Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Osowski, S." wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-8 z 8
    Tytuł:
    Bagging and boosting techniques in prediction of particulate matters
    Autorzy:
    Triana, D.
    Osowski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/202449.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ensemble of predictors
    bagging
    boosting
    PM pollution
    Opis:
    The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1207-1215
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Local dynamic integration of ensemble in prediction of time series
    Autorzy:
    Osowski, S.
    Siwek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/201557.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural networks
    ensemble of predictors
    dynamic integration
    time series prediction
    sieci neuronowe
    zespół predyktorów
    dynamiczna integracja
    Opis:
    The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 517-525
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of data mining methods for gene ranking
    Autorzy:
    Wiliński, A.
    Osowski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/201570.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    gene expression array
    feature selection
    gene ranking methods
    classification
    SVM
    Opis:
    The paper presents the ensemble of data mining methods for discovering the most important genes and gene sequences generated by the gene expression arrays, responsible for the recognition of a particular type of cancer. The analyzed methods include the correlation of the feature with a class, application of the statistical hypotheses, the Fisher measure of discrimination and application of the linear Support Vector Machine for characterization of the discrimination ability of the features. In the first step of ranking we apply each method individually, choosing the genes most often selected in the cross validation of the available data set. In the next step we combine the results of different selection methods together and once again choose the genes most frequently appearing in the selected sets. On the basis of this we form the final ranking of the genes. The most important genes form the input information delivered to the Support Vector Machine (SVM) classifier, responsible for the final recognition of tumor from non-tumor data. Different forms of checking the correctness of the proposed ranking procedure have been applied. The first one is relied on mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation and applying the cluster quality measures. The other one depicts the results in the graphical form by presenting the gene expressions in the form of pixel intensity for the available data. The final confirmation of the quality of the proposed ranking method are the classification results of recognition of the cancer cases from the non-cancer (normal) ones, performed using the Gaussian kernel SVM. The results of selection of the most significant genes used by the SVM for recognition of the prostate cancer cases from normal cases have confirmed a good accuracy of results. The presented methodology is of potential use for practical application in bioinformatics.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 461-470
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modified neuro-fuzzy TSK network and its application in electronic nose
    Autorzy:
    Osowski, S.
    Brudzewski, K.
    Tran-Hoai, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/201226.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neuro-fuzzy TSK networks
    fuzzy clusterization
    regression
    classification
    Opis:
    The paper develops the modified structure of the Takagi-Sugeno-Kang neuro-fuzzy network with a theoretical basis for its adaptation. The simplified structure follows from the basic theoretical considerations concerning the way of creating the inference rules. The important point of this solution is the application of the fuzzy clustering algorithm to the input data. The efficiency of the proposed solution has been checked on the examples of regression and classification problems concerning the electronic nose.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 675-680
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning: theory and practice
    Autorzy:
    Cichocki, A.
    Poggio, T.
    Osowski, S.
    Lempitsky, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/202346.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    networks
    theory
    practice
    uczenie głębokie
    sieci
    teoria
    praktyka
    Opis:
    This Special Section of the Bulletin of the Polish Academy of Sciences on Technical Sciences is devoted to theoretical aspects of deep machine learning as well as practical applications in some areas of signal and image processing, particularly in bioengineering.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 757-759
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning versus classical neural approach to mammogram recognition
    Autorzy:
    Kurek, J.
    Świderski, B.
    Osowski, S.
    Kruk, M.
    Barhoumi, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/200919.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolutional neural networks
    breast cancer diagnosis
    mammogram recognition
    diagnostic features
    splotowe sieci neuronowe
    diagnostyka raka piersi
    rozpoznawanie
    mammografia
    cechy diagnostyczne
    Opis:
    Automatic recognition of mammographic images in breast cancer is a complex issue due to the confusing appearance of some perfectly normal tissues which look like masses. The existing computer-aided systems suffer from non-satisfactory accuracy of cancer detection. This paper addresses this problem and proposes two alternative techniques of mammogram recognition: the application of a variety of methods for definition of numerical image descriptors in combination with an efficient SVM classifier (so-called classical approach) and application of deep learning in the form of convolutional neural networks, enhanced with additional transformations of input mammographic images. The key point of the first approach is defining the proper numerical image descriptors and selecting the set which is the most class discriminative. To achieve better performance of the classifier, many image descriptors were defined by means of applying different characterization of the images: Hilbert curve representation, Kolmogorov-Smirnov statistics, the maximum subregion principle, percolation theory, fractal texture descriptors as well as application of wavelet and wavelet packets. Thanks to them, better description of the basic image properties has been obtained. In the case of deep learning, the features are automatically extracted as part of convolutional neural network learning. To get better quality of results, additional representations of mammograms, in the form of nonnegative matrix factorization and the self-similarity principle, have been proposed. The methods applied were evaluated based on a large database composed of 10,168 regions of interest in mammographic images taken from the DDSM database. Experimental results prove the advantage of deep learning over traditional approach to image recognition. Our best average accuracy in recognizing abnormal cases (malignant plus benign versus healthy) was 85.83%, with sensitivity of 82.82%, specificity of 86.59% and AUC = 0.919. These results are among the best for this massive database.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 831-840
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Thresholding techniques for segmentation of atherosclerotic plaque and lumen areas in vascular arteries
    Autorzy:
    Markiewicz, T.
    Dziekiewicz, M.
    Osowski, S.
    Maruszyński, M.
    Kozłowski, W.
    Bogusławska-Walecka, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/201734.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    image processing
    thresholding methods
    computed tomography
    vascular image parameterization
    przetwarzanie obrazu
    metody progowania
    tomografia komputerowa
    Opis:
    The paper develops the automatic methods of segmentation of the blood vessel area in the images of the multi-slice computed tomography, allowing to separate the lumen from the atherosclerotic plaque areas. The solution is based on the application of different implementations of thresholding, including between class variance in a bimodal mode, Gaussian mixture modeling, clustering technique, polynomial and multilayer perceptron approximations. These methods are compared with many examples of arteries of different percentage of the plaque occupancy in the iliac and femoral arteries. The numerical results of segmentation have been verified by the medical experts and prove its usefulness in medical practice. The presented system can find application in an automatic evaluation of the atherosclerosis progression/regression of patients on the basis of sequence of Computed Tomography slice images.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 1; 269-280
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Developing automatic recognition system of drill wear in standard laminated chipboard drilling process
    Autorzy:
    Kurek, J.
    Kruk, M.
    Osowski, S.
    Hoser, P.
    Wieczorek, G.
    Jegorowa, A.
    Górski, J.
    Wilkowski, J.
    Śmietańska, K.
    Kossakowska, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/200766.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    diagnostic expert systems
    neural networks
    wavelet packets
    wear monitoring
    diagnostyczny system ekspercki
    sieci neuronowe
    pakiety falkowe
    monitorowanie zużycia
    Opis:
    The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: “useful” (sharp enough) and “useless” (worn out). The case “useless” indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the “useful” state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state (“useful” versus “useless”) can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 633-640
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies