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    Tytuł:
    Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level
    Autorzy:
    Kolasa, M.
    Długosz, R.
    Bieliński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/226845.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    self-organizing maps
    parallel signal processing
    CMOS realization
    low energy consumption
    digital circuits
    Opis:
    A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18žm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolution has a dominant influence on the overall circuit complexity i.e. the chip area and the energy consumption. The proposed neighborhood mechanism is very fast. For an example neighborhood range of 15 a delay between the first and the last neighboring neuron does not exceed 20 ns. This in practice means that the adaptation process starts in all neighboring neurons almost at the same time. As a result, data rates of 10-20 MHz are achievable, independently on the number of neurons in the map. The proposed SOM dissipates the power in-between 100 mW and 1 W, depending on the number of neurons in the map. For the comparison, the same network realized on PC achieves in simulations data rates in-between 10 Hz and 1 kHz. Data rate is in this case linearly dependend on the number of neurons.
    Źródło:
    International Journal of Electronics and Telecommunications; 2010, 56, 4; 367-373
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cluster analysis on the example of work data of the National Power System. Part 1, Comparative study of methods and conditions
    Autorzy:
    Tchórzewski, Jerzy
    Kania, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/1819255.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    cluster analysis
    National Electric Power System
    MATLAB and simulink environment
    ward's algorithm
    self-organizing maps
    Opis:
    The paper presents the results of the research on the comparative study of the methods of cluster analysis and conditions, which was carried out from the point of view of their use, on the example of data concerning the operation of the National Power System. Two algorithms were used for the clustering analysis, i.e. the Ward algorithm and the algorithm of self-organizing two-dimensional maps. Cluster analysis was preceded by a review of hierarchical and non-hierarchical methods of data analysis and a description of the prepared experiment. The obtained results were interpreted. The work consists of two parts published under the same main title with different subtitles. This part 1 presents the results of the conducted review of selected methods of cluster analysis and the research conditions resulting from the adopted data on the operation of the National Power System. Part 2 presents the cluster analysis process and selected research results and their discussion.
    Źródło:
    Studia Informatica : systems and information technology; 2019, 1-2(23); 25--41
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of Different-Sized Aerosol Monitoring Data
    Klasyfikacja danych monitoringowych frakcji aerozolu o różnych rozmiarach cząstek
    Autorzy:
    Tsakovski, S.
    Simeonov, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/388252.pdf
    Data publikacji:
    2011
    Wydawca:
    Towarzystwo Chemii i Inżynierii Ekologicznej
    Tematy:
    chemometria
    klasyfikacja
    mapy samoorganizujące się
    frakcja aerozolowa
    chemometrics
    classification
    self-organizing maps
    aerosol fraction
    seasonal sampling
    Opis:
    The present study deals with the application of self-organizing maps (SOM) of Kohonen for the classification of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps for five different aerosol fractions collected in four different seasons of the year. The results obtained indicated a distinct separation of the ultrafine particles (PM 0.01–PM 0.4) from the other fractions which underlines their specific effect on human health. Seasonal separation but only between summer and winter sampling is also observed.
    Przedstawiono wyniki badań monitoringowych próbek aerozolu atmosferycznego pobranych z dwóch punktów pomiarowych (Arnoldstein i Unterloibach) z pobliża granicy między Austrią i Słowenią. Dane zinterpretowano z wykorzystaniem samoorganizujących się map (SOM) Kohonena. Celem chemometrycznej interpretacji danych było znalezienie charakterystycznych struktur na mapach klasyfikacji dla pięciu różnych frakcji aerozoli, zebranych w czterech różnych porach roku. Uzyskane wyniki wskazują na wyraźne oddzielenie najdrobniejszych cząstek (PM 0,01 – PM 0,4) od innych frakcji, co wskazuje na ich specyficzne działanie na zdrowie człowieka. Obserwuje się również zmiany sezonowe, ale tylko między próbkami pobranymi latem i zimą.
