when supervising novice therapists is to effectively teach these skills. Concept mapping facilitates case conceptualization skills through the process of methodically creating graphic representations of clients' problems and dynamic relationships between these problems. This article introduces a highly structured and practical 4-stage approach to supervision that effectively introduces case formulation skills to novice therapists using concept mapping. It is assumed that concept maps, when shared with clients, function as an intervention to facilitate insight and change.

  3. Using Self-Organizing Neural Network Map Combined with Ward’s Clustering Algorithm for Visualization of Students’ Cognitive Structural Models about Aliveness Concept

    Directory of Open Access Journals (Sweden)

    Nurettin Yorek

    2016-01-01

    Full Text Available We propose an approach to clustering and visualization of students’ cognitive structural models. We use the self-organizing map (SOM combined with Ward’s clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept.

  4. 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.

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

    Science.gov (United States)

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

    2014-06-17

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

  6. 对基因表达数据进行聚类的一种新型自组织映射模型%Clustering gene expression data using a novel model of self-organizing map

    Institute of Scientific and Technical Information of China (English)

    郝伟; 郁松年; 席福利

    2007-01-01

    Clustering is an important technique for analyzing gene expression data. The self-organizing map is one of the most useful clustering algorithms. However, its applicability is limited by the fact that some knowledge about the data is required prior to clustering. This paper introduces a novel model of self-organizing map (SOM) called growing hierarchical self-organizing map (GHSOM) to cluster gene expression data. The training and growth processes of GHSOM are entirely data driven, requiring no prior knowledge or estimates for parameter specification, thus help find not only the appropriate number of clusters but also the hierarchical relations in the data set. Compared with other clustering algorithms, GHSOM has better accuracy. To validate the results, a novel validation technique is used, known as figure of merit (FOM).

  7. Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works

    Science.gov (United States)

    Bieroza, Magdalena; Baker, Andy; Bridgeman, John

    2009-12-01

    In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers.

  8. A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).

    Science.gov (United States)

    Iwasaki, Yuki; Abe, Takashi; Wada, Kennosuke; Wada, Yoshiko; Ikemura, Toshimichi

    2013-11-20

    With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.g., 1 kb) according to phylotypes, solely depending on oligonucleotide composition. Metagenomics studies of uncultivable microorganisms in clinical and environmental samples should allow extensive surveys of genes important in life sciences. BLSOM is most suitable for phylogenetic assignment of metagenomic sequences, because fragmental sequences can be clustered according to phylotypes, solely depending on oligonucleotide composition. We first constructed oligonucleotide BLSOMs for all available sequences from genomes of known species, and by mapping metagenomic sequences on these large-scale BLSOMs, we can predict phylotypes of individual metagenomic sequences, revealing a microbial community structure of uncultured microorganisms, including viruses. BLSOM has shown that influenza viruses isolated from humans and birds clearly differ in oligonucleotide composition. Based on this host-dependent oligonucleotide composition, we have proposed strategies for predicting directional changes of virus sequences and for surveilling potentially hazardous strains when introduced into humans from non-human sources.

  9. A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM

    Directory of Open Access Journals (Sweden)

    Yuki Iwasaki

    2013-11-01

    Full Text Available With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM, which allowed classification of sequence fragments (e.g., 1 kb according to phylotypes, solely depending on oligonucleotide composition. Metagenomics studies of uncultivable microorganisms in clinical and environmental samples should allow extensive surveys of genes important in life sciences. BLSOM is most suitable for phylogenetic assignment of metagenomic sequences, because fragmental sequences can be clustered according to phylotypes, solely depending on oligonucleotide composition. We first constructed oligonucleotide BLSOMs for all available sequences from genomes of known species, and by mapping metagenomic sequences on these large-scale BLSOMs, we can predict phylotypes of individual metagenomic sequences, revealing a microbial community structure of uncultured microorganisms, including viruses. BLSOM has shown that influenza viruses isolated from humans and birds clearly differ in oligonucleotide composition. Based on this host-dependent oligonucleotide composition, we have proposed strategies for predicting directional changes of virus sequences and for surveilling potentially hazardous strains when introduced into humans from non-human sources.

  10. Typha latifolia (broadleaf cattail) as bioindicator of different types of pollution in aquatic ecosystems-application of self-organizing feature map (neural network).

    Science.gov (United States)

    Klink, Agnieszka; Polechońska, Ludmiła; Cegłowska, Aurelia; Stankiewicz, Andrzej

    2016-07-01

    The contents of Cd, Cu, Fe, Mn, Ni, Pb, and Zn in leaves of Typha latifolia (broadleaf cattail), water and bottom sediment from 72 study sites designated in different regions of Poland were determined using atomic absorption spectrometry. The aim of the study was to evaluate potential use of T. latifolia in biomonitoring of trace metal pollution. The self-organizing feature map (SOFM) identifying groups of sampling sites with similar concentrations of metals in cattail leaves was able to classify study sites according to similar use and potential sources of pollution. Maps prepared for water and bottom sediment showed corresponding groups of sampling sites which suggested similarity of samples features. High concentrations of Fe, Cd, Cu, and Ni were characteristic for industrial areas. Elevated Pb concentrations were noted in regions with intensive vehicle traffic, while high Mn and Zn contents were reported in leaves from the agricultural area. Manganese content in leaves of T. latifolia was high irrespectively of the concentrations in bottom sediments and water so cattail can be considered the leaf accumulator of Mn. Once trained, SOFMs can be applied in ecological investigations and could form a future basis for recognizing the type of pollution in aquatic environments by analyzing the concentrations of elements in T. latifolia.

  11. 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.

  12. 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.

  13. Cluster Analysis of Comparative Genomic Hybridization (CGH Data Using Self-Organizing Maps: Application to Prostate Carcinomas

    Directory of Open Access Journals (Sweden)

    Torsten Mattfeldt

    2001-01-01

    Full Text Available Comparative genomic hybridization (CGH is a modern genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20–30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self‐organizing map (Genecluster as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data. We studied a group of 40 recent cases without follow‐up, an older group of 20 cases with follow‐up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.

  14. 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...

  15. Coupling Self-Organizing Maps with a Naïve Bayesian classifier: A case study for classifying Vermont streams using geomorphic, habitat and biological assessment data

    Science.gov (United States)

    Fytilis, N.; Rizzo, D. M.

    2012-12-01

    Environmental managers are increasingly required to forecast the long-term effects and the resilience or vulnerability of biophysical systems to human-generated stresses. Mitigation strategies for hydrological and environmental systems need to be assessed in the presence of uncertainty. An important aspect of such complex systems is the assessment of variable uncertainty on the model response outputs. We develop a new classification tool that couples a Naïve Bayesian Classifier with a modified Kohonen Self-Organizing Map to tackle this challenge. For proof-of-concept, we use rapid geomorphic and reach-scale habitat assessments data from over 2500 Vermont stream reaches (~1371 stream miles) assessed by the Vermont Agency of Natural Resources (VTANR). In addition, the Vermont Department of Environmental Conservation (VTDEC) estimates stream habitat biodiversity indices (macro-invertebrates and fish) and a variety of water quality data. Our approach fully utilizes the existing VTANR and VTDEC data sets to improve classification of stream-reach habitat and biological integrity. The combined SOM-Naïve Bayesian architecture is sufficiently flexible to allow for continual updates and increased accuracy associated with acquiring new data. The Kohonen Self-Organizing Map (SOM) is an unsupervised artificial neural network that autonomously analyzes properties inherent in a given a set of data. It is typically used to cluster data vectors into similar categories when a priori classes do not exist. The ability of the SOM to convert nonlinear, high dimensional data to some user-defined lower dimension and mine large amounts of data types (i.e., discrete or continuous, biological or geomorphic data) makes it ideal for characterizing the sensitivity of river networks in a variety of contexts. The procedure is data-driven, and therefore does not require the development of site-specific, process-based classification stream models, or sets of if-then-else rules associated with

  16. Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields

    Science.gov (United States)

    Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

    2009-09-01

    This study presents an unsupervised feature selection and learning approach for the discovery and intuitive imaging of significant temporal patterns in seismic single-station or network recordings. For this purpose, the data are parametrized by real-valued feature vectors for short time windows using standard analysis tools for seismic data, such as frequency-wavenumber, polarization, and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is in particular suitable for high-dimensional data sets. Our feature selection method is based on significance testing using the Wald-Wolfowitz runs test for individual features and on correlation hunting with SOMs in feature subsets. Using synthetics composed of Rayleigh and Love waves and real-world data, we show the robustness and the improved discriminative power of that approach compared to feature subsets manually selected from individual wavefield parametrization methods. Furthermore, the capability of the clustering and visualization techniques to investigate the discrimination of wave phases is shown by means of synthetic waveforms and regional earthquake recordings.

  17. Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban storm in Beijing

    Institute of Scientific and Technical Information of China (English)

    LAI Wen-li; WANG Hong-rui; WANG Cheng; ZHANG Jie; ZHAO Yong

    2017-01-01

    Due to rapid urbanization,waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety.Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing,the capital of China.Based on a self-organizing map (SOM) artificial neural network (ANN),a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing,China.Social risk factors,such as Gross domestic product (GDP),population density,and traffic congestion,were utilized as input datasets in this study.The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated with waterlogging.The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed.As a result,SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate.In this paper,the risk level of waterlogging in Beijing was divided into five grades.The points that were assigned risk grades of Ⅳ or Ⅴ were located mainly in the districts of Chaoyang,Haidian,Xicheng,and Dongcheng.

  18. 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)

  19. 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

    2013-12-01

    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.

  20. Self-organizing maps: A tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence

    Indian Academy of Sciences (India)

    D V Raje; H J Purohit; Y P Badhe; S S Tambe; B D Kulkarni

    2010-12-01

    Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

  1. 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.

  2. 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.

  3. Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence.

    Science.gov (United States)

    Raje, D V; Purohit, H J; Badhe, Y P; Tambe, S S; Kulkarni, B D

    2010-12-01

    Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

  4. Variability of Changjiang Diluted Water revealed by a 45-year long-term ocean hindcast and Self-Organizing Maps analysis

    Science.gov (United States)

    Zeng, Xiangming; He, Ruoying; Zong, Haibo

    2017-08-01

    Based on long-term realistic ocean circulation hindcast for in the Bohai, Yellow, and East China Seas, 45 years (1961-2005) of sea surface salinity data were analyzed using Self-Organizing Maps (SOM) to have a better understanding of the Changjiang Diluted Water (CDW) variation. Three spatial patterns were revealed by the SOM: normal, transition, and extension. The normal pattern mainly occurs from December to May while the CDW hugs China's east coast closely and flows southward. The extension pattern is dominant from June to October when the CDW extends northwestward toward Jeju Island in an omega shape. The transition pattern prevails for the rest of the year. Pattern-averaged temperature, circulation, and chlorophyll-a concentration show significant differences. CDW area and its eastern most extension were explored as a function of the Changjiang runoff and regional upwelling index. We found that Changjiang runoff and upwelling index can be reasonable predictors for the overall CDW area, while ambient circulation determines the distribution and structure of the CDW, and thus the CDW eastern most extension.

  5. Clavulanic acid production estimation based on color and structural features of Streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm.

    Science.gov (United States)

    Nurmohamadi, Maryam; Pourghassem, Hossein

    2014-05-01

    The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively.

  6. A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

    Science.gov (United States)

    Bahrami, Sheyda; Shamsi, Mousa

    2017-01-01

    Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

  7. SOM-based Pattern Generator: Pattern Generation Based on the Backward Projection in a Self-Organizing Map and Its Applications

    Science.gov (United States)

    Wakuya, Hiroshi; Ishiguma, Takahiro

    A major feature of the self-organizing map (SOM) is a topology-preserving projection from the input layer to the competitive layer, and it has been used mainly as an analytical tool for discovering underlying rules in the given data set. Even though recent splendid progress in this area, there are few novel ideas to break such a conventional style. On the contrary, based on its distinctive nature, a new method for generating patterns through backward projection from the competitive layer to the input layer is proposed recently. Moreover, a promising technology for producing animation as a series of backward-projected patterns along with any pathways on the competitive layer is presented. Then, in order to carry out further considerations, some computer simulations with a variety of posed stick figures are tried in this paper. After training, four kinds of pathways, which correspond to different movements such as dancing, exercising and walking, are prepared. Though some of them does not contain any training samples, all of them worked well as we have intended in advance. As a result, it is found that the proposed method shows good performance and it is also confirmed its effectiveness.

  8. 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.

  9. Understanding dynamic of biogeochemical properties in the northern Adriatic Sea by using self-organizing maps and k-means clustering

    Science.gov (United States)

    Solidoro, Cosimo; Bandelj, Vinko; Barbieri, Pierluigi; Cossarini, Gianpiero; Fonda Umani, Serena

    2007-07-01

    The dynamic of biogeochemical properties in a coastal area of the northern Adriatic Sea (Gulf of Trieste) is analyzed through (1) identification of a small number of water typology classes and classification of samples, obtained by means of a novel multivariate classification procedure based on a combination of Artificial Neural Networks (ANN) and "traditional" clusterization algorithms, (2) interpretation of each class based on biogeochemical properties and ecological phenomena likely to occur in the water body, and (3) discussion of time evolution and spatial distribution of water classes which summarized and provided indications on the system's space and time evolution. Basing itself on a multivariate comparison, the Self-Organizing Map (SOM) grouped 1292 samples collected in a 3-year-long monitoring program in 187 sets and identified a representative synthetic sample for each group. These groups were further classified in seven clusters, which identified the water typology. The complexity of the space and time coevolution of 12 variables was so reduced to variation of one categorical variable. Results included an objectively derived typology of water masses and their typical temporal succession, a spatial dividing based on biogeochemical processes, a conceptual scheme of biogeochemistry in the Gulf. Results clearly indicated the importance of river input in triggering plankton blooms and pointed out that trophodynamics followed current paradigms of marine ecosystem functioning, with shifts from conditions dominated by classical food chain to situations in which most of the energy flowed through the autotrophic and heterotrophic parts of the microbial food web.

  10. Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain

    Directory of Open Access Journals (Sweden)

    Ana Pérez-Hoyos

    2014-11-01

    Full Text Available Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a major identification of Ecosystem Functional Types (EFTs in Spain to characterize the patterns of ecosystem functional diversity and status, from several functional attributes as the Normalized Difference Vegetation Index (NDVI, Land Surface Temperature (LST and Albedo. For this purpose, several metrics, related to the spatial variability in seasonal and annual patterns (e.g., relative range, have been derived from remote sensing time series of 1 km MODIS over the period 2000–2009. Moreover, precipitation maps from data provided by the AEMet (Agencia Estatal de Meteorología and the corresponding aridity and humidity indices were also included in the analysis. To create the EFTs, the potential of the joint use of Kohonen’s Self-Organizing Map (SOM and the k-means clustering algorithm was tested. The EFTs were analyzed using different remote sensing (i.e., Gross Primary Production and climatic variables. The relationship of the EFTs with existing land cover datasets and climatic data were analyzed through a correspondence analysis (CA. The trained SOM have shown feasible in providing a comprehensive view on the functional attributes patterns and a remarkable potential for the quantification of ecosystem function. The results highlight the potential of this technique to delineate ecosystem functional types as well as to monitor the spatial pattern of the ecosystem status as a reference for changes due to human or climate impacts.

  11. 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.

  12. Recursive self-organizing network models.

    Science.gov (United States)

    Hammer, Barbara; Micheli, Alessio; Sperduti, Alessandro; Strickert, Marc

    2004-01-01

    Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.

  13. 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.

  14. Changes in the fluorescence composition of multiple DOM sources over pH gradients assessed by combining parallel factor analysis and self-organizing maps

    Science.gov (United States)

    Cuss, C. W.; Shi, Y. X.; McConnell, S. M.; Guéguen, C.

    2014-09-01

    Dissolved organic matter is a ubiquitous constituent of natural waters that plays key roles in several important processes. The fluorescence properties of DOM have been linked to its functionality, but these properties may vary with pH. In this study Kohonen's self-organizing maps (SOMs) were applied to excitation-emission matrices (EEMs) of fresh dissolved organic matter (DOM) from three sources: senescent sugar-maple leaves and white spruce needles, and humified white spruce needles, over a pH range of ~4.5 - 12.5. SOMs were applied to raw EEMs, EEMs reduced in dimensionality by pre-processing using parallel factor analysis (PARAFAC), and PARAFAC loading proportions normalized to values at initial pH. Some separation of EEMs into source-based clusters was achieved in the SOM of raw EEMs, but commingling was apparent and evidence of changes over pH gradients was overshadowed. SOMs of PARAFAC component proportions demonstrated clear source-based clustering, and pH-based gradients were visible for DOM from senescent and humified spruce needles. Changes in optical properties were obvious over pH gradients in the SOM of components normalized to starting condition. Component proportions decreased to values as low as 5% of the initial values for microbial humic-like peak M and increased to as high as 278% for a humic-like component. Tyrosine-like fluorescence increased to 112% of initial over increasing pH in humified spruce leachates but decreased to as low as 45% in the other leachates. The combination of PARAFAC and SOM drastically enhanced visualization and interpretability of pH-induced changes in DOM compared to either method alone.

  15. 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

  16. Patterns of the loop current system and regions of sea surface height variability in the eastern Gulf of Mexico revealed by the self-organizing maps

    Science.gov (United States)

    Liu, Yonggang; Weisberg, Robert H.; Vignudelli, Stefano; Mitchum, Gary T.

    2016-04-01

    The Self-Organizing Map (SOM), an unsupervised learning neural network, is employed to extract patterns evinced by the Loop Current (LC) system and to identify regions of sea surface height (SSH) variability in the eastern Gulf of Mexico (GoM) from 23 years (1993-2015) of altimetry data. Spatial patterns are characterized as different LC extensions and different stages in the process of LC eddy shedding. The temporal evolutions and the frequency of occurrences of these patterns are obtained, and the typical trajectories of the LC system progression on the SOM grid are investigated. For an elongated, northwest-extended, or west-positioned LC, it is common for the LC anticyclonic eddy (LCE) to separate and propagate into the western GoM, while an initially separated LCE in close proximity to the west Florida continental slope often reattaches to the LC and develops into an elongated LC, or reduces intensity locally before moving westward as a smaller eddy. Regions of differing SSH variations are also identified using the joint SOM-wavelet analysis. Along the general axis of the LC, SSH exhibits strong variability on time scales of 3 months to 2 years, also with energetic intraseasonal variations, which is consistent with the joint Empirical Orthogonal Function (EOF)-wavelet analysis. In the more peripheral regions, the SSH has a dominant seasonal variation that also projects across the coastal ocean. The SOM, when applied to both space and time domains of the same data, provides a powerful tool for diagnosing ocean processes from such different perspectives.

  17. Self-Organized Bistability

    CERN Document Server

    di Santo, Serena; Vezzani, Alessandro; Muñoz, Miguel A

    2016-01-01

    Self-organized criticality elucidates the conditions under which physical and biological systems tune themselves to the edge of a second-order phase transition, with scale invariance. Motivated by the empirical observation of bimodal distributions of activity in neuroscience and other fields, we propose and analyze a theory for the self-organization to the point of phase-coexistence in systems exhibiting a first-order phase transition. It explains the emergence of regular avalanches with attributes of scale-invariance which coexist with huge anomalous ones, with realizations in many fields.

  18. 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...

  19. Application of hybrid techniques (self-organizing map and fuzzy algorithm) using backscatter data for segmentation and fine-scale roughness characterization of seepage-related seafloor along the western continental margin of India

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Menezes, A.A.A.; Dandapath, S.; Fernandes, W.A.; Karisiddaiah, S.M.; Haris, K.; Gokul, G.S.

    (involving pockmarks and faulted structures) subjected to strong bottom currents and seasonal upwelling. Index Terms ─ Multi-beam backscatter, Seafloor classification and characterizations, Self-Organizing map (SOM), Fuzzy C- means (FCM), Power spectral..., ANN techniques were proposed for hydro-acoustic data classification [10]. The SOM exercises unsupervised competitive learning on the unknown dataset (input) onto coarser clusters i.e., primary classifications [11]. For real time survey applications...

  20. 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.

  1. Bioindication of trace metals in Brachythecium rutabulum around a copper smelter in Legnica (Southwest Poland): Use of a new form of data presentation in the form of a self-organizing feature map.

    Science.gov (United States)

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

    2009-05-01

    Concentrations of the elements Al, Be, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn were measured in the terrestrial moss Brachythecium rutabulum and the soil on which it grew. Soil and moss plants were sampled at sites situated 1.5, 3, 6, 9 and 15 km to the north, south, east and west of the Legnica copper smelter (SW Poland). The self-organizing feature map (SOFM) or Kohonen network was used to classify the soil and moss samples according to the concentrations of the elements. The self-organizing map yielded distinct groups of B. rutabulum and soil samples, depending on the distance from and direction to the source of pollution. When the map-identified groups of sites with similar soil metal concentrations were combined with the map-identified groups of sites with similar metal concentrations in B. rutabulum, these maps were found to correspond closely. The SOFMs accurately represented the least polluted, moderately polluted and severely polluted sites, reflecting the distribution of metals that is typical of the smelter area, caused by the prevailing westerly and northerly winds.

  2. 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

  3. Solutions to Traveling Salesman Problem (TSP) Based on Self-Organizing Maps (SOM)%基于自组织网络的货郎担问题解决方案

    Institute of Scientific and Technical Information of China (English)

    田胜

    2005-01-01

    货郎担问题(Traveling Salesman Problem,TSP)作为组合数学中的经典问题,具有一定的研究价值.首先陈述了基于自组织网络(Self-Organizing Maps,SOM)的TSP问题的解决方案,然后着重分析为什么SOM网络能够体现这样的计算智能,并探讨了如何将其应用到其它的优化问题当中.

  4. 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.

  5. Mapping based on the Growing Self-organizing Map (GSOM)%基于结构可增长自组织特征映射图的地图绘制

    Institute of Scientific and Technical Information of China (English)

    阮晓钢; 徐绍敏; 李欣源

    2008-01-01

    针对机器人环境识别问题,研究其工作环境描述与实现过程,提出一种环境拓扑地图建立的新方法.该方法以自组织特征映射图的工作算法为基础,提出GSOM(Growing Self-organizing Map)算法,该算法具有增长特性,通过不断增加新的神经元实现网络规模的增长,从而满足描述环境特征的需要,建立环境拓扑地图;仿真试验表明GSOM算法的正确性,可以在样本数未知情况下,确定描述环境特征的最优SOM神经元数量,以少数SOM图神经元分布描述具有大量特征信息的环境结构,建立更能准确描述环境的拓扑地图.

  6. Facial Expression Recognition by Supervised Independent Component Analysis Using MAP Estimation

    Science.gov (United States)

    Chen, Fan; Kotani, Kazunori

    Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical ICA to alleviate the problem. Since the prior is constructed upon the classification information from the training data, we refer to the proposed ICA model with a selective prior as a supervised ICA (sICA). We formulated the learning rule for sICA by taking a Maximum a Posteriori (MAP) scheme and further derived a fixed point algorithm for learning the de-mixing matrix. We investigate the performance of sICA in facial expression recognition from the aspects of both correct rate of recognition and robustness even with few independent components.

  7. 应用生长、分级的自组织映射模型进行意识任务分类%GROWING HIERARCHICAL SELF-ORGANIZING MAP MODELS FOR MENTAL TASK CLASSIFICATION

    Institute of Scientific and Technical Information of China (English)

    刘海龙; 王珏; 郑崇勋

    2005-01-01

    提出一种使用生长、分级的自组织映射(growing hierarchical self-organizing map,GHSOM)模型进行基于EEG信号的意识任务分类来实现脑机接口技术的方法.GHSOM模型是自组织映射(self-organizing map,SOM)的一种变体,由多层的SOM组成,具有一定的分级结构,能够表达数据中不同层次的信息.同时研究了使用平均量化误差(mean quantization error,mqe)和量化误差(quantization error,qe)两种方法实现的GHSOM模型对意识任务分类的作用.结果表明,GHSOM模型对于意识任务的可分性能够提供可视化的信息,并且发现使用量化误差方法实现的GHSOM模型提供较多的数据信息和较高的分类精度.使用GHSOM模型进行了5类意识任务的分类,平均分类精度可达80%.

  8. Classification of Flos Lonicerae Based on Self-Organizing Feature Map Neural Network%基于自组织特征映射神经网络的金银花分类研究

    Institute of Scientific and Technical Information of China (English)

    申明金

    2013-01-01

    自组织特征映射神经网络(SOM)以无监督方式进行网络训练,具有自组织功能.网络通过自身训练,自动对输入模式进行分类.中药药用价值与其所含微量元素有直接的关系,药材分类是中药质量控制的重要方法.将金银花中微量元素含量作为网络输入,利用自组织特征映射神经网络对不同产地金银花进行分类.结果表明分类效果较好,符合生产实际.%Self-organizing feature map neural network(SOM) trains the network in a way of unsupervised learning and it has the function of self-organising.The network can automatively sort the input pattern by self-training.Traiditional Chinese medicinal value is directly related to trace elements and classification is an important method for quality control.The trace element contents were used as the inputs of network,so the flos lonicerae of different producing area was classified by self-organizing feature map neural network.The results show that the effect of classification is good and according with practical production.

  9. Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions

    Directory of Open Access Journals (Sweden)

    Ke Wu

    2017-03-01

    Full Text Available Due to the relatively low temporal resolutions of high spatial resolution (HR remotely sensed images, land-cover change detection (LCCD may have to use multi-temporal images with different resolutions. The low spatial resolution (LR images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Soft classification (SC can produce the proportional fractions of land-covers, on which sub-pixel mapping (SPM can construct fine resolution land-cover maps to reduce the low-spatial-resolution-problem to some extent. Thus, in this paper, sub-pixel land-cover change detection with the use of different resolution images (SLCCD_DR is addressed based on SC and SPM. Previously, endmember combinations within pixels are ignored in the LR image, which may result in flawed fractional differences. Meanwhile, the information of a known HR land-cover map is insignificantly treated in the SPM models, which leads to a reluctant SLCCD_DR result. In order to overcome these issues, a novel approach based on a back propagation neural network (BPNN with different resolution images (BPNN_DR is proposed in this paper. Firstly, endmember variability per pixel is considered during the SC process to ensure the high accuracy of the derived proportional fractional difference image. After that, the BPNN-based SPM model is constructed by a complete supervised framework. It takes full advantage of the prior known HR image, whether it predates or postdates the LR image, to train the BPNN, so that a sub-pixel change detection map is generated effectively. The proposed BPNN_DR is compared with four state-of-the-art methods at different scale factors. The experimental results using both synthetic data and real images demonstrated that it can outperform with a more detailed change detection map being produced.

  10. On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework

    Directory of Open Access Journals (Sweden)

    Christoff Fourie

    2015-06-01

    Full Text Available Search-centric, sample supervised image segmentation has been demonstrated as a viable general approach applicable within the context of remote sensing image analysis. Such an approach casts the controlling parameters of image processing—generating segments—as a multidimensional search problem resolvable via efficient search methods. In this work, this general approach is analyzed in the context of connected component segmentation. A specific formulation of connected component labeling, based on quasi-flat zones, allows for the addition of arbitrary segment attributes to contribute to the nature of the output. This is in addition to core tunable parameters controlling the basic nature of connected components. Additional tunable constituents may also be introduced into such a framework, allowing flexibility in the definition of connected component connectivity, either directly via defining connectivity differently or via additional processes such as data mapping functions. The relative merits of these two additional constituents, namely the addition of tunable attributes and data mapping functions, are contrasted in a general remote sensing image analysis setting. Interestingly, tunable attributes in such a context, conjectured to be safely useful in general settings, were found detrimental under cross-validated conditions. This is in addition to this constituent’s requiring substantially greater computing time. Casting connectivity definitions as a searchable component, here via the utilization of data mapping functions, proved more beneficial and robust in this context. The results suggest that further investigations into such a general framework could benefit more from focusing on the aspects of data mapping and modifiable connectivity as opposed to the utility of thresholding various geometric and spectral attributes.

  11. Self-organization and clustering algorithms

    Science.gov (United States)

    Bezdek, James C.

    1991-01-01

    Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.

  12. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery

    Science.gov (United States)

    Michez, Adrien; Piégay, Hervé; Jonathan, Lisein; Claessens, Hugues; Lejeune, Philippe

    2016-02-01

    Riparian zones are key landscape features, representing the interface between terrestrial and aquatic ecosystems. Although they have been influenced by human activities for centuries, their degradation has increased during the 20th century. Concomitant with (or as consequences of) these disturbances, the invasion of exotic species has increased throughout the world's riparian zones. In our study, we propose a easily reproducible methodological framework to map three riparian invasive taxa using Unmanned Aerial Systems (UAS) imagery: Impatiens glandulifera Royle, Heracleum mantegazzianum Sommier and Levier, and Japanese knotweed (Fallopia sachalinensis (F. Schmidt Petrop.), Fallopia japonica (Houtt.) and hybrids). Based on visible and near-infrared UAS orthophoto, we derived simple spectral and texture image metrics computed at various scales of image segmentation (10, 30, 45, 60 using eCognition software). Supervised classification based on the random forests algorithm was used to identify the most relevant variable (or combination of variables) derived from UAS imagery for mapping riparian invasive plant species. The models were built using 20% of the dataset, the rest of the dataset being used as a test set (80%). Except for H. mantegazzianum, the best results in terms of global accuracy were achieved with the finest scale of analysis (segmentation scale parameter = 10). The best values of overall accuracies reached 72%, 68%, and 97% for I. glandulifera, Japanese knotweed, and H. mantegazzianum respectively. In terms of selected metrics, simple spectral metrics (layer mean/camera brightness) were the most used. Our results also confirm the added value of texture metrics (GLCM derivatives) for mapping riparian invasive species. The results obtained for I. glandulifera and Japanese knotweed do not reach sufficient accuracies for operational applications. However, the results achieved for H. mantegazzianum are encouraging. The high accuracies values combined to

  13. Combining Self-organizing Feature Map with Support Vector Regression Based on Expert System%自组织映射算法与基于专家系统的支持向量回归的结合

    Institute of Scientific and Technical Information of China (English)

    王玲; 穆志纯; 郭辉

    2005-01-01

    A new approach is proposed to model nonlinear dynamic systems by combining SOM (self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalist performance in comparison with the single SVR model.

  14. Cytoskeletal self-organization in neuromorphogenesis.

    Science.gov (United States)

    Dehmelt, Leif

    2014-01-01

    Self-organization of dynamic microtubules via interactions with associated motors plays a critical role in spindle formation. The microtubule-based mechanisms underlying other aspects of cellular morphogenesis, such as the formation and development of protrusions from neuronal cells is less well understood. In a recent study, we investigated the molecular mechanism that underlies the massive reorganization of microtubules induced in non-neuronal cells by expression of the neuronal microtubule stabilizer MAP2c. In that study we directly observed cortical dynein complexes and how they affect the dynamic behavior of motile microtubules in living cells. We found that stationary dynein complexes transiently associate with motile microtubules near the cell cortex and that their rapid turnover facilitates efficient microtubule transport. Here, we discuss our findings in the larger context of cellular morphogenesis with specific focus on self-organizing principles from which cellular shape patterns such as the thin protrusions of neurons can emerge.

  15. Emergence or self-organization?

    Science.gov (United States)

    2011-01-01

    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. PMID:21966574

  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. A self-organized neural comparator.

    Science.gov (United States)

    Ludueña, Guillermo A; Gros, Claudius

    2013-04-01

    Learning algorithms need generally the ability to compare several streams of information. Neural learning architectures hence need a unit, a comparator, able to compare several inputs encoding either internal or external information, for instance, predictions and sensory readings. Without the possibility of comparing the values of predictions to actual sensory inputs, reward evaluation and supervised learning would not be possible. Comparators are usually not implemented explicitly. Necessary comparisons are commonly performed by directly comparing the respective activities one-to-one. This implies that the characteristics of the two input streams (like size and encoding) must be provided at the time of designing the system. It is, however, plausible that biological comparators emerge from self-organizing, genetically encoded principles, which allow the system to adapt to the changes in the input and the organism. We propose an unsupervised neural circuitry, where the function of input comparison emerges via self-organization only from the interaction of the system with the respective inputs, without external influence or supervision. The proposed neural comparator adapts in an unsupervised form according to the correlations present in the input streams. The system consists of a multilayer feedforward neural network, which follows a local output minimization (anti-Hebbian) rule for adaptation of the synaptic weights. The local output minimization allows the circuit to autonomously acquire the capability of comparing the neural activities received from different neural populations, which may differ in population size and the neural encoding used. The comparator is able to compare objects never encountered before in the sensory input streams and evaluate a measure of their similarity even when differently encoded.

  18. Dynamic Growing Self-organizing Maps for Surface Reconstruction from Point Clouds%动态生长的自组织神经网络点云重建技术

    Institute of Scientific and Technical Information of China (English)

    张月; 戴宁; 刘浩; 李大伟

    2016-01-01

    为了提高自组织特征映射网络算法中点云重建技术的质量、收敛速度和表面精度,提出一种动态生长的自组织神经网络算法。首先基于自组织神经网络算法,构造了球体三角网格作为神经网络的映射结构,正确选择拓扑邻域的环数,通过对大量无规则节点进行网络训练和学习达到神经元节点的分裂,改变了网络结构的固定性,并删除不稳定的网格节点;然后对网格进行优化,让神经元节点与输入的离散点保持更加的紧密,得到较好的点云重建结果。与自组织特征映射算法训练特性相比,该算法减少了计算量,提高了网络训练的收敛速度和离散点云重建的表面精度,特别是针对海量点云数据或者含有大量噪声点云数据的重建效果更明显。%In order to improve the quality, rate of convergence and surface accuracy of point cloud reconstruction in the self-organizing neural network, the dynamic growing self-organizing neural networks algorithm is proposed in this paper. Firstly by the self-organizing maps algorithm, we construct the spherical triangle mesh as maps of the neural network and select the right loop numbers of the topology neighborhood. Then, we split the nodes and delete the unstable nodes to change the immobility of the network structure by training and learning of neural network for unorganized scattered point clouds. In addition, we optimize the grid to make the neural nodes and discrete points keep closer together. Finally, experiments demonstrate that this method can generate favorable re-sults. Compared with the training characteristics of the self-organizing neural network, the algorithm can reduce the amount of calculation and improve the rate of convergence and surface accuracy of the scattered point clouds reconstruction. Especially, it is more apparent of effect for the reconstruction of a huge amount of data or the point clouds with a lot of

  19. 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...... 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......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...

  20. Self-Organized Criticality Systems

    Science.gov (United States)

    Aschwanden, M. J.

    2013-07-01

    Contents: (1) Introduction - Norma B. Crosby --- (2) Theoretical Models of SOC Systems - Markus J. Aschwanden --- (3) SOC and Fractal Geometry - R. T. James McAteer --- (4) Percolation Models of Self-Organized Critical Phenomena - Alexander V. Milovanov --- (5) Criticality and Self-Organization in Branching Processes: Application to Natural Hazards - Álvaro Corral, Francesc Font-Clos --- (6) Power Laws of Recurrence Networks - Yong Zou, Jobst Heitzig, Jürgen Kurths --- (7) SOC computer simolations - Gunnar Pruessner --- (8) SOC Laboratory Experiments - Gunnar Pruessner --- (9) Self-Organizing Complex Earthquakes: Scaling in Data, Models, and Forecasting - Michael K. Sachs et al. --- (10) Wildfires and the Forest-Fire Model - Stefan Hergarten --- (11) SOC in Landslides - Stefan Hergarten --- (12) SOC and Solar Flares - Paul Charbonneau --- (13) SOC Systems in Astrophysics - Markus J. Aschwanden ---

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

    Science.gov (United States)

    Chen, Yun; Yang, Hui

    2012-09-01

    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.

  2. Modelling of habitat conditions by self-organizing feature maps using relations between soil, plant chemical properties and type of basaltoides

    Directory of Open Access Journals (Sweden)

    Piotr Kosiba

    2011-01-01

    Full Text Available The paper shows the use of Kohonen's network for classification of basaltoides on the base of chemical properties of soils and Polypodium vulgare L. The study area was Lower Silesia (Poland. The archival data were: chemical composition of types of basaltoides from 89 sites (Al2O3, CaO, FeO, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2 and TiO2, elements contents in soils (Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, S, Ti and Zn and leaves of P. vulgare (Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, N, Ni, P, Pb, S, Ti and Zn from 20 sites. Descriptive statistical parameters of soils and leaves chemical properties have been shown, statistical analyses using ANOVA and relationships between chemical elements were carried out, and SOFM models have been constructed. The study revealed that the ordination of individuals and groups of neurons in topological maps of plant and soil chemical properties are similar. The constructed models are related with significantly different contents of elements in plants and soils. These models represent different chemical types of soils and are connected with ordination of types of basaltoides worked out by SOFM model of TAS division. 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.

  3. A self-organized, distributed, and adaptive rule-based induction system.

    Science.gov (United States)

    Rojanavasu, Pornthep; Dam, Hai Huong; Abbass, Hussein A; Lokan, Chris; Pinngern, Ouen

    2009-03-01

    Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs sUpervised Classifier System (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a self-organized map (SOM) neural network. The overall classification problem is decomposed adaptively and in real time by the SOM into subproblems, each of which is handled by a separate UCS. The framework is also tested with replacing UCS by a feedforward artificial neural network (ANN). Experiments on several synthetic and real data sets, including a very large real data set, show that the accuracy of classifications in the proposed distributed environment is as good or better than in the nondistributed environment, and execution is faster. In general, each UCS attached to a cell in the SOM has a much smaller population size than a single UCS working on the overall problem; since each data instance is exposed to a smaller population size than in the single population approach, the throughput of the overall system increases. The experiments show that the proposed framework can decompose a problem adaptively into subproblems, maintaining or improving accuracy and increasing speed.

  4. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    2012-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  5. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    González Acuña, Ana Isabel

    2014-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  6. Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits.

    Science.gov (United States)

    Ihmaid, Saleh K; Ahmed, Hany E A; Zayed, Mohamed F; Abadleh, Mohammed M

    2016-01-30

    The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool 'Emergent Self-Organizing Maps' for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.

  7. Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace.

    Science.gov (United States)

    Zhu, Daqi; Huang, Huan; Yang, S X

    2013-04-01

    For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

  8. Identifying trace metal distribution and occurrence in sediments, inundated soils, and non-flooded soils of a reservoir catchment using Self-Organizing Maps, an artificial neural network method.

    Science.gov (United States)

    Cheng, Fangyan; Liu, Shiliang; Yin, Yijie; Zhang, Yueqiu; Zhao, Qinghe; Dong, Shikui

    2017-07-10

    The Lancang-Mekong River is a trans-boundary river which provides a livelihood for over 60 million people in Southeast Asia. Its environmental security is vital to both local and regional inhabitants. Efforts have been undertaken to identify controlling factors of the distribution of trace metals in sediments and soils of the Manwan Reservoir catchment in the Lancang-Mekong River basin. The physicochemical attributes of 63 spatially distributed soil and sediment samples, along with land-use, flooding, topographic, and location characteristics, were analyzed using the Self-Organizing Map (SOM) methodology. The SOM permits the analysis of complex multivariate datasets and gives a visual interpretation that is generally not easy to obtain using traditional statistical methods. Across the catchment, enrichments of trace metals are rare overall, despite the severely enriched cadmium (Cd). The analysis of SOM showed that flooded levels and land-use types were associated with high concentrations of Cd. Sediments and inundated soils covered with shrub and open woodlands in downstream always have a high concentration of Cd. The results demonstrate that SOM is a useful tool that can aid in the interpretation of complex datasets and help identify the environment of enriched metals on a catchment scale.

  9. Investigating the effect of landfill leachates on the characteristics of dissolved organic matter in groundwater using excitation-emission matrix fluorescence spectra coupled with fluorescence regional integration and self-organizing map.

    Science.gov (United States)

    He, Xiao-Song; Fan, Qin-Dong

    2016-11-01

    For the purpose of investigating the effect of landfill leachate on the characteristics of organic matter in groundwater, groundwater samples were collected near and in a landfill site, and dissolved organic matter (DOM) was extracted from the groundwater samples and characterized by excitation-emission matrix (EEM) fluorescence spectra combined with fluorescence regional integration (FRI) and self-organizing map (SOM). The results showed that the groundwater DOM comprised humic-, fulvic-, and protein-like substances. The concentration of humic-like matter showed no obvious variation for all groundwater except the sample collected in the landfill site. Fulvic-like substance content decreased when the groundwater was polluted by landfill leachates. There were two kinds of protein-like matter in the groundwater. One kind was bound to humic-like substances, and its content did not change along with groundwater pollution. However, the other kind was present as "free" molecules or else bound in proteins, and its concentration increased significantly when the groundwater was polluted by landfill leachates. The FRI and SOM methods both can characterize the composition and evolution of DOM in the groundwater. However, the SOM analysis can identify whether protein-like moieties was bound to humic-like matter.

  10. 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.

  11. Segmentation of CAPTCHA characters based on self-organizing maps and Voronoi%SOM聚类与Voronoi图在验证码字符分割中的应用

    Institute of Scientific and Technical Information of China (English)

    简献忠; 曹树建; 郭强

    2015-01-01

    字符分割是验证码字符识别的关键。为了解决粘连字符构成的验证码分割成功率低的问题,提出了一种基于SOM(self-organizing maps)神经网络聚类与维诺图(Voronoi)骨架形态分析相结合的粘连字符分割算法。该算法通过连通分量区分粘连字符,然后利用Voronoi 图获得粘连字符的骨架形态,提取粘连字符的骨架特征点;根据SOM聚类后的拓扑神经元分布确定分割点,完成粘连字符骨架的分割与复原。用网络验证码图片集进行了测试,实验效果与滴水法和连通分量提取法对比显示了该分割算法的优越性。该算法对各种字符粘连类型及字体倾斜扭曲的验证码均能准确分割,为粘连字符分割提供了一种新的方法。%Character segmentation is the point in CAPTCHA recognition.As the connected characters in CAPTCHA would be segmented with a low success rate,this paper proposed a character segmentation algorithm based on the clustering of the tou-ching region via self-organizing maps and skeletonization via Voronoi.Firstly,it used connected-component-based method to confirm connected character pairs,and selected feature points through a skeletonization process by Voronoi.Then determined the segmentation points by the neurons of SOM,leading to the final segmentation and character restoration.The results from the tests on the online CAPTCHA collections show that this algorithm achieves a better performance than the drop-fall and the con-nected-component-based algorithms.It can segment varieties of connected and distorted CAPTCHA,providing a new method for the segmentation of connected characters.

  12. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  13. Self-Organizing Tunnel Peers

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Tunneling is an important approach in IPv6 transition techniques. The tunnel broker model provides a way to build virtual IPv6 networks without manual configuration.However, neither it adapts performance variation on the IPv4 infrastructure,nor it is a scalable solution for a wide-area IPv6 networking environment. In this paper, a self-organizing tunnel peer (SOTP)model is presented. Tunnel peers are clustered in the SOTP system so that optimization is scalable. Four primitive operations related to cluster construction - arrest,release,division and death - endow the system with the nature of self-organization.Occurrence and behavior of the operations are decided by criteria on the IPv4 end-to-end performance; hence measurement is an indispensable component of the system. The metabolism of cluster relaxes the requirement to accuracy of measurement and optimization.

  14. Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

    Science.gov (United States)

    Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

    Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

  15. Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOULi-Ming; CHENTian-Lun

    2004-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on the sewe also investigate the effect of the refractoryperiod on the self-organized criticality of the system.

  16. Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOU Li-Ming; CHEN Tian-Lun

    2004-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-tire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on these we also investigate the effect of the refractoryperiod on the self-organized criticality of the system.

  17. Event Sequence Analysis using Self Organizing Map

    OpenAIRE

    2012-01-01

    In today’s world we have abundance of data and scarcity of Knowledge data mining field emerged as the fit of thetool to the problem. With the advent of internet technology and the exponential growth in the technology behind theworld wide web, the concept of web mining and found a place for itself and emerged as a separate field of research.Web mining involves a wide range of applications that aim at discovering and extracting hidden information in datastored on the Web. Web log analysis is an...

  18. 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.

  19. 一种增长型自组织特征映射文本聚类方法%A Growing Self-organizing Feature Map Text-based Clustering Method

    Institute of Scientific and Technical Information of China (English)

    张颖超; 李继扬

    2012-01-01

    To build a harmonious and civilized Internet environment, poor text messages on the network to enhance the recognition and response capabilities. Article uses a novel method based on growing self-organizing feature map (GSOFM) and latent semantic indexing (LSI) method for performing a combination of text clustering.The combination of these two algorithms to find global and local features of the model. Experiments under the same conditions used in this new model and a single GSOFM and compared. Experimental results show that: The new combination of two technologies compared with the single GSOFM method improves the accuracy of clustering results, reducing the computation time for performing text clustering network provides a better way.%为建设和谐文明的网络环境,提升对网络不良文本信息的识别和应对能力.文章使用一种新颖的基于增长型自组织特征映射(GSOFM)和潜在语义索引(LSI)相结合方法用于不良文本聚类.这两种算法的结合能够发现全局和局部的模式特点.实验在相同的条件下使用了这种新颖的模式并和单一的GSOFM相比较.实验结果证明:这种新的两种技术的结合与单一的GSOFM方法相比提高了聚类结果的精确性,缩短了计算时问,为网络不良文本聚类提供了一种较好的方法.

  20. Research on Deep Web Classification Approach Based on Quantum Self-organization Feature Mapping Network%基于量子自组织神经网络的Deep Web分类方法研究

    Institute of Scientific and Technical Information of China (English)

    张亮; 陆余良; 房珊瑶

    2011-01-01

    In order to solve the problem of Deep Web data sources classification, this paper firstly researched how features in different position could effect the domain of Deep Web interfaces, and proposed a feature selection method RankFW which is based on Ranked weights.Then, a quantum self-organization feature mapping network model was proposed with a classification algorithm.This model relies on the feature vectors and target vectors incoordinately in different phases of training, making a more centralized distribution of winner neurons in competition layer and more obvious boundaries among clusters.Finally, some experiments were designed and carried out on the expanded TEL-8 dataset to test the validity of RankFW and DR-QSOFM.%针对Deep Web数据源主题分类问题,首先研究了不同位置的特征项对Deep Web接口领域分类的影响,提出一种基于分级权重的特征选择方法RankFW;然后提出一种依赖领域知识的量子自组织特征映射神经网络模型DR-QSOFM及其分类算法,该模型在训练的不同阶段对特征向量和目标向量产生不同程度的依赖,使竞争层中获胜神经元的分布更为集中,簇的区域划分更为明显;最后,在扩展后的TEL-8数据集上进行的实验验证了RankFW和DR-QSOFM的有效性.

  1. Networking algorithm based on self-organizing map neural network for VANET%基于自组织映射神经网络的VANET组网算法

    Institute of Scientific and Technical Information of China (English)

    吴怡; 杨琼; 吴庆祥; 沈连丰; 林潇

    2011-01-01

    研究了应用于汽车辅助驾驶、无人驾驶等智能交通领域的车辆组网方法,提出一种将自组织映射神经网络算法应用于车辆自组织网络进行车辆组网的算法,该算法根据车辆定时发出的消息中位置、行驶方向等信息对车辆按目的地、行驶方向的相似性进行组网,组网后的车辆主要接收并处理与之在同一个网络中的车辆的信息.理论分析和仿真结果表明,组网后的系统传输时延远低于未组网通信情况,吞吐量有显著提高.%An approach was proposed to apply a self-organizing map neural network to organize vehicular ad hoc networks, which were used for car driving-aid systems and automatic driverless systems in intelligent transportation area. Based on the location and driving direction information of the periodic message of each vehicle, the vehicles were organized into vehicular ad hoc networks according to the similarity of destination and driving direction. The organized vehicles only communicate mainly with the vehicles in the same vehicular ad hoc network and deal with messages from the same network. Theoretic analysis and simulation results show that the system transmission delay of this proposed algorithm is lower than one of the previous method, and the system throughput is remarkably improved.

  2. Understanding and Self-Organization.

    Science.gov (United States)

    Newton, Natika W

    2017-01-01

    How do we manage to understand a completely novel state of affairs, such as the sudden effects of an unexpected earthquake, or the arrival of a total stranger instead of the sister we were waiting for? In each case, for a moment we might be stunned, but we are able quite quickly to fit these events into our overall framework for understanding the world. However, terrified and despairing we feel, we know what earthquakes are and this event fits that schema; in the case of the stranger we know that this kind of thing happens, and that we must ask the stranger "Who are you, and where is my sister?" This paper asks about the mechanisms by which we rapidly achieve an understanding of our world, both the unexpected changes we may experience, and the ongoing comfortable familiarity we normally have with our surroundings. We attempt a solution by means of examining fundamental questions: What is it to understand something?What sorts of things do we try to understand?Is there a conscious EXPERIENCE of understanding?Does understanding involve conscious mental images?What is self-organization? I will argue that these questions revolve around the need of a living organism to take action, and that understanding anything involves knowing how we might act relative to that thing in our environment. The experience of understanding is a feeling that the action affordances of a situation are clear and available. Action (as opposed to reaction) includes imagery, particularly motor imagery, which can be used in the guidance of action. Understanding requires a conscious process involving motor imagery of action affordances, and action can be understood only in self-organizational terms. I explain how self-organization can ground the kinds of action affordance experience needed for conscious understanding. The paper concludes that our day-to-day understanding of our environment is the result of a self-organizing process.

  3. Self-Organized Network Flows

    CERN Document Server

    Helbing, D; Lämmer, S; Helbing, Dirk; Siegmeier, Jan; L\\"{a}mmer, Stefan

    2007-01-01

    A model for traffic flow in street networks or material flows in supply networks is presented, that takes into account the conservation of cars or materials and other significant features of traffic flows such as jam formation, spillovers, and load-dependent transportation times. Furthermore, conflicts or coordination problems of intersecting or merging flows are considered as well. Making assumptions regarding the permeability of the intersection as a function of the conflicting flows and the queue lengths, we find self-organized oscillations in the flows similar to the operation of traffic lights.

  4. Self-organizing biochemical cycles

    Science.gov (United States)

    Orgel, L. E.; Bada, J. L. (Principal Investigator)

    2000-01-01

    I examine the plausibility of theories that postulate the development of complex chemical organization without requiring the replication of genetic polymers such as RNA. One conclusion is that theories that involve the organization of complex, small-molecule metabolic cycles such as the reductive citric acid cycle on mineral surfaces make unreasonable assumptions about the catalytic properties of minerals and the ability of minerals to organize sequences of disparate reactions. Another conclusion is that data in the Beilstein Handbook of Organic Chemistry that have been claimed to support the hypothesis that the reductive citric acid cycle originated as a self-organized cycle can more plausibly be interpreted in a different way.

  5. Self-organizing nets for optimization.

    Science.gov (United States)

    Milano, Michele; Koumoutsakos, Petros; Schmidhuber, Jürgen

    2004-05-01

    Given some optimization problem and a series of typically expensive trials of solution candidates sampled from a search space, how can we efficiently select the next candidate? We address this fundamental problem by embedding simple optimization strategies in learning algorithms inspired by Kohonen's self-organizing maps and neural gas networks. Our adaptive nets or grids are used to identify and exploit search space regions that maximize the probability of generating points closer to the optima. Net nodes are attracted by candidates that lead to improved evaluations, thus, quickly biasing the active data selection process toward promising regions, without loss of ability to escape from local optima. On standard benchmark functions, our techniques perform more reliably than the widely used covariance matrix adaptation evolution strategy. The proposed algorithm is also applied to the problem of drag reduction in a flow past an actively controlled circular cylinder, leading to unprecedented drag reduction.

  6. 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.

  7. 基于样本相关度和SOM的改进型Wang-Mendel算法%An Improved Wang-Mendel Method Based on Cooperation Degree of Sample and Self-Organizing Mapping

    Institute of Scientific and Technical Information of China (English)

    缑锦; 陈文瑜

    2013-01-01

    Wang-Mendel algorithm is commonly used as a classic method to generate fuzzy rule base. But rules with low confidence are usually extracted when noise appears in the sample data set, while its efficiency also often drops fast when the scale of sample data increases. To solve those problems, two methods, cooperation relationship and self-organizing mapping ( SOM) neural network, are introduced. Cooperation relationship among sample data improves the accuracy of rules and approximation ability to the original model. On the other hand, SOM can well preprocess sample data for denoising and reduce its scale through a self-adaptive learning procedure of weights network. Then an improved Wang-Mendel algorithm is proposed based on cooperation relationship degree of sample data and SOM. The experimental results, including trigonometric function approximation and artificial driving simulation of a train operation control system, show its completeness, robustness and operating efficiency.%Wang-Mendel算法是生成模糊规则库的经典算法。处理过程中,当样本数据存在噪声时,该算法易提取出可信度较低的规则;当样本数据规模增大时,算法效率易快速下降。针对这两个问题,引入样本间协调关系可提高结果的准确性,改善逼近性能。利用SOM算法对样本预处理可有效去噪,且其对样本分布的自适应学习能力可在一定程度上减小样本规模。基于样本相关度和SOM算法,文中提出一种Wang-Mendel模糊规则提取算法,函数逼近和列车控制系统的仿真实验结果表明其具有较好的完备性、鲁棒性和效率。

  8. What Is a Doctorate? A Concept-Mapped Analysis of Process versus Product in the Supervision of Lab-Based PhDs

    Science.gov (United States)

    Kandiko, Camille B.; Kinchin, Ian M.

    2012-01-01

    Background: Concept-mapping and interview techniques are used to track knowledge and understanding over the duration of PhD study amongst four students and their supervisors in the course of full-time research towards their PhDs. This work is in contrast to much PhD supervision research and policy research that focuses on supervisory styles and…

  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 and social science

    OpenAIRE

    Barbrook-Johnson, P.; Anzola, D; Cano, J.I.

    2017-01-01

    Abstract Complexity science and its methodological applications have increased in popularity in social science during the last two decades. One key concept within complexity science is that of self-organization. Self-organization is used to refer to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level. In spite of its potential relevance for the study of social dynamics, the articulation and use of the concept of self-organization has been kept ...

  11. 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.

  12. Self-organizing model of motor cortical activities during drawing

    Science.gov (United States)

    Lin, Siming H.; Si, Jennie; Schwartz, Andrew B.

    1996-05-01

    The population vector algorithm has been developed to combine the simultaneous direction- related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In our study, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. Self-organizing feature mapping (SOFM) is used to select the optimal set of weights in the model to determine the contribution of individual neuron to the overall movement. The correspondence between the movement directions and the discharge patterns of the motor cortical neurons is established in the output map. The topology preserving property of the SOFM is used to analyze real recorded data of a behavior monkey. The data used in this analysis were taken while the monkey was drawing spirals and doing the center out movement. Using such a statistical model, the monkey's arm moving directions could be well predicted based on the motor cortex neuronal firing information.

  13. Self-Steered Self-Organization

    NARCIS (Netherlands)

    Keijzer, Fred; Tschacher, W.; Dauwalder, J.P.

    2003-01-01

    Self-organization has become a well-established phenomenon in physics. It is now also propagated as an important phenomenon in psychology. What is the difference between these two forms of self-organization? One important way in which these two forms are distinguished is by the additional presence o

  14. Physical Foundations of Self-organizing Systems

    Science.gov (United States)

    Chatterjee, Atanu; Georgiev, Georgi

    2014-03-01

    The appearance of coherent global pattern due to local interactions is known as self-organization. Self-organization is a spontaneous process in highly non-equilibrium dissipative systems that form structures which tend to maximize energy dissipation by leveling off energy gradients. This follows as a direct consequence of the Second Law of Thermodynamics. Also, a local interaction embodies in the above definition a mechanistic dimension to self-organization. The link between mechanics and the Second Law of Thermodynamics lie in the Principle of Least Action, a strong law of nature that is obeyed in every spontaneous process. Thus, self-organization rests on two basic foundational principles of nature namely, the Second Law of Thermodynamics and the Principle of Least Action. We attempt to develop a formal definition of self-organization based on those principles.

  15. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  16. Clustering Study of a Growing Self-Organizing Neural Network%一种生长型自组织神经网络的聚类研究

    Institute of Scientific and Technical Information of China (English)

    傅雪; 张少白

    2011-01-01

    The self-organizing feature maps is a good clustering tool, but there are some restrictions, such as it needs to pre-define the network size, its convergence is poor and the structure is not flexible. To overcome these shortcomings, a clustering method based on a growing self-organizing neural network is proposed by the knowledge of self-organizing neural network. This method controls neural's prowths and deletions by implementing trigger mechamiam of the threshold value without supervision, and through making adjustments of neural weight,it can get clustering results of data objects. The experiment results prove the method's effectiveness and superiority by choosing data objects in two-dimensional space aa input samples.%自组织特征映射神经网络SOM(Self-Organizing Feature Maps)是一种优良的聚类工具,但其存在着一些限制,如需要预先定义网络大小、网络的收敛性较差和结构不灵活等.为了克服这些不足,在自组织神经网络理论的指导下,提出了一种基于生长型自组织神经网络的聚类方法.在无监督的情况下,该方法采用阈值控制的触发机制实现网络中神经元的生长和删除,并通过神经元权值的有效调整,以期得到数据对象的聚类结果.实验以二维空间中的数据对象为输入样本,验证了该方法的有效性和优越性.

  17. A new tool for supervised classification of satellite images available on web servers: Google Maps as a case study

    Science.gov (United States)

    García-Flores, Agustín.; Paz-Gallardo, Abel; Plaza, Antonio; Li, Jun

    2016-10-01

    This paper describes a new web platform dedicated to the classification of satellite images called Hypergim. The current implementation of this platform enables users to perform classification of satellite images from any part of the world thanks to the worldwide maps provided by Google Maps. To perform this classification, Hypergim uses unsupervised algorithms like Isodata and K-means. Here, we present an extension of the original platform in which we adapt Hypergim in order to use supervised algorithms to improve the classification results. This involves a significant modification of the user interface, providing the user with a way to obtain samples of classes present in the images to use in the training phase of the classification process. Another main goal of this development is to improve the runtime of the image classification process. To achieve this goal, we use a parallel implementation of the Random Forest classification algorithm. This implementation is a modification of the well-known CURFIL software package. The use of this type of algorithms to perform image classification is widespread today thanks to its precision and ease of training. The actual implementation of Random Forest was developed using CUDA platform, which enables us to exploit the potential of several models of NVIDIA graphics processing units using them to execute general purpose computing tasks as image classification algorithms. As well as CUDA, we use other parallel libraries as Intel Boost, taking advantage of the multithreading capabilities of modern CPUs. To ensure the best possible results, the platform is deployed in a cluster of commodity graphics processing units (GPUs), so that multiple users can use the tool in a concurrent way. The experimental results indicate that this new algorithm widely outperform the previous unsupervised algorithms implemented in Hypergim, both in runtime as well as precision of the actual classification of the images.

  18. 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.

  19. Heredity and self-organization: partners in the generation and evolution of phenotypes.

    Science.gov (United States)

    Malagon, Nicolas; Larsen, Ellen

    2015-01-01

    In this review we examine the role of self-organization in the context of the evolution of morphogenesis. We provide examples to show that self-organized behavior is ubiquitous, and suggest it is a mechanism that can permit high levels of biodiversity without the invention of ever-increasing numbers of genes. We also examine the implications of self-organization for understanding the "internal descriptions" of organisms and the concept of a genotype-phenotype map. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Preliminary hard and soft bottom seafloor substrate map derived from an supervised classification of bathymetry derived from multispectral World View-2 satellite imagery of Ni'ihau Island, Territory of Main Hawaiian Islands, USA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from a supervised classification from multispectral World View-2 satellite imagery of Ni'ihau Island,...

  1. ANOMALY INTRUSION DETECTION DESIGN USING HYBRID OF UNSUPERVISED AND SUPERVISED NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Bahrololum

    2009-07-01

    Full Text Available This paper proposed a new approach to design the system using a hybrid of misuse and anomalydetection for training of normal and attack packets respectively. The utilized method for attack training isthe combination of unsupervised and supervised Neural Network (NN for Intrusion Detection System. Bythe unsupervised NN based on Self Organizing Map (SOM, attacks will be classified into smallercategories considering their similar features, and then unsupervised NN based on Backpropagation willbe used for clustering. By misuse approach known packets would be identified fast and unknown attackswill be able to detect by this method.

  2. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

  3. Quantifying self-organization in fusion plasmas

    Science.gov (United States)

    Rajković, M.; Milovanović, M.; Škorić, M. M.

    2017-05-01

    A multifaceted framework for understanding self-organization in fusion plasma dynamics is presented which concurrently manages several important issues related to the nonlinear and multiscale phenomena involved, namely,(1) it chooses the optimal template wavelet for the analysis of temporal or spatio-temporal plasma dynamics, (2) it detects parameter values at which bifurcations occur, (3) it quantifies complexity and self-organization, (4) it enables short-term prediction of nonlinear dynamics, and (5) it extracts coherent structures in turbulence by separating them from the incoherent component. The first two aspects including the detection of changes in the dynamics of a nonlinear system are illustrated by analyzing Stimulated Raman Scattering in a bounded, weakly dissipative plasma. Self-organization in the fusion plasma is quantitatively analyzed based on the numerical simulations of the Gyrokinetic-Vlasov (GKV) model of plasma dynamics. The parameters for the standard and inward shifted magnetic configurations, relevant for the Large Helical Device, were used in order to quantitatively compare self-organization and complexity in the two configurations. Finally, self-organization is analyzed for three different confinement regimes of the MAST device.

  4. Self-organization in social tagging systems

    CERN Document Server

    Liu, Chuang; Zhang, Zi-Ke

    2011-01-01

    Individuals often imitate each other to fall into the typical group, leading to a self-organized state of typical behaviors in a community. In this paper, we model self-organization in social tagging systems and illustrate the underlying interaction and dynamics. Specifically, we introduce a model in which individuals adjust their own tagging tendency to imitate the average tagging tendency. We found that when users are of low confidence, they tend to imitate others and lead to a self-organized state with active tagging. On the other hand, when users are of high confidence and are stubborn for changes, tagging becomes inactive. We observe a phase transition at a critical level of user confidence when the system changes from one regime to the other. The distributions of post length obtained from the model are compared to real data which show good agreements.

  5. Regeneration, morphogenesis and self-organization.

    Science.gov (United States)

    Goldman, Daniel

    2014-07-01

    The RIKEN Center for Developmental Biology in Kobe, Japan, hosted a meeting entitled 'Regeneration of Organs: Programming and Self-Organization' in March, 2014. Scientists from across the globe met to discuss current research on regeneration, organ morphogenesis and self-organization - and the links between these fields. A diverse range of experimental models and organ systems was presented, and the speakers aptly illustrated the unique power of each. This Meeting Review describes the major advances reported and themes emerging from this exciting meeting. © 2014. Published by The Company of Biologists Ltd.

  6. Improved self-organizing mapping tree algorithm and its application to interorganizational relationship classification%一种改进的自组织映射树算法及在组织际关系分类中的应用

    Institute of Scientific and Technical Information of China (English)

    张群洪; 刘震宇; 严静; 黄辉; 苏世彬

    2009-01-01

    The advantage and disadvantage of some kinds of the improved self-organizing neural network algorithm are discussed in the paper, and an dynamical binary-tree based self-organizing neural network is improved and implemented in detail. In the binary-tree, neunron nodes can be growing and pruning, and the self-organizing mapping structure is flexible, not needed to be determined in advance. DBTSONN1 algorithm uses single path to search the winning leaf nodes, and DBTSONN2 algorithm uses double path search, considering the hierarchical position of the winning node, which can improve the searching efficiency.By combining belief and action components of a transaction relationship, a key mediating variable set is developed to establish a database of measures, and the DBTSONN2 algorithm is applied to classify interorganizational relationship into four structures labeled "bilateral, recurrent, dominant partner, and discrete".%分析了自组织映射树各种改进算法的优缺点,改进和实现了一种基于动态二叉树的自组织神经网络(Improved dynamical binary-tree based self-organizing neural network,DBTSONN).在改进动态二叉树中神经元节点可以自动生长和剪除,无需在训练前预先确定网络结构.DBTSONN1算法采用单路径搜索最匹配叶节点(获胜神经元),DBTSONN2算法考虑了获胜神经元节点所在自组织二叉树的层次,采用双路径搜索获胜叶节点,提高了搜索效率.以交易关系的经济和行为维度建立起来的关键中介变量集为度量指标,使用该算法把组织际关系分为四种类型:双边关系、周期性关系、层级关系以及分散关系,验证该算法的效率,并分析这种组织际关系分类的实际意义.

  7. Self-organizing sensing and actuation for automatic control

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Designing Self-Organized Contextualized Feedback Loops

    NARCIS (Netherlands)

    Kalz, Marco

    2013-01-01

    Kalz, M. (2013). Designing Self-Organized Contextualized Feedback Loops. In D. Whitelock, W. Warburton, G. Wills, & L. Gilbert (Eds.), International Conference on Computer Assisted Assessment (CAA 2013). July, 9-10, 2013, University of Southampton, Southampton, UK. http://caaconference.com.

  9. 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.

  10. 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...

  11. 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...

  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...

  13. How self-organization can guide evolution.

    Science.gov (United States)

    Glancy, Jonathan; Stone, James V; Wilson, Stuart P

    2016-11-01

    Self-organization and natural selection are fundamental forces that shape the natural world. Substantial progress in understanding how these forces interact has been made through the study of abstract models. Further progress may be made by identifying a model system in which the interaction between self-organization and selection can be investigated empirically. To this end, we investigate how the self-organizing thermoregulatory huddling behaviours displayed by many species of mammals might influence natural selection of the genetic components of metabolism. By applying a simple evolutionary algorithm to a well-established model of the interactions between environmental, morphological, physiological and behavioural components of thermoregulation, we arrive at a clear, but counterintuitive, prediction: rodents that are able to huddle together in cold environments should evolve a lower thermal conductance at a faster rate than animals reared in isolation. The model therefore explains how evolution can be accelerated as a consequence of relaxed selection, and it predicts how the effect may be exaggerated by an increase in the litter size, i.e. by an increase in the capacity to use huddling behaviours for thermoregulation. Confirmation of these predictions in future experiments with rodents would constitute strong evidence of a mechanism by which self-organization can guide natural selection.

  14. Self-Organizing Tree Using Cluster Validity

    Science.gov (United States)

    Sasaki, Yasue; Suzuki, Yukinori; Miyamoto, Takayuki; Maeda, Junji

    Self-organizing tree (S-TREE) models solve clustering problems by imposing tree-structured constraints on the solution. It has a self-organizing capacity and has better performance than previous tree-structured algorithms. S-TREE carries out pruning to reduce the effect of bad leaf nodes when the tree reaches a predetermined maximum size (U), However, it is difficult to determine U beforehand because it is problem-dependent. U gives the limit of tree growth and can also prevent self-organization of the tree. It may produce an unnatural clustering. In this paper, we propose an algorithm for pruning algorithm that does not require U. This algorithm prunes extra nodes based on a significant level of cluster validity and allows the S-TREE to grow by a self-organization. The performance of the new algorithm was examined by experiments on vector quantization. The results of experiments show that natural leaf nodes are formed by this algorithm without setting the limit for the growth of the S-TREE.

  15. Neurodynamics with spatial self-organizations.

    Science.gov (United States)

    Zak, M

    1991-01-01

    A neural network architecture with self-organization in phase and actual space is proposed and discussed. Special type of differential local interconnections simulating diffusion, dispersion, and convection were investigated. It is shown that these interconnections are responsible for biological pattern formation in a homogeneous neural structure. The model suggests a phenomenological explanation of the mechanisms of edge detection in vision process.

  16. SELF-ORGANIZED CRITICALITY AND CELLULAR AUTOMATA

    Energy Technology Data Exchange (ETDEWEB)

    CREUTZ,M.

    2007-01-01

    Cellular automata provide a fascinating class of dynamical systems based on very simple rules of evolution yet capable of displaying highly complex behavior. These include simplified models for many phenomena seen in nature. Among other things, they provide insight into self-organized criticality, wherein dissipative systems naturally drive themselves to a critical state with important phenomena occurring over a wide range of length and the scales. This article begins with an overview of self-organized criticality. This is followed by a discussion of a few examples of simple cellular automaton systems, some of which may exhibit critical behavior. Finally, some of the fascinating exact mathematical properties of the Bak-Tang-Wiesenfeld sand-pile model [1] are discussed.

  17. Hierarchical Self-organization of Complex Systems

    Institute of Scientific and Technical Information of China (English)

    CHAI Li-he; WEN Dong-sheng

    2004-01-01

    Researches on organization and structure in complex systems are academic and industrial fronts in modern sciences. Though many theories are tentatively proposed to analyze complex systems, we still lack a rigorous theory on them. Complex systems possess various degrees of freedom, which means that they should exhibit all kinds of structures. However, complex systems often show similar patterns and structures. Then the question arises why such similar structures appear in all kinds of complex systems. The paper outlines a theory on freedom degree compression and the existence of hierarchical self-organization for all complex systems is found. It is freedom degree compression and hierarchical self-organization that are responsible for the existence of these similar patterns or structures observed in the complex systems.

  18. 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.

  19. Tilt aftereffects in a self-organizing model of the primary visual cortex.

    Science.gov (United States)

    Bednar, J A; Miikkulainen, R

    2000-07-01

    RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short timescales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.

  20. Self-organized podosomes are dynamic mechanosensors

    OpenAIRE

    2008-01-01

    Podosomes are self-organized dynamic actin-containing structures that adhere to the extracellular matrix via integrins [1–5]. Yet it is not clear what regulates podosome dynamics and whether podosomes can function as direct mechanosensors like focal adhesions [6–9]. We show here that myosin IIs form circular structures outside and at the podosome actin ring to regulate podosome dynamics. Inhibiting myosin II-dependent tension dissipated podosome actin rings before dissipating the myosin ring ...

  1. Big Data Empowered Self Organized Networks

    OpenAIRE

    Baldo, Nicola; Giupponi, Lorenza; Mangues-Bafalluy, Josep

    2014-01-01

    Mobile networks are generating a huge amount of data in the form of network measurements as well as network control and management interactions, and 5G is expected to make it even bigger. In this paper, we discuss the different approaches according to which this information could be leveraged using a Big Data approach. In particular, we focus on Big Data Empowered Self Organized Networks, discussing its most peculiar traits, its potential, and the relevant related work, as well as analysing s...

  2. Self organizing software research : LDRD final report.

    Energy Technology Data Exchange (ETDEWEB)

    Osbourn, Gordon Cecil

    2004-01-01

    We have made progress in developing a new statistical mechanics approach to designing self organizing systems that is unique to SNL. The primary application target for this ongoing research has been the development of new kinds of nanoscale components and hardware systems. However, this research also enables an out of the box connection to the field of software development. With appropriate modification, the collective behavior physics ideas for enabling simple hardware components to self organize may also provide design methods for a new class of software modules. Our current physics simulations suggest that populations of these special software components would be able to self assemble into a variety of much larger and more complex software systems. If successful, this would provide a radical (disruptive technology) path to developing complex, high reliability software unlike any known today. This high risk, high payoff opportunity does not fit well into existing SNL funding categories, as it is well outside of the mainstreams of both conventional software development practices and the nanoscience research area that spawned it. This LDRD effort was aimed at developing and extending the capabilities of self organizing/assembling software systems, and to demonstrate the unique capabilities and advantages of this radical new approach for software development.

  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. SELF-ORGANIZED SEMANTIC FEATURE EVOLUTION FOR AXIOMATIC DESIGN

    Institute of Scientific and Technical Information of China (English)

    HAO He; FENG Yixiong; TAN Jianrong; XUE Yang

    2008-01-01

    Aiming at the problem existing in the computer aided design process that how to express the design intents with high-level engineering terminologies, a mechanical product self-organized semantic feature evolution technology for axiomatic design is proposed, so that the constraint relations between mechanical parts could be expressed in a semantic form which is more suitable for designers. By describing the evolution rules for semantic constraint information, the abstract expression of design semantics in mechanical product evolution process is realized and the constraint relations between parts are mapped to the geometric level from the semantic level; With semantic feature relation graph, the abstract semantic description, the semantic relative structure and the semantic constraint information are linked together; And the methods of semantic feature self-organized evolution are classified. Finally, combining a design example of domestic high-speed elevator, how to apply the theory to practical product development is illustrated and this method and its validity is described and verified. According to the study results, the designers are able to represent the design intents at an advanced semantic level in a more intuitional and natural way and the automation, recursion and visualization for mechanical product axiomatic design are also realized.

  5. 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,…

  6. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  7. Self-organized motion in anisotropic swarms

    Institute of Scientific and Technical Information of China (English)

    Tianguang CHU; Long WANG; Tongwen CHEN

    2003-01-01

    This paper considers an anisotropic swarm model with a class of attraction and repulsion functions. It is shown that the members of the swarm will aggregate and eventually form a cohesive cluster of finite size around the swarm center. Moreover,It is also proved that under certain conditions, the swarm system can be completely stable, i. e., every solution converges to the equilibrium points of the system. The model and results of this paper extend a recent work on isotropic swarms to more general cases and provide further insight into the effect of the interaction pattern on self-organized motion in a swarm system.

  8. Self-organized criticality on quasiperiodic graphs

    Science.gov (United States)

    Joseph, D.

    1999-09-01

    Self-organized critical models are used to describe the 1/f-spectra of rather different physical situations like snow avalanches, noise of electric currents, luminosities of stars or topologies of landscapes. The prototype of the SOC-models is the sandpile model of Bak, Tang and Wiesenfeld (Phys. Rev. Lett. 59, (1987) 351). We implement this model on non-periodic graphs where it can become either isotropic or anisotropic and compare its properties with the periodic counterpart on the square lattice.

  9. Biologically inspired self-organizing networks

    Institute of Scientific and Technical Information of China (English)

    Naoki WAKAMIYA; Kenji LEIBNITZ; Masayuki MURATA

    2009-01-01

    Information networks are becoming more and more complex to accommodate a continuously increasing amount of traffic and networked devices, as well as having to cope with a growing diversity of operating environments and applications. Therefore, it is foreseeable that future information networks will frequently face unexpected problems, some of which could lead to the complete collapse of a network. To tackle this problem, recent attempts have been made to design novel network architectures which achieve a high level of scalability, adaptability, and robustness by taking inspiration from self-organizing biological systems. The objective of this paper is to discuss biologically inspired networking technologies.

  10. Self-organized model of cascade spreading

    Science.gov (United States)

    Gualdi, S.; Medo, M.; Zhang, Y.-C.

    2011-01-01

    We study simultaneous price drops of real stocks and show that for high drop thresholds they follow a power-law distribution. To reproduce these collective downturns, we propose a minimal self-organized model of cascade spreading based on a probabilistic response of the system elements to stress conditions. This model is solvable using the theory of branching processes and the mean-field approximation. For a wide range of parameters, the system is in a critical state and displays a power-law cascade-size distribution similar to the empirically observed one. We further generalize the model to reproduce volatility clustering and other observed properties of real stocks.

  11. Self-organized model of cascade spreading

    CERN Document Server

    Gualdi, Stanislao; Zhang, Yi-Cheng

    2010-01-01

    We study simultaneous price drops of real stocks and show that for high drop thresholds they follow a power-law distribution. To reproduce these collective downturns, we propose a self-organized model of cascade spreading based on a probabilistic response of the system's elements to stress conditions. This model is solvable using the theory of branching processes and the mean-field approximation and displays a power-law cascade-size distribution-similar to the empirically observed one-over a wide range of parameters.

  12. Self-organized chaos through polyhomeostatic optimization.

    Science.gov (United States)

    Markovic, D; Gros, Claudius

    2010-08-06

    The goal of polyhomeostatic control is to achieve a certain target distribution of behaviors, in contrast to homeostatic regulation, which aims at stabilizing a steady-state dynamical state. We consider polyhomeostasis for individual and networks of firing-rate neurons, adapting to achieve target distributions of firing rates maximizing information entropy. We show that any finite polyhomeostatic adaption rate destroys all attractors in Hopfield-like network setups, leading to intermittently bursting behavior and self-organized chaos. The importance of polyhomeostasis to adapting behavior in general is discussed.

  13. Robin Hood as self-organized criticality

    Science.gov (United States)

    Zaitsev, S. I.

    1992-11-01

    It is shown that a wide class of physical processes named low-temperature creep (or Robin Hood systems) has to demonstrate self-organized criticality. At least “real” and “toy” models (1D and 2D) demonstrate long range (restricted by the model size only) spatial correlation in Monte Carlo simulation. The models can be used for investigation of such phenomena as dislocation glide, movement of flux in superconductors, movement of domain walls in magnetics, grain boundaries in polycrystals, plastic deformation and so on.

  14. KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification

    CERN Document Server

    Fernandes, C; Merelo, J J; Ramos, V; Laredo, J L J

    2008-01-01

    In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

  15. Self-Organization of Bioinspired Fibrous Surfaces

    Science.gov (United States)

    Kang, Sung Hoon

    Nature uses fibrous surfaces for a wide range of functions such as sensing, adhesion, structural color, and self-cleaning. However, little is known about how fiber properties enable them to self-organize into diverse and complex functional forms. Using polymeric micro/nanofiber arrays with tunable properties as model systems, we demonstrate how the combination of mechanical and surface properties can be harnessed to transform an array of anchored nanofibers into a variety of complex, hierarchically organized dynamic functional surfaces. We show that the delicate balance between fiber elasticity and surface adhesion plays a critical role in determining the shape, chirality, and hierarchy of the assembled structures. We further report a strategy for controlling the long-range order of fiber assemblies by manipulating the shape and movement of the liquid-vapor interface. Our study provides fundamental understanding of the pattern formation by self-organization of bioinspired fibrous surfaces. Moreover, our new strategies offer a foundation for designing a vast assortment of functional surfaces with adhesive, optical, water-repellent, capture and release, and many more capabilities with the structural and dynamic sophistication of their biological counterparts.

  16. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  17. Self-organization of antiperiodic oscillations

    Science.gov (United States)

    Freire, J. G.; Cabeza, C.; Marti, A. C.; Pöschel, T.; Gallas, J. A. C.

    2014-12-01

    Antiperiodic oscillations forming infinite cascades of spirals were recently found experimentally and numerically in the control parameter space of an autonomous electronic circuit. They were discovered while recording one specific voltage of the circuit. Here, we show that such regular self-organization may be measured in any of the four variables of the circuit. Although the relative size of individual phases, their boundaries and the number of peaks of each characteristic oscillation depends on the physical quantity used to record them, the global structural organization of the complex phase diagrams is an invariant of the circuit. Tunable families of antiperiodic oscillations cast fresh light on new intricate behavior of nonlinear systems and open the possibility of studying hitherto unobserved phenomena.

  18. Self-organized atomic switch networks

    Science.gov (United States)

    Stieg, Adam Z.; Avizienis, Audrius V.; Sillin, Henry O.; Martin-Olmos, Cristina; Lam, Miu-Ling; Aono, Masakazu; Gimzewski, James K.

    2014-01-01

    The spontaneous emergence of complex behavior in dynamical systems occurs through the collective interaction of nonlinear elements toward a highly correlated, non-equilibrium critical state. Criticality has been proposed as a model for understanding complexity in systems whose behavior can be approximated as a state lying somewhere between order and chaos. Here we present unique, purpose-built devices, known as atomic switch networks (ASN), specifically designed to generate the class of emergent properties which underlie critical dynamics in complex systems. The network is an open, dissipative system comprised of highly interconnected (˜109/cm2) atomic switch interfaces wired through the spontaneous electroless deposition of metallic silver fractal architectures. The functional topology of ASN architectures self-organizes to produce persistent critical dynamics without fine-tuning, indicating a capacity for memory and learning via persistent critical states toward potential utility in real-time, neuromorphic computation.

  19. The self-organizing worm algorithm

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions.The main idea of this algorithm can be described as follows:disperse some worms equably in the domain;the worms exchange the information each other and creep toward the nearest high point;at last they will stop on the nearest high point.All peaks of multi-modal function can be found rapidly through studying and chasing among the worms.In contrast with the classical multi-modal optimization algorithms,SOWA is provided with a simple calculation,strong convergence,high precision,and does not need any prior knowledge.Several simulation experiments for SOWA are performed,and the complexity of SOWA is analyzed amply.The results show that SOWA is very effective in optimization of multi-modal functions.

  20. Pearls are self-organized natural ratchets.

    Science.gov (United States)

    Cartwright, Julyan H E; Checa, Antonio G; Rousseau, Marthe

    2013-07-02

    Pearls, the most flawless and highly prized of them, are perhaps the most perfectly spherical macroscopic bodies in the biological world. How are they so round? Why are other pearls solids of revolution (off-round, drop, ringed pearl), and yet others have no symmetry (baroque pearls)? We observe that with a spherical pearl the growth fronts of nacre are spirals and target patterns distributed across its surface, and that this is true for a baroque pearl, too, but that in pearls with rotational symmetry spirals and target patterns are found only in the vicinity of the poles; elsewhere the growth fronts are arrayed in ratchet fashion around the equator. We argue that pearl rotation is a self-organized phenomenon caused and sustained by physical forces from the growth fronts, and that rotating pearls are an example--perhaps unique--of a natural ratchet.

  1. 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.

  2. The Association of Pre-storm Ground Wetness with Inland Penetration of Monsoon Depressions : A Study Using Self Organizing Maps (SOM) C.M. Kishtawal Meteorology and Oceanography Group, Space Applications Center, Ahmedabad, INDIA Dev Niyogi2 Department of Agronomy, and Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana

    Science.gov (United States)

    Kishtawal, C. M.; Niyogi, D.

    2009-12-01

    Monsoon depressions (MDs)are probably the most important rain bearing systems that occur during the Indian summer monsoon season. The unique topography of Indian peninsula and Indo-china region favor the formation and development of MDs in the warm and moist air over the Bay of Bengal. After formation the MDs move in a north-northwest track along the monsoon trough to the warmer and drier heat low regions of Northwest India and Pakistan. The dynamic structure of MDs is largely maintained by convergence of atmospheric water vapor flux coupled with the lower tropospheric divergent circulation (Chen et al., 2005), and they weaken rapidly after landfall due to the lack of surface moisture fluxes (Dastoor and Krishnamurti, 1991). In the present study we explored the association between pre-storm wetness conditions and the post-landfall situation of MDs using 54-year long observations (1951-2004) of 183 MDs and daily surface rainfall. Our analysis suggests that the MD’s post-landfall behavior is most sensitive to mean inland rainfall between To-1 to To-8 days (the pre-storm rainfall), where To is the day of formation of MD in the Bay of Bengal. Further, pre-storm rainfall over a broad region along the monsoon trough is found to exhibit the maximum association with the MDs inland lifespan. We further carried out the unsupervised classification of pre-storm rainfall patterns using Self Organizing Map(SOM), a topology preserving map that maps data from higher dimensions onto a two dimensional grid(Kohenen, 1990). The SOM patterns of rainfall indicate that pre-storm wetness is strongly associated with the inland penetration length of MDs with wetter conditions supporting MDs to survive longer after the landfall. Although the pre-storm inland wetness has not been found to be associated with the formation of MDs and a number of MDs form during relatively dry inland conditions during the early (June) and late (September) phases of monsoon, the inland-penetration and post

  3. Applications of collaborative helping maps: supporting professional development, supervision and work teams in family-centered practice.

    Science.gov (United States)

    Madsen, William C

    2014-03-01

    Collaborative, family-centered practice has become an influential approach in helping efforts across a broad spectrum of human services. This article draws from previous work that presented a principle-based, practice framework of Collaborative Helping and highlighted the use of Collaborative Helping maps as a tool both to help workers think their way through complex situations and to provide a guideline for constructive conversations between families and helpers about challenging issues. It builds on that work to examine ways to utilize Collaborative Helping maps at worker, supervisory, and organizational levels to enhance and sustain collaborative, family-centered practice and weave its core values and principles into the everyday fabric of organizational cultures in human service agencies and government agencies that serve poor and marginalized families and communities.

  4. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

    Science.gov (United States)

    Naghibi, Seyed Amir; Moradi Dashtpagerdi, Mostafa

    2016-09-01

    One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

  5. Self-organization in complex systems as decision making

    CERN Document Server

    Yukalov, V I

    2014-01-01

    The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of c...

  6. 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.

  7. Functional Nanostructures and Dynamic Materials through Self-Organization

    Institute of Scientific and Technical Information of China (English)

    Jean-Marie; LEHN

    2007-01-01

    1 Results Supramolecular chemistry is actively exploring systems undergoing self-organization.The design of molecular information controlled,"programmed"and functional self-organizing systems provides an original approach to nanoscience and nanotechnology.The spontaneous but controlled generation of well-defined,functional molecular and supramolecular architectures of nanometric size through self-organization represents a means of performing programmed engineering and processing of functional nanostruct...

  8. Self-Organizing Maps for Fast LES Combustion Modeling Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Tremendous advances have been made in the development of large and accurate detailed reaction chemistry models for hydrocarbon fuels. Comparable progress has also...

  9. BUSINESS CLIENT SEGMENTATION IN BANKING USING SELF-ORGANIZING MAPS

    National Research Council Canada - National Science Library

    Mirjana Pejic Bach; Sandro Jukovic; Ksenija Dumicic; Natasa Sarlija

    2013-01-01

    ...) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational...

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

    National Research Council Canada - National Science Library

    Mirjana Pejić Bach; Sandro Juković; Ksenija Dumičić; Nataša Šarlija

    2014-01-01

    ...) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational...

  11. Vector Quantization Landmark Points for Supervised Isometric Mapping with Explicit Mapping%矢量量化地标点的显式监督等距映射算法

    Institute of Scientific and Technical Information of China (English)

    陈诗文; 王宪保; 李梦园; 姚明海

    2015-01-01

    Since isometric mapping ( ISOMAP ) has no supervision and explicit mapping function and other limitations, an improved algorithm, selection of vector quantization landmark points for supervised isometric mapping with explicit mapping ( SE-VQ-ISOMAP ) , is put forward. Firstly, the category information is introduced in the construction of neighborhood graph and geodesic distance matrix. Aiming at the problem that the landmark points are introduced into iterative optimization when distance matrix is processed, a method of vector quantization is employed instead of the traditional random selection. Thus, the whole manifold structure is indicated better by the selected samples. Finally, the radial function is regarded as basis, and consequently explicit mapping of dimensionality reduction method is obtained. On the handwritten digits sets and UCI datasets, the experimental results show that the proposed algorithm is fast and stable with a higher recognition rate.%针对等距映射( ISOMAP)无监督、不能生成显式映射函数等局限性,提出矢量量化地标点的显式监督等距映射算法。该算法首先在构建的邻域图和测地线距离矩阵中引入类别信息;然后针对在迭代优化处理距离矩阵时引入地标点的问题,运用矢量量化方法代替传统随机选取方法,使选取的地标点更能反映整个流形结构;最后把径向基函数作为函数基,得到降维方法的显式映射表示。在手写数字数据集和UCI数据集上的实验表明,文中算法降维效果快速稳定,识别率较高。

  12. Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

    Directory of Open Access Journals (Sweden)

    Martin Hitziger

    2014-01-01

    Full Text Available A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders and landslides (mixing up mineral soil horizons on slopes.

  13. Self-organizing strategies for a column-store database

    NARCIS (Netherlands)

    Ivanova, M.G.; Kersten, M.L.; Nes, N.J.

    2008-01-01

    Column-store database systems open new vistas for improved maintenance through self-organization. Individual columns are the focal point, which simplify balancing conflicting requirements. This work presents two workload-driven self-organizing techniques in a column-store, i.e. adaptive segmentation

  14. Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

    Science.gov (United States)

    2009-03-01

    overarching control of the system. For instance, ants and termites display this kind of behavior to an amazing degree. Even though many human organizations...determine whether specified hypotheses based on research objectives have been falsified or validated (since experimentation cannot verify hypotheses...self organized criticality. However, the metric used to measure self organization should be developed further. While the experimentation with self

  15. 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.

  16. The concept of self-organization in cellular architecture

    Science.gov (United States)

    Misteli, Tom

    2001-01-01

    In vivo microscopy has recently revealed the dynamic nature of many cellular organelles. The dynamic properties of several cellular structures are consistent with a role for self-organization in their formation, maintenance, and function; therefore, self-organization might be a general principle in cellular organization. PMID:11604416

  17. Self-organization in chronic pain: a concept analysis.

    Science.gov (United States)

    Monsivais, Diane

    2005-01-01

    The purpose of this article is to examine the concept of self-organization in chronic pain using Rodgers' (2000) evolutionary approach. This article describes the antecedents, attributes, and consequences of self-organization in chronic pain. Self-organization in chronic pain may be achieved through the attributes of being believed, accessing credible resources, and taking action and responsibility. Self-organization occurs when the patient with pain develops a transformed identity, new insights, and is an active, in-control participant in care. Chronic pain is a common and costly problem, and recognition of the key attributes of self-organization in this condition is an important step in promoting positive health outcomes. Rehabilitation nurses play a key role in providing credible resources and working with the patient to take action and responsibility.

  18. Self-organization criticality of debris flow rheology

    Institute of Scientific and Technical Information of China (English)

    WANG Yuyi; JAN Chyandeng; CHEN Xiaoqing; HAN Wenliang

    2003-01-01

    Based on the viewpoint of stress and strain self-organization criticality of debris flow mass, this paper probes into inter-nonlinear action between different factors in the thixotropic liquefaction system of loose clastic soil onslope to make clastic soil in slope develop naturally towards critical stress status, and slope debris flow finally occurs under trigging by rainstorm. Also according to observation and analysis of self-organization criticality of sedimentrunoff system of viscous debris flow surges in ravines and power relation between magnitude and frequency of debris flows, this paper expounds similarity of the self-organized structure of debris flow mass. The self-organized critical system is a weak chaotic system. Debris flow occurrences can be predicted accordingly by means of observation at certain time scale and analysis of self-organization criticality of magnitude, frequency and time interval of debris flows.

  19. A nanobiosensor for dynamic single cell analysis during microvascular self-organization.

    Science.gov (United States)

    Wang, S; Sun, J; Zhang, D D; Wong, P K

    2016-10-14

    The formation of microvascular networks plays essential roles in regenerative medicine and tissue engineering. Nevertheless, the self-organization mechanisms underlying the dynamic morphogenic process are poorly understood due to a paucity of effective tools for mapping the spatiotemporal dynamics of single cell behaviors. By establishing a single cell nanobiosensor along with live cell imaging, we perform dynamic single cell analysis of the morphology, displacement, and gene expression during microvascular self-organization. Dynamic single cell analysis reveals that endothelial cells self-organize into subpopulations with specialized phenotypes to form microvascular networks and identifies the involvement of Notch1-Dll4 signaling in regulating the cell subpopulations. The cell phenotype correlates with the initial Dll4 mRNA expression level and each subpopulation displays a unique dynamic Dll4 mRNA expression profile. Pharmacological perturbations and RNA interference of Notch1-Dll4 signaling modulate the cell subpopulations and modify the morphology of the microvascular network. Taken together, a nanobiosensor enables a dynamic single cell analysis approach underscoring the importance of Notch1-Dll4 signaling in microvascular self-organization.

  20. From self-organized to extended criticality

    Directory of Open Access Journals (Sweden)

    Elisa eLovecchio

    2012-04-01

    Full Text Available We address the issue of criticality that is attracting the attention of an increasing number of neurophysiologists. Our main purpose is to establish the specific nature of some dynamical processes that although physically different, are usually termed as "critical", and we focus on those characterized by the cooperative interaction of many units. We notice that the term "criticality" has been adopted to denote both noise-induced phase transitions and Self-Organized Criticality (SOC with no clear connection with the traditional phase transitions, namely the transformation of a thermodynamic system from one state of matter to another. We notice the recent attractive proposal of extended criticality advocated by Bailly and Longo, which is realized through a wide set of critical points rather than emerging as a singularity from a unique value of the control parameter. We study a set of cooperatively firing neurons and we show that for an extended set of interaction couplings the system exhibits a form of temporal complexity similar to that emerging at criticality from ordinary phase transitions. This extended criticality regime is characterized by three main properties: i In the ideal limiting case of infinitely large time period, temporal complexity corresponds to Mittag-Leffler complexity; ii For large values of the interaction coupling the periodic nature of the process becomes predominant while maintaining to some extent, in the intermediate time asymptotic region, the signature of complexity; iii Focusing our attention on firing neuron avalanches, we find two of the popular SOC properties, namely the power indexes 2 and 1.5 respectively for time length and for the intensity of the avalanches. We derive the conclusion that SOC emerges from extended criticality, thereby explaining the experimental observation of Plenz and Beggs: avalanches occur in time with surprisingly regularity, in apparent conflict with he temporal complexity of physical

  1. Self-Organization in Coordination-Driven Self-Assembly

    Science.gov (United States)

    Northrop, Brian H.; Zheng, Yao-Rong; Chi, Ki-Whan; Stang, Peter J.

    2009-01-01

    Conspectus Self-assembly allows for the preparation of highly complex molecular and supramolecular systems from relatively simple starting materials. Typically, self-assembled supramolecules are constructed by combining complementary pairs of two highly symmetric molecular components, thus limiting the chances of forming unwanted side products. Combining asymmetric molecular components or multiple complementary sets of molecules in one complex mixture can produce myriad different ordered and disordered supramolecular assemblies. Alternatively, spontaneous self-organization phenomena can promote the formation of specific product(s) out of a collection of multiple possibilities. Self-organization processes are common throughout much of nature and are especially common in biological systems. Recently, researchers have studied self-organized self-assembly in purely synthetic systems. This Account describes our investigations of self-organization in the coordination-driven self-assembly of platinum(II)-based metallosupramolecules. The modularity of the coordination-driven approach to self-assembly has allowed us to systematically study a wide variety of different factors that can control the extent of supramolecular self-organization. In particular, we have evaluated the effects of the symmetry and polarity of ambidentate donor subunits, differences in geometrical parameters (e.g. the size, angularity, and dimensionality) of Pt(II)-based acceptors and organic donors, the influence of temperature and solvent, and the effects of intermolecular steric interactions and hydrophobic interactions on self-organization. Our studies have shown that the extent of self-organization in the coordination-driven self-assembly of both 2D polygons and 3D polyhedra ranges from no organization (a statistical mixture of multiple products), to amplified organization (wherein a particular product or products are favored over others), and all the way to the absolute self-organization of

  2. Self-organization in coordination-driven self-assembly.

    Science.gov (United States)

    Northrop, Brian H; Zheng, Yao-Rong; Chi, Ki-Whan; Stang, Peter J

    2009-10-20

    Self-assembly allows for the preparation of highly complex molecular and supramolecular systems from relatively simple starting materials. Typically, self-assembled supramolecules are constructed by combining complementary pairs of two highly symmetric molecular components, thus limiting the chances of forming unwanted side products. Combining asymmetric molecular components or multiple complementary sets of molecules in one complex mixture can produce myriad different ordered and disordered supramolecular assemblies. Alternatively, spontaneous self-organization phenomena can promote the formation of specific product(s) out of a collection of multiple possibilities. Self-organization processes are common throughout much of nature and are especially common in biological systems. Recently, researchers have studied self-organized self-assembly in purely synthetic systems. This Account describes our investigations of self-organization in the coordination-driven self-assembly of platinum(II)-based metallosupramolecules. The modularity of the coordination-driven approach to self-assembly has allowed us to systematically study a wide variety of different factors that can control the extent of supramolecular self-organization. In particular, we have evaluated the effects of the symmetry and polarity of ambidentate donor subunits, differences in geometrical parameters (e.g., the size, angularity, and dimensionality) of Pt(II)-based acceptors and organic donors, the influence of temperature and solvent, and the effects of intermolecular steric interactions and hydrophobic interactions on self-organization. Our studies have shown that the extent of self-organization in the coordination-driven self-assembly of both 2D polygons and 3D polyhedra ranges from no organization (a statistical mixture of multiple products) to amplified organization (wherein a particular product or products are favored over others) and all the way to the absolute self-organization of discrete

  3. Global consensus theorem and self-organized criticality: unifying principles for understanding self-organization, swarm intelligence and mechanisms of carcinogenesis.

    Science.gov (United States)

    Rosenfeld, Simon

    2013-01-01

    Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Generally, each level in a biological hierarchy possesses its own systemic properties and requires its own way of observation, conceptualization, and modeling. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) equally applicable to neural networks and to Lotka-Volterra population dynamics. Universality of the mechanistic principles outlined by GCT allows for a unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, social networks and many other systems. Another cluster of universal laws governing the self-organization in large communities of locally interacting individuals is built around the principle of self-organized criticality (SOC). The GCT and SOC, separately or in combination, provide a conceptual basis for understanding the phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. In this paper, the point of view is put forward that apparently irreconcilable contradictions between two opposing theories of carcinogenesis, that is, the Somatic Mutation Theory and the Tissue Organization Field Theory, may be resolved using the systemic approaches

  4. Global Consensus Theorem and Self-Organized Criticality: Unifying Principles for Understanding Self-Organization, Swarm Intelligence and Mechanisms of Carcinogenesis

    Science.gov (United States)

    Rosenfeld, Simon

    2013-01-01

    Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Generally, each level in a biological hierarchy possesses its own systemic properties and requires its own way of observation, conceptualization, and modeling. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) equally applicable to neural networks and to Lotka-Volterra population dynamics. Universality of the mechanistic principles outlined by GCT allows for a unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, social networks and many other systems. Another cluster of universal laws governing the self-organization in large communities of locally interacting individuals is built around the principle of self-organized criticality (SOC). The GCT and SOC, separately or in combination, provide a conceptual basis for understanding the phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. In this paper, the point of view is put forward that apparently irreconcilable contradictions between two opposing theories of carcinogenesis, that is, the Somatic Mutation Theory and the Tissue Organization Field Theory, may be resolved using the systemic approaches

  5. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  6. Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

    Institute of Scientific and Technical Information of China (English)

    Hao Cheng; Na Yu,; Tai-Jun Wang

    2015-01-01

    Abstract¾An idea of estimating the direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map (SOFM) neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise (SNR) and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm’s bit error rate (BER) and complexity are lower.

  7. 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

  8. 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

  9. Two possible mechanisms for vortex self-organization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The vortex self-organization is investigated in this paper by four groups of numerical experiments within the framework of quasi-geostrophic model, and based on the experimental results two types of possible mechanisms for vortex self-organization are suggested. The meso-scale topography may enable separated vortices to merge into a larger scale vortex; and the interaction of meso-γand meso-β scale systems may make separated vortices to self organize a typhoon-like vortex circulation.

  10. Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-Wei; ZHOU Li-Ming; CHEN Tian-Lun

    2003-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model's avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.

  11. Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAOXiao-Wei; ZHOULi-Ming; CHENTian-Lun

    2003-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model''s avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.

  12. Self-organization in magnetic flux ropes

    Science.gov (United States)

    Lukin, Vyacheslav S.

    2014-06-01

    This cross-disciplinary special issue on 'Self-organization in magnetic flux ropes' follows in the footsteps of another collection of manuscripts dedicated to the subject of magnetic flux ropes, a volume on 'Physics of magnetic flux ropes' published in the American Geophysical Union's Geophysical Monograph Series in 1990 [1]. Twenty-four years later, this special issue, composed of invited original contributions highlighting ongoing research on the physics of magnetic flux ropes in astrophysical, space and laboratory plasmas, can be considered an update on our state of understanding of this fundamental constituent of any magnetized plasma. Furthermore, by inviting contributions from research groups focused on the study of the origins and properties of magnetic flux ropes in a variety of different environments, we have attempted to underline both the diversity of and the commonalities among magnetic flux ropes throughout the solar system and, indeed, the universe. So, what is a magnetic flux rope? The answer will undoubtedly depend on whom you ask. A flux rope can be as narrow as a few Larmor radii and as wide as the Sun (see, e.g., the contributions by Heli Hietala et al and by Angelous Vourlidas). As described below by Ward Manchester IV et al , they can stretch from the Sun to the Earth in the form of interplanetary coronal mass ejections. Or, as in the Swarthmore Spheromak Experiment described by David Schaffner et al , they can fit into a meter-long laboratory device tended by college students. They can be helical and line-tied (see, e.g., Walter Gekelman et al or J Sears et al ), or toroidal and periodic (see, e.g., John O'Bryan et al or Philippa Browning et al ). They can form in the low plasma beta environment of the solar corona (Tibor Török et al ), the order unity beta plasmas of the solar wind (Stefan Eriksson et al ) and the plasma pressure dominated stellar convection zones (Nicholas Nelson and Mark Miesch). In this special issue, Setthivoine You

  13. Complexity in plasma: From self-organization to geodynamo

    Energy Technology Data Exchange (ETDEWEB)

    Sato, T. [Theory and Computer Simulation Center, National Institute for Fusion Science, Nagoya 464-01 (Japan); the Complexity Simulation Group

    1996-05-01

    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} {ital 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. Self-Organized Criticality in a Random Network Model

    OpenAIRE

    Nirei, Makoto

    1998-01-01

    A new model of self-organized criticality is defined by incorporating a random network model in order to explain endogenous complex fluctuations of economic aggregates. The model can feature many globally interactive systems such as economies or societies.

  16. 用自组织特征映射神经网络对飞行时间质谱采集的大气气溶胶单粒子进行分类%Classification of Atmospheric Individual Aerosol Particles Sampled by Time-of-flight Mass Spectrometry Using Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    郭晓勇; 稳国柱; 黄德双; 方黎; 张为俊

    2014-01-01

    Large amount of data including chemical composition and size information of individual particles would be generated in the measurement of aerosol particles using atmospheric aerosol time-of-flight mass spectrometry ( ATOFMS ) . Our home-made ATOFMS was used to measure the indoor individual aerosol particles in real-time for 24 h, and the obtained mass spectrometric data were clustering analysis by self-organizing map ( SOM ) because of its ability of vector quantization and data dimensionality reduction. 20 classification results were got which included"Calcium-Containing","Salt+Secondary particles","Secondary particles","Organic Amines","K+-Rich Organics" and"Soil" particles, etc. Compared with previous mass spectrometric methods, SOM is a natural visualization tool, more classification results can be obtained. This classification information would be useful to assess the response and toxicity of atmospheric aerosol particles and identify the origin of atmospheric aerosol particles.%气溶胶飞行时间质谱仪( ATOFMS)在对气溶胶粒子的测量过程中,产生大量包含单粒子化学成分和粒径信息的数据。本研究采用具备矢量量化与数据降维能力的自组织特征映射网络( SOM ),对自制的气溶胶飞行时间质谱仪24 h采集到的室内大气气溶胶质谱数据进行聚类分析。获得“含钙”、“盐类和二次气溶胶”、“二次颗粒”、“有机胺”、“富含钾有机物”、“无机盐”和“土壤”等20类颗粒。相比于其它聚类方法,SOM可进行可视化分析,对神经元进行再次聚类,聚类中心多。这些分类信息将有助于评估气溶胶粒子的反应和毒性,以及鉴别气溶胶粒子的起源。

  17. A Mathod of Network Blocking Forecasting about Kohonen Self-Organizing Maps and Radial Basis Function Network%一种利用自组织映射和径向基函数神经网络的网络拥塞预测方法

    Institute of Scientific and Technical Information of China (English)

    葛彦强; 汪向征; 于江德

    2012-01-01

    We propose an adaptive Kohonen Self-Organizing Maps and Radial Basis Function Network-based method (KR) for network blocking forecasting in the paper. It shows that there are some problems in the network blocking forecasting now, especially when the data set is just small. Therefore, for achieving high accuracy in the network blocking forecasting, it is necessary to consider the relationships between each data within the original data set in the forecasting process. Now to get more valuable position information, a series of processes including Kohonen neural network and RBF network is proposed to meet the types of different data. The process makes the network can meet the different kinds of data. In this application to a city's network blocking forecasting, we investigate KR's and two other algorithms performance on a original data set. The comparison of experimental results shows that KR is better location performance than others.%文中提出了一种利用自组织映射(KSOM)和径向基函数(KR)神经网络进行网络拥塞预测的方法.目前的研究表明,预测网络拥塞还存在一些问题,尤其在数据集比较小的时候.因此,为了使网络拥塞问题预测精度高,在预测过程中有必要考虑原有的数据集中每个数据之间的关系.现在为了获得更多的有价值的位置信息,采取了一系列的措施去满足不同数据的情况,包括使用自组织映射神经网络和径向基函数神经网络算法.这一过程使网络能满足不同类型的数据.在本文网络拥塞预测中,采用同一原始数据集,分别对利用自组织映射和径向基函数神经网络的算法和另外两种算法的性能进行比较.实验结果表明,利用自组织映射和径向基函数神经网络的算法具有更好的效果.

  18. Self-organization in cold atomic gases: a synchronization perspective.

    Science.gov (United States)

    Tesio, E; Robb, G R M; Oppo, G-L; Gomes, P M; Ackemann, T; Labeyrie, G; Kaiser, R; Firth, W J

    2014-10-28

    We study non-equilibrium spatial self-organization in cold atomic gases, where long-range spatial order spontaneously emerges from fluctuations in the plane transverse to the propagation axis of a single optical beam. The self-organization process can be interpreted as a synchronization transition in a fully connected network of fictitious oscillators, and described in terms of the Kuramoto model. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  19. 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....

  20. RM-SORN: a reward-modulated self-organizing recurrent neural network.

    Science.gov (United States)

    Aswolinskiy, Witali; Pipa, Gordon

    2015-01-01

    Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

  1. Corporate competition: A self-organized network

    CERN Document Server

    Braha, Dan; Bar-Yam, Yaneer

    2011-01-01

    A substantial number of studies have extended the work on universal properties in physical systems to complex networks in social, biological, and technological systems. In this paper, we present a complex networks perspective on interfirm organizational networks by mapping, analyzing and modeling the spatial structure of a large interfirm competition network across a variety of sectors and industries within the United States. We propose two micro-dynamic models that are able to reproduce empirically observed characteristics of competition networks as a natural outcome of a minimal set of general mechanisms governing the formation of competition networks. Both models, which utilize different approaches yet apply common principles to network formation give comparable results. There is an asymmetry between companies that are considered competitors, and companies that consider others as their competitors. All companies only consider a small number of other companies as competitors; however, there are a few compan...

  2. 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.

  3. Self-organization of atoms coupled to a chiral reservoir

    CERN Document Server

    Eldredge, Zachary; Chang, Darrick; Gorshkov, Alexey V

    2016-01-01

    Tightly confined modes of light, as in optical nanofibers or photonic crystal waveguides, can lead to large optical coupling in atomic systems, which mediates long-range interactions between atoms. These one-dimensional systems can naturally possess couplings that are asymmetric between modes propagating in different directions. Strong long-range interaction among atoms via these modes can drive them to a self-organized periodic distribution. In this paper, we examine the self-organizing behavior of atoms in one dimension coupled to a chiral reservoir. We determine the solution to the equations of motion in different parameter regimes, relative to both the detuning of the pump laser that initializes the atomic dipole-dipole interactions and the degree of reservoir chirality. In addition, we calculate possible experimental signatures such as reflectivity from self-organized atoms and motional sidebands.

  4. Clustering with an Improved Self-Organizing Tree

    Science.gov (United States)

    Suzuki, Yukinori; Sasaki, Yasue

    A self-organizing tree (S-TREE) has a self-organizing capability and better performance than previously reported tree-structured clustering. In the S-TREE algorithm, since a tree grows in greedy fashion, a pruning mechanism is necessary to reduce the effect of bad leaf nodes. Extra nodes are pruned when the tree reaches a predetermined maximum size (U). U is problem-dependent and is therefore difficult to specify beforehand. Furthermore, since U gives the limit of tree growth and also prevents self-organizing of the tree, it may produce unnatural clustering. We are presenting a new pruning algorithm without U. In this paper, we present results showing the performance of the new pruning algorithm using samples generated from normal distributions. The results of computational experiments showed that the new pruning algorithm works well for clustering of those samples.

  5. Emergence of cooperation with self-organized criticality

    CERN Document Server

    Jeong, Hyeong-Chai

    2010-01-01

    Cooperation and self-organized criticality are two main keywords in current studies of evolution. We propose a generalized Bak-Sneppen model and provide a natural mechanism which accounts for both phenomena simultaneously. We use the prisoner's dilemma games to mimic the interactions among the species. Each species is identified by its cooperation probability and its fitness is given by the payoffs from the neighbors. The species with the least payoff is replaced by a new species with a random cooperation probability. When the neighbors of the least fit one are also replaced with a non-zero probability, a strong cooperation emerges. Bak-Sneppen process builds a self-organized structure so that the cooperation can emerge even in the parameter region where a uniform or random population decreases the number of cooperators. The emergence of cooperation is due to the same dynamical correlation which leads to self-organized criticality in replacement activities.

  6. Self-organized structures in soft confined thin films

    Indian Academy of Sciences (India)

    Ashutosh Sharma

    2005-10-01

    We present a mini-review of our recent work on spontaneous, self-organized creation of mesostructures in soft materials like thin films of polymeric liquids and elastic solids. These very small scale, highly confined systems are inherently unstable and thus self-organize into ordered structures which can be exploited for MEMS, sensors, opto-electronic devices and a host of other nanotechnology applications. In particular, mesomechanics requires incorporation of intermolecular interactions and surface tension forces, which are usually inconsequential in classical macroscale mechanics. We point to some experiments and quasi-continuum simulations of self-organized structures in thin soft films which are germane not only to nanotechnology, but also to a spectrum of classical issues such as adhesion/debonding, wetting, coatings, tribology and membranes.

  7. UNSUPERVISED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY BY SELF-ORGANIZING NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    ÁRPÁD BARSI

    2010-06-01

    Full Text Available The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispectral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.

  8. 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.

  9. 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.

  10. 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.

  11. Rapid self-organized criticality: Fractal evolution in extreme environments

    Science.gov (United States)

    Halley, Julianne D.; Warden, Andrew C.; Sadedin, Suzanne; Li, Wentian

    2004-09-01

    We introduce the phenomenon of rapid self-organized criticality (RSOC) and show that, like some models of self-organized criticality (SOC), RSOC generates scale-invariant event distributions and 1/f noise. Unlike SOC, however, RSOC persists despite more than an order of magnitude variation in driving rate and displays extremely thick and dynamic branching geometry. Starting with an initial set of parameter values, we perform two numerical experiments in which nonequilibrium RSOC systems are tuned towards their critical points. The approach to the critical state is tracked using average branching rates, which must equal 1 if systems are genuinely critical.

  12. Variants of guided self-organization for robot control.

    Science.gov (United States)

    Martius, Georg; Herrmann, J Michael

    2012-09-01

    Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.

  13. Macroscopic and microscopic self-organization by nonlocal anisotropic interactions

    CERN Document Server

    Cristiani, Emiliano; Tosin, Andrea

    2009-01-01

    This paper is concerned with mathematical modeling of intelligent systems, such as human crowds and animal groups. In particular, the focus is on the emergence of different self-organized patterns from non-locality and anisotropy of the interactions among individuals. A mathematical technique by time-evolving measures is introduced to deal with both macroscopic and microscopic scales within a unified modeling framework. Then self-organization issues are investigated and numerically reproduced at the proper scale, according to the kind of agents under consideration.

  14. Resource Letter SOP-1: Self-Organizing Physics

    Science.gov (United States)

    Jacobs, Donald T.

    2015-08-01

    This Resource Letter introduces the reader to an area of physics where systems can self-organize to a particular shape or behavior that, while dynamically changing, is surprisingly robust. The self-organization is due to the complex interactions that typically preclude explanation from just the forces among adjacent molecules or objects. How one recognizes such systems and explains their behavior is the topic of this Resource Letter. Some systems exhibit universal behavior that is well documented and understood, but other systems are just now being investigated.

  15. Self-Organized Fission Control for Flocking System

    Directory of Open Access Journals (Sweden)

    Mingyong Liu

    2015-01-01

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

  16. Structure and dynamics in self-organized C60 fullerenes.

    Science.gov (United States)

    Patnaik, Archita

    2007-01-01

    This manuscript on 'structure and dynamics in self-organized C60 fullerenes' has three sections dealing with: (A) pristine C60 aggregate structure and geometry in solvents of varying dielectric constant. Here, using positronium (Ps) as a fundamental probe which maps changes in the local electron density of the microenvironment, the onset concentration for stable C60 aggregate formation and its phase behavior is deduced from the specific interactions of the Ps atom with the surrounding. (B) A novel methanofullerene dyad, based on a hydrophobic (acceptor C60 moiety)-hydrophilic (bridge with benzene and ester functionalities)-hydrophobic (donor didodecyloxybenzene) network is chosen for investigation of characteristic self-assembly it undergoes leading to supramolecular aggregates. The pi-electronic amphiphile, necessitating a critical dielectric constant epsilon > or = 30 in binary THF-water mixtures, dictated the formation of bilayer vesicles as precursors for spherical fractal aggregates upon complete dyad extraction into a more polar water phase. (C) While the molecular orientation is dependent on the packing density, the ordering of the molecular arrangement, indispensable for self-assembly depends on the balance between the structures demanded by inter-molecular and molecule-substrate interactions. The molecular orientation in a monolayer affects the orientation in a multilayer, formed on the monolayer, suggesting the possibility of the latter to act as a template for controlling the structure of the three dimensionally grown self-assembled molecular aggregation. A systematic study on the electronic structure and orientation associated with C60 functionalized aminothiol self-assembled monolayers on Au(111) surface is presented using surface sensitive Ultra-Violet Photoelectron Spectroscopy (UPS) and C-K edge Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy. The results revealed drastic modifications to d-band structure of Au(111) and the

  17. Stigmergy, self-organization, and sorting in collective robotics.

    Science.gov (United States)

    Holland, O; Melhuish, C

    1999-01-01

    Many structures built by social insects are the outcome of a process of self-organization, in which the repeated actions of the insects interact over time with the changing physical environment to produce a characteristic end state. A major mediating factor is stigmergy, the elicitation of specific environment-changing behaviors by the sensory effects of local environmental changes produced by previous behavior. A typical task involving stigmergic self-organization is brood sorting: Many ant species sort their brood so that items at similar stages of development are grouped together and separated from items at different stages of development. This article examines the operation of stigmergy and self-organization in a homogeneous group of physical robots, in the context of the task of clustering and sorting Frisbees of two different types. Using a behavioral rule set simpler than any yet proposed for ant sorting, and having no capacity for spatial orientation or memory, the robots are able to achieve effective clustering and sorting showing all the signs of self-organization. It is argued that the success of this demonstration is crucially dependent on the exploitation of real-world physics, and that the use of simulation alone to investigate stigmergy may fail to reveal its power as an evolutionary option for collective life forms.

  18. 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 the...

  19. Simple model of self-organized biological evolution

    Science.gov (United States)

    de Boer, Jan; Derrida, Bernard; Flyvbjerg, Henrik; Jackson, Andrew D.; Wettig, Tilo

    1994-08-01

    We give an exact solution of a recently proposed self-organized critical model of biological evolution. We show that the model has a power law distribution of durations of coevolutionary ``avalanches'' with a mean field exponent 3/2. We also calculate analytically the finite size effects which cut off this power law at times of the order of the system size.

  20. 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 exampl...

  1. Precipitate coarsening and self organization in erbium-doped silica

    DEFF Research Database (Denmark)

    Sckerl, Mads W.; Guldberg-Kjær, Søren Andreas; Poulsen, Mogens Rysholt

    1999-01-01

    , and formation of erbium-rich. precipitates is seen to occur if the erbium concentration exceeds similar to 0.01 at. %. These precipitates are observed to coarsen and subsequently dissolve with increasing annealing time. Moreover, self organization of precipitates has been observed in the form of layering...

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

    NARCIS (Netherlands)

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

  3. Self-organization and coherent structures in plasmas and fluids

    DEFF Research Database (Denmark)

    Nielsen, A.H.; Juul Rasmussen, J.; Schmidt, M.R.

    1996-01-01

    momentum the development into propagating dipolar structures is observed. This development is discussed by employing self-organization principles. The detailed structures of the evolving dipoles depends on the initial condition. It seems that there are no unique dipolar solutions, but a large class...

  4. Adaptive self-organization of Bali's ancient rice terraces.

    Science.gov (United States)

    Lansing, J Stephen; Thurner, Stefan; Chung, Ning Ning; Coudurier-Curveur, Aurélie; Karakaş, Çağil; Fesenmyer, Kurt A; Chew, Lock Yue

    2017-06-20

    Spatial patterning often occurs in ecosystems as a result of a self-organizing process caused by feedback between organisms and the physical environment. Here, we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmers' decisions and the ecology of the paddies, which triggers a transition from local to global-scale control of water shortages and rice pests. We propose an evolutionary game, based on local farmers' decisions that predicts specific power laws in spatial patterning that are also seen in a multispectral image analysis of Balinese rice terraces. The model shows how feedbacks between human decisions and ecosystem processes can evolve toward an optimal state in which total harvests are maximized and the system approaches Pareto optimality. It helps explain how multiscale cooperation from the community to the watershed scale could persist for centuries, and why the disruption of this self-organizing system by the Green Revolution caused chaos in irrigation and devastating losses from pests. The model shows that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In previous exogenously driven SOC models, adaptation plays no role, and no optimization occurs. In contrast, adaptive SOC is a self-organizing process where local adaptations drive the system toward local and global optima.

  5. Eco-evolutionary feedbacks in self-organized ecosystems

    NARCIS (Netherlands)

    de Jager, M.; 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. Aggregat

  6. Self-Organizing Individual Differences in Brain Development

    Science.gov (United States)

    Lewis, Marc D.

    2005-01-01

    Brain development is self-organizing in that the unique structure of each brain evolves in unpredictable ways through recursive modifications of synaptic networks. In this article, I review mechanisms of neural change in real time and over development, and I argue that change at each of these time scales embodies principles of self-organizing…

  7. THEORETICAL BASES OF PEDAGOGICAL MAINTENANCE OF SCHOOL STUDENTS’ SELF- ORGANIZATION

    Directory of Open Access Journals (Sweden)

    Komova O. V.

    2015-12-01

    Full Text Available The theoretical elements of pedagogical maintenance of school students’ self-organization are considered in the article, as new forms of organization of educational process. We research the problem of pedagogical maintenance in psychological and pedagogical literature. There is a definition of this concept. The author thinks that the process of quality’s improvement of school students’ independent activity and their selforganization is not good developed. It is necessary to investigate this process. The problem of school students’ self-organization is described in pedagogic. There is a structure of a motivational and self - organizational basis of educational activity. This structure consists of certain stages. The first, it is a concentration of attention on an educational situation. The second, it is a pupils’ orientation in activity. The third, it has to define the purpose. The fourth, these are the ways to achievement of the purpose (performance of educational actions. Then it is a control and correction of educational actions. The last, it is an assessment (self-assessment of the received result. The pedagogical maintenance of self - organization and elements of the chosen structure makes the main contents of research in system of additional education. The author allocates levels of management of selforganization of school students. There is a definition of pedagogical maintenance of self-organization of school students. There is a conclusion that mastering skills of self-organization and self-control it not only pledge of a successful organization of educational activity, but also successful existence and selfrealization in modern society

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

    Science.gov (United States)

    Yang, Hui; Liu, Gang

    2013-12-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hui, E-mail: huiyang@usf.edu; Liu, Gang [Complex Systems Monitoring, Modeling and Analysis Laboratory, University of South Florida, Tampa, Florida 33620 (United States)

    2013-12-15

    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.

  10. Self-Organization of Blood Pressure Regulation: Experimental Evidence

    Science.gov (United States)

    Fortrat, Jacques-Olivier; Levrard, Thibaud; Courcinous, Sandrine; Victor, Jacques

    2016-01-01

    Blood pressure regulation is a prime example of homeostatic regulation. However, some characteristics of the cardiovascular system better match a non-linear self-organized system than a homeostatic one. To determine whether blood pressure regulation is self-organized, we repeated the seminal demonstration of self-organized control of movement, but applied it to the cardiovascular system. We looked for two distinctive features peculiar to self-organization: non-equilibrium phase transitions and hysteresis in their occurrence when the system is challenged. We challenged the cardiovascular system by means of slow, 20-min Tilt-Up and Tilt-Down tilt table tests in random order. We continuously determined the phase between oscillations at the breathing frequency of Total Peripheral Resistances and Heart Rate Variability by means of cross-spectral analysis. We looked for a significant phase drift during these procedures, which signed a non-equilibrium phase transition. We determined at which head-up tilt angle it occurred. We checked that this angle was significantly different between Tilt-Up and Tilt-Down to demonstrate hysteresis. We observed a significant non-equilibrium phase transition in nine healthy volunteers out of 11 with significant hysteresis (48.1 ± 7.5° and 21.8 ± 3.9° during Tilt-Up and Tilt-Down, respectively, p < 0.05). Our study shows experimental evidence of self-organized short-term blood pressure regulation. It provides new insights into blood pressure regulation and its related disorders. PMID:27065880

  11. Dynamic self-organization of microwell-aggregated cellular mixtures.

    Science.gov (United States)

    Song, Wei; Tung, Chih-Kuan; Lu, Yen-Chun; Pardo, Yehudah; Wu, Mingming; Das, Moumita; Kao, Der-I; Chen, Shuibing; Ma, Minglin

    2016-06-29

    Cells with different cohesive properties self-assemble in a spatiotemporal and context-dependent manner. Previous studies on cell self-organization mainly focused on the spontaneous structural development within a short period of time during which the cell numbers remained constant. However the effect of cell proliferation over time on the self-organization of cells is largely unexplored. Here, we studied the spatiotemporal dynamics of self-organization of a co-culture of MDA-MB-231 and MCF10A cells seeded in a well defined space (i.e. non-adherent microfabricated wells). When cell-growth was chemically inhibited, high cohesive MCF10A cells formed a core surrounded by low cohesive MDA-MB-231 cells on the periphery, consistent with the differential adhesion hypothesis (DAH). Interestingly, this aggregate morphology was completely inverted when the cells were free to grow. At an initial seeding ratio of 1 : 1 (MDA-MB-231 : MCF10A), the fast growing MCF10A cells segregated in the periphery while the slow growing MDA-MB-231 cells stayed in the core. Another morphology developed at an inequal seeding ratio (4 : 1), that is, the cell mixtures developed a side-by-side aggregate morphology. We conclude that the cell self-organization depends not only on the cell cohesive properties but also on the cell seeding ratio and proliferation. Furthermore, by taking advantage of the cell self-organization, we purified human embryonic stem cells-derived pancreatic progenitors (hESCs-PPs) from co-cultured feeder cells without using any additional tools or labels.

