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    Wyświetlanie 1-8 z 8
    Tytuł:
    Wykorzystanie maszyny wektorów wspierających (SVM) do klasyfikacji sygnału EEG na użytek interfejsu mózg-komputer
    Implementation of support vector machine for classification of EEG signal for brain-computer interface
    Autorzy:
    Kołodziej, M.
    Majkowski, A.
    Rak, R. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/155968.pdf
    Data publikacji:
    2011
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    BCI
    interfejs mózg-komputer
    EEG
    maszyna wektorów wspierających
    SVM
    brain-computer interface
    support vector machine
    Opis:
    W artykule przedstawiono wykorzystanie maszyny wektorów wspierających (SVM) na użytek interfejsów mózg-komputer (BCI). W opracowanych algorytmach jako cechy sygnału EEG wykorzystano jego wariancję. Przedstawiono wyniki badań związanych z wykorzystaniem sieci SVM jako klasyfikatora. Eksperymenty przeprowadzono przy użyciu różnego rodzaju funkcji jądra.
    Implementing communication between man and machine by use of EEG signals is one of the biggest challenges in the signal theory. Such communication could improve the standard of living of people with severe motor disabilities. Some disable persons cannot move, however they can think about moving their arms, legs and this way produce stable motor-related EEG signals. These signals can be used to construct BCI systems. However, the proper interpretation of the EEG signals is a very difficult task. There are three main stages in EEG signal analysis: feature extraction, feature selection and classification. The main aim of the paper is to implement a support vector machine as a classifier for the brain-computer interface. The proposed algorithm uses the EEG signal variance in the frequency range 8-30Hz. Experiments were conducted with use of different kernel functions for the SVM classifier. The best results were achieved for the quadratic polynomial kernel function. The classification error for testing data was 0.13.
    Źródło:
    Pomiary Automatyka Kontrola; 2011, R. 57, nr 12, 12; 1546-1548
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification
    Autorzy:
    Chmielnicki, W.
    Stąpor, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/330749.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    multiclass classification
    pairwise coupling
    problem decomposition
    support vector machine (SVM)
    klasyfikacja wieloklasowa
    rozkład problemu
    maszyna wektorów wspierających
    Opis:
    The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes. A closer look at the pairwise strategy shows the problem which impacts the final result. Each binary classifier votes for each object even if it does not belong to one of the two classes which it is trained on. This problem is addressed in our strategy. We propose to use additional classifiers to select the objects which will be considered by the pairwise classifiers. A similar solution was proposed by Moreira and Mayoraz (1998), but they use classifiers which are biased according to imbalance in the number of samples representing classes.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 191-201
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A support vector machine with the tabu search algorithm for freeway incident detection
    Autorzy:
    Yao, B.
    Hu, P.
    Zhang, M.
    Jin, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/329943.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automated incident detection
    support vector machine (SVM)
    tabu search
    freeway
    maszyna wektorów wspierających
    odcinek swobodny trasy
    algorytm tabu search
    Opis:
    Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 397-404
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Klasyfikacja tekstur za pomocą SVM - Maszyny Wektorów Wspierających
    Texture classification using Support Vector Machine
    Autorzy:
    Goszczyński, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/289416.pdf
    Data publikacji:
    2006
    Wydawca:
    Polskie Towarzystwo Inżynierii Rolniczej
    Tematy:
    maszyna wektorów wspierających
    rozpoznawanie wzorców
    rozpoznawanie obrazów
    rolniczy robot mobilny
    support vector machine (SVM)
    pattern recognition
    image recognition
    agriculture mobile robot
    Opis:
    Motywacją do badań był pomysł wytworzenia robota-kosiarki wyposażonego w system komputerowego widzenia. Rozpoznawanie obrazu może zostać zrealizowane za pomocą klasyfikacji tekstur obiektów, które otaczają robota. Artykuł przedstawia przykład klasyfikacji tekstur za pomocą Maszyny wektorów wspierających SVM (ang. Support Vector Machine) Do badań wykorzystano oprogramowanie LIBSVM.
    Motivation for research was idea to create mower robot with computer vision system. Image recognition can be done by textures classification of objects that robot is surrounded. This article has reviewed example of texture classification by SVM Support vector machine. For research was used LIBSVM software.
    Źródło:
    Inżynieria Rolnicza; 2006, R. 10, nr 13(88), 13(88); 119-126
    1429-7264
    Pojawia się w:
    Inżynieria Rolnicza
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimating the compressive strength of concrete, using vacuum dewatering technique
    Autorzy:
    Subhash, D.
    Gupta, S. M.
    Setia, S.
    Pavlykivskyi, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/378711.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    vacuum dewatering
    concrete compressive strength
    artificial neural network
    Support Vector Machine
    odwadnianie próżniowe
    wytrzymałość betonu na ściskanie
    sztuczna sieć neuronowa
    maszyna wektorów wspierających
    Opis:
    Purpose: Investigate the potential of vacuum dewatering process of on three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. Design/methodology/approach: For this study a data set of 90 experimental observations obtained from laboratory testing with and without application of vacuum dewatering after designing and casting the concrete of said three grades. The standard cubes of size 150 mm × 150 mm × 150 mm were obtained by core cutting and tested for compression after 3, 7, 14, 21 and 28 days of proper curing. Accuracy of prediction of compressive strength of concrete by application of M5P, ANN and SVM as artificial intelligence techniques and their feasibility are assessed to estimate the compressive strength of the concrete enacted with vacuum dewatering technique. A total data set was segregated in two groups. A group of 63 observations was used for model development and smaller group of 27 observations was used for testing the models. Findings: Overall performance of ANN based developed model is better than M5P and SVM based models for predicting the compressive strength of concrete for this data set. Research limitations/implications: Investigated three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. The experimental research involved only testing of cubes only. Practical implications: Using ANN based developed model makes it possible to quickly and accurately predict the compressive strength of concrete. Originality/value: The results of comparing three models for predicting the compressive strength of concrete and the optimal values of ANN based developed models are presented. Earlier no one has applied M5P, ANN and SVM modelling to predict the compressive strength of vacuum dewatered concrete.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 99, 1/2; 30-41
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptive control scheme based on the least squares support vector machine network
    Autorzy:
    Mahmoud, T. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/930155.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    modelowanie systemu
    system nieliniowy
    system sterowania
    sieć neuronowa
    maszyna wektorów wspierających
    support vector machine (SVM)
    neural network
    nonlinear system modeling
    nonlinear system control
    pH control
    Opis:
    Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 685-696
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic parametric fault detection in complex analog systems based on a method of minimum node selection
    Autorzy:
    Bilski, A.
    Wojciechowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/330761.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    complex analog system
    support vector machine (SVM)
    tabu search
    genetic algorithm
    parametric fault detection
    system analogowy
    maszyna wektorów wspierających
    metoda tabu search
    algorytm genetyczny
    detekcja uszkodzeń
    Opis:
    The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 3; 655-668
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Lung cancer detection using an integration of fuzzy K-Means clustering and deep learning techniques for CT lung images
    Autorzy:
    Prasad, J. Maruthi Nagendra
    Chakravarty, S.
    Krishna, M. Vamsi
    Powiązania:
    https://bibliotekanauki.pl/articles/2173683.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fuzzy K-means
    artificial neural networks
    SVM
    support vector machine
    crow search optimization algorithm
    algorytm rozmytych k-średnich
    sztuczne sieci neuronowe
    maszyna wektorów wspierających
    algorytm optymalizacji wyszukiwania kruków
    Opis:
    Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139006
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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