    Źródło:
    Ecological Chemistry and Engineering. A; 2011, 18, 2; 275-288
    1898-6188
    2084-4530
    Pojawia się w:
    Ecological Chemistry and Engineering. A
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nursing logistics activities in massive services
    Autorzy:
    Simić, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/333536.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    systemy klasyfikacji
    samoorganizujące się mapy
    nursing logistics activities
    classification system
    learning vector quantization
    self-organizing maps
    Opis:
    Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. It is possible to discus three types of experimental results. First result type could be assessment for risk of falling measured by Mors scale and pressure sores risk measured by Braden scale. Both of them are assessed by LVQ. Hybrid LVQ-SOM model is used for second result type, which presents the time for nursing logistics activities. The third type is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 18; 77-84
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Image Retrieval Based on Text and Visual Content Using Neural Networks
    Autorzy:
    Castro, D. A.
    Seijas, L. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/108732.pdf
    Data publikacji:
    2010
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    image retrieval
    Self-Organizing Maps (SOM)
    content-based image retrieval (CBIR)
    Text-Based Image Retrieval (TBIR)
    ParBSOM
    Scoring function
    Opis:
    In the last few years there has been a dramatic increase in the amount of visual data to be searched and retrieved. Typically, images are described by their textual content (TBIR) or by their visual features (CBIR). However, these approaches still present many problems. The hybrid approach was recently introduced, combining both characteristics to improve the benefits of using text and visual content separately. In this work we examine the use of the Self Organizing Maps for content-based image indexing and retrieval. We propose a scoring function which eliminates irrelevant images from the results and we also introduce a SOM variant (ParBSOM) that reduces training and retrieval times. The application of these techniques to the hybrid approach improved computational results.
    Źródło:
    Journal of Applied Computer Science Methods; 2010, 2 No. 1; 21-39
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiscale evaluation of a thin-bed reservoir
    Autorzy:
    Lis-Śledziona, Anita
    Powiązania:
    https://bibliotekanauki.pl/articles/1841759.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    thin beds
    high resolution well logs prediction
    horizontal resistivity
    unsupervised neural network
    self-organizing maps (SOM)
    electrofacies
    low resistivity pay
    Opis:
    A thin-bed laminated shaly-sand reservoir of the Miocene formation was evaluated using two methods: high resolution microresistivity data from the XRMI tool and conventional well logs. Based on high resolution data, the Earth model of the reservoir was defined in a way that allowed the analyzed interval to be subdivided into thin layers of sandstones, mudstones, and claystones. Theoretical logs of gamma ray, bulk density, horizontal and vertical resistivity were calculated based on the forward modeling method to describe the petrophysical properties of individual beds and calculate the clay volume, porosity, and water saturation. The relationships amongst the contents of minerals were established based on the XRD data from the neighboring wells; hence, the high-resolution lithological model was evaluated. Predicted curves and estimated volumes of minerals were used as an input in multimineral solver and based on the assumed petrophysical model the input data were recalculated, reconstructed and compared with the predicted curves. The volumes of minerals and input curves were adjusted during several runs to minimalize the error between predicted and recalculated variables. Another approach was based on electrofacies modeling using unsupervised self-organizing maps. As an input, conventional well logs were used. Then, the evaluated facies model was used during forward modeling of the effective porosity, horizontal resistivity and water saturation. The obtained results were compared and, finally, the effective thickness of the reservoir was established based on the results from the two methods.
    Źródło:
    Geology, Geophysics and Environment; 2021, 47, 1; 5-20
    2299-8004
    2353-0790
    Pojawia się w:
    Geology, Geophysics and Environment
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network models for combinatorial optimization : a survey of deterministic, stochastic and chaotic approaches
    Autorzy:
    Smith, K.
    Potvin, J.