  12. Supervision as Metaphor

    Science.gov (United States)

    Lee, Alison; Green, Bill

    2009-01-01

    This article takes up the question of the language within which discussion of research degree supervision is couched and framed, and the consequences of such framings for supervision as a field of pedagogical practice. It examines the proliferation and intensity of metaphor, allegory and allusion in the language of candidature and supervision,…

  13. A Supervision of Solidarity

    Science.gov (United States)

    Reynolds, Vikki

    2010-01-01

    This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

  14. Variability of surfer circulation and Kuroshio intrusion in northern South China Sea using growing hierarchical self-organizing maps%应用GHSOM网络分析南海北部表层环流模态与黑潮入侵

    Institute of Scientific and Technical Information of China (English)

    徐晓华; 廖光洪; 杨成浩; 袁耀初; 黄韦艮

    2013-01-01

    基于1992年10月至2009年11月卫星观测的海表高度(SSH)时间序列数据,应用增长型分级自组织映射(GHSOM)人工神经网络方法研究南海北部和西太平洋 SSH和中尺度涡旋的变化,识别出该海域 SSH的季节和年际变化信号。分析表明,流经吕宋海峡的黑潮分支在冷季入侵南海北部,同时在吕宋岛西北海域出现一个强烈的气旋式涡旋,表层黑潮的入侵与跨过吕宋海峡南北的经向压力梯度密切相关。黑潮的非入侵事件主要出现在暖季。春秋季节作为两个事件的过渡期,环流结构复杂,由 GHSOM的第2层特征图进一步进行分类识别。黑潮入侵事件和非入侵事件发生的百分比分别为24.57%和27.53%,过渡模态的百分比为47.87%。当入侵南海事件发生时,南海北部表层环流流态相对简单,主要为气旋环流控制南海北部,吕宋海峡表层海流是否入侵南海,与南海北部中尺度涡旋特别是吕宋岛西北的气旋式涡的变化关系密切;反之,在非入侵事件发生时,南海北部出现多涡结构,环流流态复杂,表明吕宋海峡海流入侵南海对南海北部环流也有重要调整作用。除季节尺度变化外,年际时间尺度变化信号也十分显著。在1994-1995、1997-1998和2002-2003年期间,表层黑潮入侵南海北部的事件要显著多于其他年份,然而入侵事件在1998-2001年和2006-2009年时间段明显减少,非入侵事件增加。应用欧氏距离定义的模态2的时间发展序列与Niño3.4指数序列延迟相关。%Sea surface height (SSH) variations and eddies on both sides of the Luzon Strait are examined from the merged satellite altimeter data from October 1992 to November 2009. The neural network analyses based on the growing hierarchical self-organizing map (GHSOM) are used to extract feature patterns of the circulation variability. The evolution of the characteristic circulation patterns

  15. DSOM: a novel self-organizing model based on NO dynamic diffusing mechanism

    Institute of Scientific and Technical Information of China (English)

    YIN Junsong; HU Dewen; CHEN Shuang; ZHOU Zongtan

    2005-01-01

    In this paper the four-dimensional dynamic diffusing mechanism and the enhancement in Long-Term Potentiation (LTP) of intrinsic nitric oxide (NO) in nervous system are studied computationally. A novel unsupervised Diffusing Self-Organizing Maps (DSOM) model is presented on the union of SOM with NO diffusing mechanism. Based on the spatial prototype mapping, temporal enhancement is introduced in DSOM and the fine-tuning manner is improved by the simplified NO diffusing mechanism. Furthermore, the quantization error of optimal weights is valuated and the detailed noise analysis of DSOM is presented. Finally some typical stimulation experiments are presented to illustrate how DSOM gracefully handles time warping and multiple patterns with overlapping reference vectors.

  16. Good supervision and PBL

    DEFF Research Database (Denmark)

    Otrel-Cass, Kathrin

    This field study was conducted at the Faculty of Social Sciences at Aalborg University with the intention to investigate how students reflect on their experiences with supervision in a PBL environment. The overall aim of this study was to inform about the continued work in strengthening supervision...... at this faculty. This particular study invited Master level students to discuss: • How a typical supervision process proceeds • How they experienced and what they expected of PBL in the supervision process • What makes a good supervision process...

  17. Self-organization of gold nanoparticles on silanated surfaces

    Directory of Open Access Journals (Sweden)

    Htet H. Kyaw

    2015-12-01

    Full Text Available The self-organization of monolayer gold nanoparticles (AuNPs on 3-aminopropyltriethoxysilane (APTES-functionalized glass substrate is reported. The orientation of APTES molecules on glass substrates plays an important role in the interaction between AuNPs and APTES molecules on the glass substrates. Different orientations of APTES affect the self-organization of AuNps on APTES-functionalized glass substrates. The as grown monolayers and films annealed in ultrahigh vacuum and air (600 °C were studied by water contact angle measurements, atomic force microscopy, X-ray photoelectron spectroscopy, UV–visible spectroscopy and ultraviolet photoelectron spectroscopy. Results of this study are fundamentally important and also can be applied for designing and modelling of surface plasmon resonance based sensor applications.

  18. Self-Organizing OFDMA System for Broadband Communication

    Science.gov (United States)

    Roy, Aloke (Inventor); Anandappan, Thanga (Inventor); Malve, Sharath Babu (Inventor)

    2016-01-01

    Systems and methods for a self-organizing OFDMA system for broadband communication are provided. In certain embodiments a communication node for a self organizing network comprises a communication interface configured to transmit data to and receive data from a plurality of nodes; and a processing unit configured to execute computer readable instructions. Further, computer readable instructions direct the processing unit to identify a sub-region within a cell, wherein the communication node is located in the sub-region; and transmit at least one data frame, wherein the data from the communication node is transmitted at a particular time and frequency as defined within the at least one data frame, where the time and frequency are associated with the sub-region.

  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-organized Criticality Behavior in Bulk Metallic Glasses

    Institute of Scientific and Technical Information of China (English)

    Jun-wei QIAO; Zhong WANG

    2016-01-01

    Serrated flows are known as repeated yielding of bulk metallic glasses (BMGs)during plastic deformation under different loading conditions,which are associated with the operation of shear banding.According to the statis-tics of some parameters,the shear avalanches can display a self-organized critical state,suggesting a large ductility of BMGs.The emergence of the self-organized criticality (SOC)behavior in different BMGs is due to the tempera-ture,strain rate,and chemical compositions.The SOC behavior is accompanied with the following phenomena:the interactions occur in the shear bands;the incubation time is longer than the relaxation time;the time interval is lac-king of typical time scale;and the spatial or temporal parameters should display a power-law distribution.

  1. 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.

  2. 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.

  3. 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.

  4. Analytical investigation of self-organized criticality in neural networks.

    Science.gov (United States)

    Droste, Felix; Do, Anne-Ly; Gross, Thilo

    2013-01-06

    Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.

  5. Can dynamical synapses produce true self-organized criticality?

    Science.gov (United States)

    Costa, Ariadne de Andrade; Copelli, Mauro; Kinouchi, Osame

    2015-06-01

    Neuronal networks can present activity described by power-law distributed avalanches presumed to be a signature of a critical state. Here we study a random-neighbor network of excitable cellular automata coupled by dynamical synapses. The model exhibits a very similar to conservative self-organized criticality (SOC) models behavior even with dissipative bulk dynamics. This occurs because in the stationary regime the model is conservative on average, and, in the thermodynamic limit, the probability distribution for the global branching ratio converges to a delta-function centered at its critical value. So, this non-conservative model pertain to the same universality class of conservative SOC models and contrasts with other dynamical synapses models that present only self-organized quasi-criticality (SOqC). Analytical results show very good agreement with simulations of the model and enable us to study the emergence of SOC as a function of the parametric derivatives of the stationary branching ratio.

  6. Developing neuronal networks: self-organized criticality predicts the future.

    Science.gov (United States)

    Pu, Jiangbo; Gong, Hui; Li, Xiangning; Luo, Qingming

    2013-01-01

    Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and "aging" process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

  7. 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.

  8. Self-organization of gold nanoparticles on silanated surfaces.

    Science.gov (United States)

    Kyaw, Htet H; Al-Harthi, Salim H; Sellai, Azzouz; Dutta, Joydeep

    2015-01-01

    The self-organization of monolayer gold nanoparticles (AuNPs) on 3-aminopropyltriethoxysilane (APTES)-functionalized glass substrate is reported. The orientation of APTES molecules on glass substrates plays an important role in the interaction between AuNPs and APTES molecules on the glass substrates. Different orientations of APTES affect the self-organization of AuNps on APTES-functionalized glass substrates. The as grown monolayers and films annealed in ultrahigh vacuum and air (600 °C) were studied by water contact angle measurements, atomic force microscopy, X-ray photoelectron spectroscopy, UV-visible spectroscopy and ultraviolet photoelectron spectroscopy. Results of this study are fundamentally important and also can be applied for designing and modelling of surface plasmon resonance based sensor applications.

  9. Exploiting Self-organization in Bioengineered Systems: A Computational Approach.

    Science.gov (United States)

    Davis, Delin; Doloman, Anna; Podgorski, Gregory J; Vargis, Elizabeth; Flann, Nicholas S

    2017-01-01

    The productivity of bioengineered cell factories is limited by inefficiencies in nutrient delivery and waste and product removal. Current solution approaches explore changes in the physical configurations of the bioreactors. This work investigates the possibilities of exploiting self-organizing vascular networks to support producer cells within the factory. A computational model simulates de novo vascular development of endothelial-like cells and the resultant network functioning to deliver nutrients and extract product and waste from the cell culture. Microbial factories with vascular networks are evaluated for their scalability, robustness, and productivity compared to the cell factories without a vascular network. Initial studies demonstrate that at least an order of magnitude increase in production is possible, the system can be scaled up, and the self-organization of an efficient vascular network is robust. The work suggests that bioengineered multicellularity may offer efficiency improvements difficult to achieve with physical engineering approaches.

  10. Self-Organization and Forces in the Mitotic Spindle.

    Science.gov (United States)

    Pavin, Nenad; Tolić, Iva M

    2016-07-05

    At the onset of division, the cell forms a spindle, a precise self-constructed micromachine composed of microtubules and the associated proteins, which divides the chromosomes between the two nascent daughter cells. The spindle arises from self-organization of microtubules and chromosomes, whose different types of motion help them explore the space and eventually approach and interact with each other. Once the interactions between the chromosomes and the microtubules have been established, the chromosomes are moved to the equatorial plane of the spindle and ultimately toward the opposite spindle poles. These transport processes rely on directed forces that are precisely regulated in space and time. In this review, we discuss how microtubule dynamics and their rotational movement drive spindle self-organization, as well as how the forces acting in the spindle are generated, balanced, and regulated.

  11. Universal Quantification for Self-Organized Criticality in Atmospheric Flows

    CERN Document Server

    Selvam, A M

    1997-01-01

    Atmospheric flows exhibit selfsimilar fluctuations on all scales(space-time) ranging from climate(kilometers/years) to turbulence(millimeters/seconds) manifested as fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations. Selfsimilar fluctuations implying long-range correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality in atmospheric flows. Also, mathematical models for simulation and prediction of atmospheric flows are nonlinear and computer realizations give unrealistic solutions because of deterministic chaos, a direct consequence of finite precision round-off error doubling for each iteration of iterative computations incorporated in long-term numerical integration schemes used for model solutions An alternative non-deterministic cell dynamical system model predicts, (a): the observed self organized criticality as a consequence of quantumlike mechanics governing...

  12. Secure steganographic communication algorithm based on self-organizing patterns

    Science.gov (United States)

    Saunoriene, Loreta; Ragulskis, Minvydas

    2011-11-01

    A secure steganographic communication algorithm based on patterns evolving in a Beddington-de Angelis-type predator-prey model with self- and cross-diffusion is proposed in this paper. Small perturbations of initial states of the system around the state of equilibrium result in the evolution of self-organizing patterns. Small differences between initial perturbations result in slight differences also in the evolving patterns. It is shown that the generation of interpretable target patterns cannot be considered as a secure mean of communication because contours of the secret image can be retrieved from the cover image using statistical techniques if only it represents small perturbations of the initial states of the system. An alternative approach when the cover image represents the self-organizing pattern that has evolved from initial states perturbed using the dot-skeleton representation of the secret image can be considered as a safe visual communication technique protecting both the secret image and communicating parties.

  13. 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.

  14. THEORETICAL BASES OF PEDAGOGICAL MAINTENANCE OF SCHOOL STUDENTS’ SELF- ORGANIZATION

    OpenAIRE

    Komova O. V.

    2015-01-01

    The theoretical elements of pedagogical maintenance of school students’ self-organization are considered in the article, as new forms of organization of educational process. We research the problem of pedagogical maintenance in psychological and pedagogical literature. There is a definition of this concept. The author thinks that the process of quality’s improvement of school students’ independent activity and their selforganization is not good developed. It is necessary to investigate this p...

  15. Characterizing self-organization and coevolution by ergodic invariants

    CERN Document Server

    Mendes, R V

    1999-01-01

    In addition to the emergent complexity of patterns that appears when many agents come in interaction, it is also useful to characterize the dynamical processes that lead to their self-organization. A set of ergodic invariants is identified for this purpose, which is computed in several examples, namely a Bernoulli network with either global or nearest-neighbor coupling, a generalized Bak-Sneppen model and a continuous minority model.

  16. Digitally Printed Dewetting Patterns for Self-Organized Microelectronics.

    Science.gov (United States)

    Eckstein, Ralph; Alt, Milan; Rödlmeier, Tobias; Scharfer, Philip; Lemmer, Uli; Hernandez-Sosa, Gerardo

    2016-09-01

    Self-organization of functional materials induced by low surface-energetic direct printed structures is presented. This study investigates fundamental fluid and substrate interactions and fabricates all-printed small area organic photodetectors with On-Off ratios of ≈10(5) and dark current densities of ≈10(-4) mA cm(-2) , as well as ring oscillators based on n-type organic field-effect transistors showing working frequencies up to 400 Hz.

  17. Simple model of self-organized biological evolution

    Energy Technology Data Exchange (ETDEWEB)

    de Boer, J.; Derrida, B.; Flyvbjerg, H.; Jackson, A.D.; Wettig, T. (Department of Physics, State University of New York at Stony Brook, Stony Brook, New York 11794-3800 (United States) The Isaac Newton Institute for Mathematical Sciences, 20 Clarkson Road, Cambridge, CB4 0EH (United Kingdom) Laboratoire de Physique Statistique, Ecole Normale Superieure, 24 rue Lhomond, F-75005 Paris (France) Service de Physique Theorique, Centre de Etudes Nucleaires de Saclay, F-91191, Gif-Sur-Yvette (France) CONNECT, The Niels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen (Denmark))

    1994-08-08

    We give an exact solution of a recently proposed self-organized critical model of biological evolution. We show that the model has a power law distribution of durations of coevolutionary avalanches'' with a mean field exponent 3/2. We also calculate analytically the finite size effects which cut off this power law at times of the order of the system size.

  18. Thermosolutal self-organization of supramolecular polymers into nanocraters.

    Science.gov (United States)

    Marangoni, Tomas; Mezzasalma, Stefano A; Llanes-Pallas, Anna; Yoosaf, K; Armaroli, Nicola; Bonifazi, Davide

    2011-02-15

    The ability of two complementary molecular modules bearing H-bonding uracilic and 2,6-(diacetylamino)pyridyl moieties to self-assemble and self-organize into submicrometer morphologies has been investigated by means of spectroscopic, thermogravimetric, and microscopic methods. Using uracilic (3)N-BOC-protected modules, it has been possible to thermally trigger the self-assembly/self-organization process of the two molecular modules, inducing the formation of objects on a mica surface that exhibit crater-like morphology and a very homogeneous size distribution. Confirmation of the presence of the hydrogen-bonding-driven self-assembly/self-organization process in solution was obtained by variable-temperature (VT) steady-state UV-vis absorption and emission measurements. The variation of the geometric and spatial features of the morphologies was monitored at different T by means of atomic force microscopy (AFM) and was interpreted by a nonequilibrium diffusion model for two chemical species in solution. The formation of nanostructures turned out to be affected by the solid substrate (molecular interactions at a solid-liquid interface), by the matter-momentum transport in solution (solute diffusivity D(0) and solvent kinematic viscosity ν), and the thermally dependent cleavage reaction of the BOC functions (T-dependent differential weight loss, θ = θ(Τ)) in a T interval extrapolated to ∼60 K. A scaling function, f = f (νD(0), ν/D(0), θ), relying on the onset condition of a concentration-driven thermosolutal instability has been established to simulate the T-dependent behavior of the structural dimension (i.e., height and radius) of the self-organized nanostructures as ⟨h⟩ ≈ f (T) and ⟨r⟩ ≈ 1/f (T).

  19. 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

  20. Quantitative analysis of cellular metabolic dissipative, self-organized structures

    OpenAIRE

    Ildefonso Martínez de la Fuente

    2010-01-01

    One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the ...

  1. 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.

  2. Self-Organized Collective Displacements of Self-Driven Individuals

    Science.gov (United States)

    Albano, Ezequiel V.

    1996-09-01

    An archetype model for the collective displacements of self-driven individuals, aimed to describe the dynamic of flocking behavior among living things, is presented and studied. Processes such as growth, death, survival, self-propagation, and competition are considered. It is shown that systems ruled by the model self-organize into a critical state exhibiting power-law behavior in both the distribution of population avalanches and the spatial correlation between individuals.

  3. Subharmonic instability of a self-organized granular jet.

    Science.gov (United States)

    Kollmer, J E; Pöschel, T

    2016-03-22

    Downhill flows of granular matter colliding in the lowest point of a valley, may induce a self-organized jet. By means of a quasi two-dimensional experiment where fine grained sand flows in a vertically sinusoidally agitated cylinder, we show that the emergent jet, that is, a sheet of ejecta, does not follow the frequency of agitation but reveals subharmonic response. The order of the subharmonics is a complex function of the parameters of driving.

  4. Sonification of a Network's Self-Organized Criticality

    OpenAIRE

    Vickers, Paul; Laing, Chris; Fairfax, Tom

    2014-01-01

    Communication networks involve the transmission and reception of large volumes of data. Research indicates that network traffic volumes will continue to increase. These traffic volumes will be unprecedented and the behaviour of global information infrastructures when dealing with these data volumes is unknown. It has been shown that complex systems (including computer networks) exhibit self-organized criticality under certain conditions. Given the possibility in such systems of a sudden and s...

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

    Science.gov (United States)

    2011-05-01

    of needs ( Maslow 1943). At the base of the hierarchy are the physiological needs ; these are the most primitive needs for all organisms based on self...motivation hierarchy is self-actualization. Maslow describes this motivation as a person achieving potential ( Maslow 1943). Satisfaction of needs at any...show that they are necessary for self-organization to occur. Common Purpose Abraham Maslow proposed a theory on human motivation based on a hierarchy

  6. Scaling and self-organized criticality in proteins II

    OpenAIRE

    2009-01-01

    The complexity of proteins is substantially simplified by regarding them as archetypical examples of self-organized criticality (SOC). To test this idea and to elaborate it, this article applies the Moret–Zebende (MZ) SOC hydrophobicity scale to transport repeat proteins of the HEAT superfamily, importin β, and transportin, as well as the export protein Cse1p, and their ubiquitous cargo manager Ran. The difference between the MZ scale and conventional hydrophobicity scales reflects long-range...