    Kwok, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/205943.pdf
    Data publikacji:
    2002
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    chaos
    odwzorowanie samoporządkujące
    optymalizacja kombinatoryczna
    sieć Hopfielda
    sieć neuronowa
    combinatorial optimization
    deformable templates
    Hopfield networks
    neural networks
    self-organizing maps
    Opis:
    This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models : the gradient-based neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self organizing maps. In each class, the original model is presented, its limitations discussed, and subsequent developments and extensions are reviewed. Particular emphasis is placed on stochastic and chaotic variations on the neural network models designed to improve the optimization performance. Finally, the performance of these neural network models is compared and discussed relative to other heuristic approaches.
    Źródło:
    Control and Cybernetics; 2002, 31, 2; 183-216
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Probabilistic morphological modeling of hydrographic networks from satellite imagery using Self-Organizing Maps
    Autorzy:
    Zaremba, M.
    Palenichka, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/206609.pdf
    Data publikacji:
    2002
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    łańcuch Markowa
    modelowanie morfologiczne
    odwzorowanie samoporządkujące
    satelitarne rozpoznawanie obrazów
    sieć neuronowa
    szkieletyzacja
    Markov chains
    morphological modeling
    neural networks
    satellite image processing
    self-organizing maps
    skeletonization
    Opis:
    Adequate and concise representation of the shape of irregular objects from satellite imagery is a challenging problem in remote sensing. The conventional methods for cartographic shape representation are usually inaccurate and will provide only a rough shape description if the description process is to be fully automated. The method for automatic cartographic description of water basins presented in this paper is based on Self-Organizing Maps (SOM) - a class of neural networks with unsupervised learning. So-called structured SOM with local shape attributes such as scale and local connections of vertices are proposed for the description of object shape. The location of each vertex of piecewise linear generating curves that represent skeletons of the objects corresponds to the position of a particular SOM unit. The proposed method makes it possible to extract the object skeletons and to reconstruct the planar shapes of sparse objects based on the topological constraints of generating lines and the estimation of local scale. A context-dependent vertex connectivity test is proposed to enhance the skeletonization process. The test is based on the Markov random chain model of vertices belonging to the same generating line and the Bayesian decision-making principle. The experimental test results using Landsat-7 images demonstrate the accuracy of the proposed approach and its potential for fully automated mapping of hydrological objects.
    Źródło:
    Control and Cybernetics; 2002, 31, 2; 343-369
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cluster analysis on the example of work data of the National Power System. Part 2, Research and selected results
    Autorzy:
    Tchórzewski, Jerzy
    Kania, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052268.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    cluster analysis
    National Power System
    MATLAB
    Simulink
    Ward's algorithm
    self-organizing two-dimensional maps
    Opis:
    The work is a continuation of the article under the same main title and subtitle Part 1. Comparative study of methods and conditions. This article concerns the cluster analysis, which was carried out on the example of data concerning the operation of the National Power System, namely the total generations of nCDGUs and CDGUs listed by PSE Operator. Two algorithms were used to obtain the results of the cluster analysis, i.e. the Ward algorithm and the algorithm of self-organizing twodimensional topographic maps. The obtained results were interpreted and their discussion and interpretation were conducted.
    Źródło:
    Studia Informatica : systems and information technology; 2020, 1-2(24); 5-23
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Music Mood Visualization Using Self-Organizing Maps
    Autorzy:
    Plewa, M.
    Kostek, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/176410.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    music mood
    music parameterization
    MER (Music Emotion Recognition)
    MIR (Music Information Retrieval)
    Multidimensional Scaling (MDS)
    principal component analysis (PCA)
    Self-Organizing Maps (SOM)
    ANN (Artificial Neural Networks)
    Opis:
    Due to an increasing amount of music being made available in digital form in the Internet, an automatic organization of music is sought. The paper presents an approach to graphical representation of mood of songs based on Self-Organizing Maps. Parameters describing mood of music are proposed and calculated and then analyzed employing correlation with mood dimensions based on the Multidimensional Scaling. A map is created in which music excerpts with similar mood are organized next to each other on the two-dimensional display.
    Źródło:
    Archives of Acoustics; 2015, 40, 4; 513-525
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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