  7. Self-organizing Complex Networks: individual versus global rules

    Science.gov (United States)

    Mahmoodi, Korosh; West, Bruce J.; Grigolini, Paolo

    2017-01-01

    We introduce a form of Self-Organized Criticality (SOC) inspired by the new generation of evolutionary game theory, which ranges from physiology to sociology. The single individuals are the nodes of a composite network, equivalent to two interacting subnetworks, one leading to strategy choices made by the individuals under the influence of the choices of their nearest neighbors and the other measuring the Prisoner's Dilemma Game payoffs of these choices. The interaction between the two networks is established by making the imitation strength K increase or decrease according to whether the last two payoffs increase or decrease upon increasing or decreasing K. Although each of these imitation strengths is selected selfishly, and independently of the others as well, the social system spontaneously evolves toward the state of cooperation. Criticality is signaled by temporal complexity, namely the occurrence of non-Poisson renewal events, the time intervals between two consecutive crucial events being given by an inverse power law index μ = 1.3 rather than by avalanches with an inverse power law distribution as in the original form of SOC. This new phenomenon is herein labeled self-organized temporal criticality (SOTC). We compare this bottom-up self-organization process to the adoption of a global choice rule based on assigning to all the units the same value K, with the time evolution of common K being determined by consciousness of the social benefit, a top-down process implying the action of a leader. In this case self-organization is impeded by large intensity fluctuations and the global social benefit turns out to be much weaker. We conclude that the SOTC model fits the requests of a manifesto recently proposed by a number of European social scientists. PMID:28736534

  8. 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....

  9. Mechanical models for the self-organization of tubular patterns.

    Science.gov (United States)

    Guo, Chin-Lin

    2013-01-01

    Organogenesis, such as long tubule self-organization, requires long-range coordination of cell mechanics to arrange cell positions and to remodel the extracellular matrix. While the current mainstream in the field of tissue morphogenesis focuses primarily on genetics and chemical signaling, the influence of cell mechanics on the programming of patterning cues in tissue morphogenesis has not been adequately addressed. Here, we review experimental evidence and propose quantitative mechanical models by which cells can create tubular patterns.

  10. Dynamic Self-Organization and Early Lexical Development in Children

    Science.gov (United States)

    Li, Ping; Zhao, Xiaowei; Whinney, Brian Mac

    2007-01-01

    In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition,…

  11. On the Gompertzian dynamics of growth and self-organization

    OpenAIRE

    Molski, Marcin; Konarski, Jerzy

    2007-01-01

    Comment on the Waliszewski's article "A principle of fractal-sto-chastic dualism and Gompertzian dynamics of growth and self-organization" (BioSystems 82 (2005)61-73) is presented. It has been proved that the main idea of this work that Gompertzian dynamics is governed by the Schr\\"{o}dinger-like equation including anharmonic Morse potential has been already introduced by Molski and Konarski in 2003. Some inconsistencies and mathematical errors in the Waliszewski's model are also pointed out.

  12. Self-organization of functional materials in confinement.

    Science.gov (United States)

    Gentili, Denis; Valle, Francesco; Albonetti, Cristiano; Liscio, Fabiola; Cavallini, Massimiliano

    2014-08-19

    This Account aims to describe our experience in the use of patterning techniques for addressing the self-organization processes of materials into spatially confined regions on technologically relevant surfaces. Functional properties of materials depend on their chemical structure, their assembly, and spatial distribution at the solid state; the combination of these factors determines their properties and their technological applications. In fact, by controlling the assembly processes and the spatial distribution of the resulting structures, functional materials can be guided to technological and specific applications. We considered the principal self-organizing processes, such as crystallization, dewetting and phase segregation. Usually, these phenomena produce defective molecular films, compromising their use in many technological applications. This issue can be overcome by using patterning techniques, which induce molecules to self-organize into well-defined patterned structures, by means of spatial confinement. In particular, we focus our attention on the confinement effect achieved by stamp-assisted deposition for controlling size, density, and positions of material assemblies, giving them new chemical/physical functionalities. We review the methods and principles of the stamp-assisted spatial confinement and we discuss how they can be advantageously exploited to control crystalline order/orientation, dewetting phenomena, and spontaneous phase segregation. Moreover, we highlight how physical/chemical properties of soluble functional materials can be driven in constructive ways, by integrating them into operating technological devices.

  13. 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.

  14. Cellular self-organization by autocatalytic alignment feedback

    Science.gov (United States)

    Junkin, Michael; Leung, Siu Ling; Whitman, Samantha; Gregorio, Carol C.; Wong, Pak Kin

    2011-01-01

    Myoblasts aggregate, differentiate and fuse to form skeletal muscle during both embryogenesis and tissue regeneration. For proper muscle function, long-range self-organization of myoblasts is required to create organized muscle architecture globally aligned to neighboring tissue. However, how the cells process geometric information over distances considerably longer than individual cells to self-organize into well-ordered, aligned and multinucleated myofibers remains a central question in developmental biology and regenerative medicine. Using plasma lithography micropatterning to create spatial cues for cell guidance, we show a physical mechanism by which orientation information can propagate for a long distance from a geometric boundary to guide development of muscle tissue. This long-range alignment occurs only in differentiating myoblasts, but not in non-fusing myoblasts perturbed by microfluidic disturbances or other non-fusing cell types. Computational cellular automata analysis of the spatiotemporal evolution of the self-organization process reveals that myogenic fusion in conjunction with rotational inertia functions in a self-reinforcing manner to enhance long-range propagation of alignment information. With this autocatalytic alignment feedback, well-ordered alignment of muscle could reinforce existing orientations and help promote proper arrangement with neighboring tissue and overall organization. Such physical self-enhancement might represent a fundamental mechanism for long-range pattern formation during tissue morphogenesis. PMID:22193956

  15. 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.

  16. Modeling self-organizing traffic lights with elementary cellular automata

    CERN Document Server

    Gershenson, Carlos

    2009-01-01

    There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions. We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high dens...

  17. 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

  18. A self-organized internal models architecture for coding sensory-motor schemes

    Directory of Open Access Journals (Sweden)

    Esaú eEscobar Juárez

    2016-04-01

    Full Text Available Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that textit{simulation}, textit{prediction}, and textit{multi-modal integration} are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity.Research in this direction has brought extensive empirical evidencesuggesting that textit{Internal Models} are suitable mechanisms forsensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints.We propose the Self-Organized Internal ModelsArchitecture (SOIMA, a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory-motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models.We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

  19. 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.

  20. A Design Approach for Controlled Self-Organization-Based Sensor Networks Focused on Control Timescale

    OpenAIRE

    2013-01-01

    Many researches on network control with a design principle of self-organization have been studied for large-scale networks. Since self-organized control is based on local interactions between system elements, it has high scalability, adaptability, and robustness; however, the management of the whole system is very difficult. In order to solve this problem, a controlled self-organization scheme has been proposed, which aims for desired system behavior by controlling a part of self-organized no...

  1. Effect of Correlations on the Exponents for the Power-Law Distributions in Self-Organized Criticality

    Institute of Scientific and Technical Information of China (English)

    邓永菊; 郑华; 杨纯斌

    2012-01-01

    The origin of power-law distributions in self-organized criticality is investigated by treating the variation of the number of active sites in the system as a stochastic process. An avalanche is mapped to a first-return random- walk process in a one-dimensional lattice. In order to understand the reason of variant exponents for the power-law distributions in different self-organized critical systems, we introduce the correlations among evolution steps. Power-law distributions of the lifetime and spatial size are found when the random walk is unbiased with equal probability to move in opposite directions. It is found that the longer the correlation length, the smaller values of the exponents for the power-law distributions.

  2. Growth and self-organization of SiGe nanostructures

    Energy Technology Data Exchange (ETDEWEB)

    Aqua, J.-N., E-mail: aqua@insp.jussieu.fr [Institut des Nanosciences de Paris, Université Pierre et Marie Curie Paris 6 and CNRS UMR 7588, 4 place Jussieu, 75252 Paris (France); Berbezier, I., E-mail: isabelle.berbezier@im2np.fr [Institut Matériaux Microélectronique Nanoscience de Provence, Aix-Marseille Université, UMR CNRS 6242, 13997 Marseille (France); Favre, L. [Institut Matériaux Microélectronique Nanoscience de Provence, Aix-Marseille Université, UMR CNRS 6242, 13997 Marseille (France); Frisch, T. [Institut Non Linéaire de Nice, Université de Nice Sophia Antipolis, UMR CNRS 6618, 1361 routes des Lucioles, 06560 Valbonne (France); Ronda, A. [Institut Matériaux Microélectronique Nanoscience de Provence, Aix-Marseille Université, UMR CNRS 6242, 13997 Marseille (France)

    2013-01-01

    Many recent advances in microelectronics would not have been possible without the development of strain induced nanodevices and bandgap engineering, in particular concerning the common SiGe system. In this context, a huge amount of literature has been devoted to the growth and self-organization of strained nanostructures. However, even if an overall picture has been drawn out, the confrontation between theories and experiments is still, under various aspects, not fully satisfactory. The objective of this review is to present a state-of-the-art of theoretical concepts and experimental results on the spontaneous formation and self-organization of SiGe quantum dots on silicon substrates. The goal is to give a comprehensive overview of the main experimental results on the growth and long time evolution of these dots together with their morphological, structural and compositional properties. We also aim at describing the basis of the commonly used thermodynamic and kinetic models and their recent refinements. The review covers the thermodynamic theory for different levels of elastic strain, but focuses also on the growth dynamics of SiGe quantum dots in several experimental circumstances. The strain driven kinetically promoted instability, which is the main form of instability encountered in the epitaxy of SiGe nanostructures at low strain, is described. Recent developments on its continuum description based on a non-linear analysis particularly useful for studying self-organization and coarsening are described together with other theoretical frameworks. The kinetic evolution of the elastic relaxation, island morphology and film composition are also extensively addressed. Theoretical issues concerning the formation of ordered island arrays on a pre-patterned substrate, which is governed both by equilibrium ordering and kinetically-controlled ordering, are also reported in connection with the experimental results for the fabrication technology of ordered arrays of Si

  3. 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

  4. Self-Organized Topological State with Majorana Fermions

    Science.gov (United States)

    Vazifeh, M. M.; Franz, M.

    2013-11-01

    Most physical systems known to date tend to resist entering the topological phase and must be fine-tuned to reach that phase. Here, we introduce a system in which a key dynamical parameter adjusts itself in response to the changing external conditions so that the ground state naturally favors the topological phase. The system consists of a quantum wire formed of individual magnetic atoms placed on the surface of an ordinary s-wave superconductor. It realizes the Kitaev paradigm of topological superconductivity when the wave vector characterizing the emergent spin helix dynamically self-tunes to support the topological phase. We call this phenomenon a self-organized topological state.

  5. Scaling and Regeneration of Self-Organized Patterns

    Science.gov (United States)

    Werner, Steffen; Stückemann, Tom; Beirán Amigo, Manuel; Rink, Jochen C.; Jülicher, Frank; Friedrich, Benjamin M.

    2015-04-01

    Biological patterns generated during development and regeneration often scale with organism size. Some organisms, e.g., flatworms, can regenerate a rescaled body plan from tissue fragments of varying sizes. Inspired by these examples, we introduce a generalization of Turing patterns that is self-organized and self-scaling. A feedback loop involving diffusing expander molecules regulates the reaction rates of a Turing system, thereby adjusting pattern length scales proportional to system size. Our model captures essential features of body plan regeneration in flatworms as observed in experiments.

  6. Self-organization in collective behaviour of active nanoparticles

    Directory of Open Access Journals (Sweden)

    A.Sh. Baranova

    2010-01-01

    Full Text Available The self-organization of the set of active nanoparticles was self-consistently described on the basis of Lorentz's three-parametrical system in frameworks of the phenomenological scheme. The continuous and discontinuous types of transition from a rotary movement mode to the forward were considered. The fluctuation’s influence on transition is investigated and diagrammed of possible modes of active nanoparticles group behavior are constructed. The kinetics of transition between rotary and forward movement types for different correlations between characteristic times of system’s key parameters was analyzed on the basis of phase portraits.

  7. Self-organized Criticality Model for Ocean Internal Waves

    Institute of Scientific and Technical Information of China (English)

    WANG Gang; LIN Min; QIAO Fang-Li; HOU Yi-Jun

    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.

  8. Self-organization, embodiment, and biologically inspired robotics.

    Science.gov (United States)

    Pfeifer, Rolf; Lungarella, Max; Iida, Fumiya

    2007-11-16

    Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

  9. Self-organization in bacterial swarming: lessons from myxobacteria

    Science.gov (United States)

    Wu, Yilin; Jiang, Yi; Kaiser, A. Dale; Alber, Mark

    2011-10-01

    When colonizing surfaces, many bacteria are able to self-organize into an actively expanding biofilm, in which millions of cells move smoothly and orderly at high densities. This phenomenon is known as bacterial swarming. Despite the apparent resemblance to patterns seen in liquid crystals, the dynamics of bacterial swarming cannot be explained by theories derived from equilibrium statistical mechanics. To understand how bacteria swarm, a central question is how order emerges in dense and initially disorganized populations of bacterial cells. Here we briefly review recent efforts, with integrated computational and experimental approaches, in addressing this question.

  10. Self-organized synchronization in decentralized power grids.

    Science.gov (United States)

    Rohden, Martin; Sorge, Andreas; Timme, Marc; Witthaut, Dirk

    2012-08-10

    Robust synchronization (phase locking) of power plants and consumers centrally underlies the stable operation of electric power grids. Despite current attempts to control large-scale networks, even their uncontrolled collective dynamics is not fully understood. Here we analyze conditions enabling self-organized synchronization in oscillator networks that serve as coarse-scale models for power grids, focusing on decentralizing power sources. Intriguingly, we find that whereas more decentralized grids become more sensitive to dynamical perturbations, they simultaneously become more robust to topological failures. Decentralizing power sources may thus facilitate the onset of synchronization in modern power grids.

  11. Adaptation to optimal cell growth through self-organized criticality.

    Science.gov (United States)

    Furusawa, Chikara; Kaneko, Kunihiko

    2012-05-18

    A simple cell model consisting of a catalytic reaction network is studied to show that cellular states are self-organized in a critical state for achieving optimal growth; we consider the catalytic network dynamics over a wide range of environmental conditions, through the spontaneous regulation of nutrient transport into the cell. Furthermore, we find that the adaptability of cellular growth to reach a critical state depends only on the extent of environmental changes, while all chemical species in the cell exhibit correlated partial adaptation. These results are in remarkable agreement with the recent experimental observations of the present cells.

  12. Clogging and self-organized criticality in complex networks.

    Science.gov (United States)

    Bianconi, Ginestra; Marsili, Matteo

    2004-09-01

    We propose a simple model that aims at describing, in a stylized manner, how local breakdowns due to imbalances or congestion propagate in real dynamical networks. The model converges to a self-organized critical stationary state in which the network shapes itself as a consequence of avalanches of rewiring processes. Depending on the model's specification, we obtain either single-scale or scale-free networks. We characterize in detail the relation between the statistical properties of the network and the nature of the critical state, by computing the critical exponents. The model also displays a nontrivial, sudden collapse to a complete network.

  13. Self-organization of charged particles in circular geometry

    Science.gov (United States)

    Nazmitdinov, R. G.; Puente, A.; Cerkaski, M.; Pons, M.

    2017-04-01

    The basic principles of self-organization of one-component charged particles, confined in disk and circular parabolic potentials, are proposed. A system of equations is derived, which allows us to determine equilibrium configurations for an arbitrary, but finite, number of charged particles that are distributed over several rings. Our approach reduces significantly the computational effort in minimizing the energy of equilibrium configurations and demonstrates a remarkable agreement with the values provided by molecular dynamics calculations. With the increase of particle number n >180 we find a steady formation of a centered hexagonal lattice that smoothly transforms to valence circular rings in the ground-state configurations for both potentials.

  14. Financial market model based on self-organized percolation

    Institute of Scientific and Technical Information of China (English)

    YANG Chunxia; WANG Jie; ZHOU Tao; LIU Jun; XU Min; ZHOU Peiling; WANG Binghong

    2005-01-01

    Starting with the self-organized evolution of the trader group's structure, a parsimonious percolation model for stock market is established, which can be considered as a kind of betterment of the Cont-Bouchaud model. The return distribution of the present model obeys Lévy form in the center and displays fat-tail property, in accord with the stylized facts observed in real-life financial time series. Furthermore, this model reveals the power-law relationship between the peak value of the probability distribution and the time scales, in agreement with the empirical studies on the Hang Seng Index.

  15. Self-organizing migrating algorithm methodology and implementation

    CERN Document Server

    Zelinka, Ivan

    2016-01-01

    This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA. .

  16. 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...

  17. Surface Approximation using Growing Self-Organizing Nets and Gradient Information

    Directory of Open Access Journals (Sweden)

    Jorge Rivera-Rovelo

    2007-01-01

    Full Text Available In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free. Our algorithms were tested with several images, including medical images (CT and MR images. We include also some examples for the case of 3D surface estimation.

  18. Master thesis: Growth and Self-Organization Processes in Directed Social Network

    CERN Document Server

    Gligorijevic, Vladimir

    2013-01-01

    Large dataset collected from Ubuntu chat channel is studied as a complex dynamical system with emergent collective behaviour of users. With the appropriate network mappings we examined wealthy topological structure of Ubuntu network. The structure of this network is determined by computing different topological measures. The directed, weighted network, which is a suitable representation of the dataset from Ubuntu chat channel is characterized with power law dependencies of various quantities, hierarchical organization and disassortative mixing patterns. Beyond the topological features, the emergent collective state is further quantified by analysis of time series of users activities driven by emotions. Analysis of time series reveals self-organized dynamics with long-range temporal correlations in user actions.

  19. A self-organizing Lagrangian particle method for adaptive-resolution advection-diffusion simulations

    Science.gov (United States)

    Reboux, Sylvain; Schrader, Birte; Sbalzarini, Ivo F.

    2012-05-01

    We present a novel adaptive-resolution particle method for continuous parabolic problems. In this method, particles self-organize in order to adapt to local resolution requirements. This is achieved by pseudo forces that are designed so as to guarantee that the solution is always well sampled and that no holes or clusters develop in the particle distribution. The particle sizes are locally adapted to the length scale of the solution. Differential operators are consistently evaluated on the evolving set of irregularly distributed particles of varying sizes using discretization-corrected operators. The method does not rely on any global transforms or mapping functions. After presenting the method and its error analysis, we demonstrate its capabilities and limitations on a set of two- and three-dimensional benchmark problems. These include advection-diffusion, the Burgers equation, the Buckley-Leverett five-spot problem, and curvature-driven level-set surface refinement.

  20. Implementing automatic LiDAR and supervised mapping methodologies to quantify agricultural terraced landforms at landscape scale: the case of Veneto Region

    Science.gov (United States)

    Eugenio Pappalardo, Salvatore; Ferrarese, Francesco; Tarolli, Paolo; Varotto, Mauro

    2016-04-01

    Traditional agricultural terraced landscapes presently embody an important cultural value to be deeply investigated, both for their role in local heritage and cultural economy and for their potential geo-hydrological hazard due to abandonment and degradation. Moreover, traditional terraced landscapes are usually based on non-intensive agro-systems and may enhance some important ecosystems services such as agro-biodiversity conservation and cultural services. Due to their unplanned genesis, mapping, quantifying and classifying agricultural terraces at regional scale is often critical as far as they are usually set up on geomorphologically and historically complex landscapes. Hence, traditional mapping methods are generally based on scientific literature and local documentation, historical and cadastral sources, technical cartography and aerial images visual interpretation or, finally, field surveys. By this, limitations and uncertainty in mapping at regional scale are basically related to forest cover and lack in thematic cartography. The Veneto Region (NE of Italy) presents a wide heterogeneity of agricultural terraced landscapes, mainly distributed within the hilly and Prealps areas. Previous studies performed by traditional mapping method quantified 2,688 ha of terraced areas, showing the higher values within the Prealps of Lessinia (1,013 ha, within the Province of Verona) and in the Brenta Valley (421 ha, within the Province of Vicenza); however, terraced features of these case studies show relevant differences in terms of fragmentation and intensity of terraces, highlighting dissimilar degrees of clusterization: 1.7 ha on one hand (Province of Verona) and 1.2 ha per terraced area (Province of Vicenza) on the other one. The aim of this paper is to implement and to compare automatic methodologies with traditional survey methodologies to map and assess agricultural terraces in two representative areas of the Veneto Region. Testing different Remote Sensing