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    Wyświetlanie 1-15 z 15
    Tytuł:
    Characteristic points detection in ECG signal using Bayesian learning and fuzzy system
    Autorzy:
    Momot, M.
    Momot, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333840.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    sygnał EKG
    systemy rozmyte
    ECG signal
    fuzzy systems
    bayesian learning
    Opis:
    Characteristic points detection such as beginnings and ends of P-wave, T-wave or QRS complex is one of primary aims in automated analysis of ECG signal. The paper presents one possible approach based on Bayesian inference to design of kernel based classifier. The classification function is constructed using the probability distribution function of standard normal distribution and independent Gaussian random variables. The parameters of such variables are computed using iterative Expectation-Maximization algorithm. This approach is used to calculate parameters of classification function to modelling Takagi-Sugeno-Kang fuzzy systems. Numerical experiment of characteristic points detection in ECG signal using CTS database is also presented.
    Źródło:
    Journal of Medical Informatics & Technologies; 2007, 11; 171-176
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Underwater target direction of arrival estimation by small acoustic sensor array based on sparse bayesian learning
    Autorzy:
    Wang, B.
    He, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/260588.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    DOA
    underwater acoustic signal processing
    sparse Bayesian learning
    temporally correlated source
    Opis:
    Assuming independently but identically distributed sources, the traditional DOA (direction of arrival) estimation method of underwater acoustic target normally has poor estimation performance and provides inaccurate estimation results. To solve this problem, a new high-accuracy DOA algorithm based on sparse Bayesian learning algorithm is proposed in terms of temporally correlated source vectors. In novel method, we regarded underwater acoustic source as a first-order auto-regressive process. And then we used the new algorithm of multi-vector SBL to reconstruct the signal spatial spectrum. Then we used the CS-MMV model to estimate the DOA. The experiment results have shown the novel algorithm has a higher spatial resolution and estimation accuracy than other DOA algorithms in the cases of less array element space and less snapshots.
    Źródło:
    Polish Maritime Research; 2017, S 2; 95-102
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals
    Autorzy:
    Upadhyaya, Vivek
    Salim, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/1844532.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    compressive sensing
    CS
    mean square error
    MSE
    structural similarity index measure
    SSIM
    electroencephalogram
    EEG
    digital signal processing
    DSP
    block sparse Bayesian learning
    BSBL
    Opis:
    Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 3; 331-336
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wykorzystanie sieci bayesowskich do prognozowania bankructwa firm
    Bankruptcy prediction with Bayesian networks
    Autorzy:
    Gąska, Damian
    Powiązania:
    https://bibliotekanauki.pl/articles/434020.pdf
    Data publikacji:
    2016
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    bankruptcy prediction
    Bayesian network
    structure learning
    Opis:
    The aim of the paper is to compare accuracy of some bankruptcy prediction models based on Bayesian networks. Some network structure learning algorithms were analyzed as a tool for classifiers construction. Empirical analysis was applied to companies listed on Warsaw Stock Exchange. The paper gives short overview of theoretical background behind discussed issues and presents results of empirical analysis.
    Źródło:
    Śląski Przegląd Statystyczny; 2016, 14 (20); 131-144
    1644-6739
    Pojawia się w:
    Śląski Przegląd Statystyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evolutionary algorithm for learning Bayesian structures from data
    Autorzy:
    Kozłowski, M.
    Wierzchoń, S. T.
    Powiązania:
    https://bibliotekanauki.pl/articles/1986916.pdf
    Data publikacji:
    2002
    Wydawca:
    Politechnika Gdańska
    Tematy:
    Bayesian networks
    structure learning
    evolutionary algorithm
    discrete optimization
    Opis:
    In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in the space of directed acyclic graphs instead of standard space of binary strings. This requires adaptation of evolutionary methodology into very specific needs. We propose quite new data representation and implementation of generalized genetic operators and then we present an efficient algorithm capable of learning complex networks without additional assumptions. We discuss results obtained with this algorithm. The approach presented in this paper can be extended with the possibility to absorb some suggestions from experts or obtained by means of data preprocessing.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2002, 6, 3; 509-521
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children
    Autorzy:
    Łaguna, W.
    Bagińska, J.
    Oniśko, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1918880.pdf
    Data publikacji:
    2019-08-26
    Wydawca:
    Uniwersytet Medyczny w Białymstoku
    Tematy:
    dental caries
    Bayesian network
    learning from data
    risk assessment
    Opis:
    Purpose - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. Materials and Methods - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. Results - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. Conclusions - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.
    Źródło:
    Progress in Health Sciences; 2019, 1; 118-125
    2083-1617
    Pojawia się w:
    Progress in Health Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    From conventional to machine learning methods for maritime riskassessment
    Autorzy:
    Rawson, A.
    Brito, M.
    Sabeur, Z.
    Tran-Thanh, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/2063954.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    risk assessment
    machine learning method
    bayesian networks
    machine learning algorithms
    multicriteria approach
    maritime risk
    Opis:
    Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2021, 15, 3; 757--764
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Sparse Bayesian learning in classifying face feature vectors
    Autorzy:
    Momot, A.
    Kawulok, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333794.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    wnioskowanie bayesowskie
    rozpoznanie twarzy
    supervised learning
    Bayesian inference
    face recognition
    Opis:
    The Relevance Vector Machine (RVM), a Bayesian treatment of generalized linear model of identical functional form to the Support Vector Machine (SVM), is the recently developed machine learning framework capable of building simple models from large sets of candidate features. The paper describes the application of the RVM to a classification algorithm of face feature vectors, obtained by Eigenfaces method. Moreover, the results of the RVM classification are compared with those obtained by using both the Support Vector Machine method and the method based on the Euclidean distance.
    Źródło:
    Journal of Medical Informatics & Technologies; 2005, 9; 151-158
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Falcon optimization algorithm for bayesian network structure learning
    Autorzy:
    Kareem, Shahab Wahhab
    Okur, Mehmet Cudi
    Powiązania:
    https://bibliotekanauki.pl/articles/2097968.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    Bayesian network
    global search
    falcon optimization algorithm
    structure learning
    search and score
    Opis:
    In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).
    Źródło:
    Computer Science; 2021, 22 (4); 553--569
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Outside the box : an alternative data analytics framework
    Autorzy:
    Angelov, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/950988.pdf
    Data publikacji:
    2014
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    data density
    proximity measures
    RDE
    data
    analytics
    data-driven approaches
    machine learning
    Bayesian
    Opis:
    In this paper, an alternative framework for data analytics is proposed which is based on the spatially-aware concepts of eccentricity and typicality which represent the density and proximity in the data space. This approach is statistical, but differs from the traditional probability theory which is frequentist in nature. It also differs from the belief and possibility-based approaches as well as from the deterministic first principles approaches, although it can be seen as deterministic in the sense that it provides exactly the same result for the same data. It also differs from the subjective expert-based approaches such as fuzzy sets. It can be used to detect anomalies, faults, form clusters, classes, predictive models, controllers. The main motivation for introducing the new typicality- and eccentricity-based data analytics (TEDA) is the fact that real processes which are of interest for data analytics, such as climate, economic and financial, electro-mechanical, biological, social and psychological etc., are often complex, uncertain and poorly known, but not purely random. Unlike, purely random processes, such as throwing dices, tossing coins, choosing coloured balls from bowls and other games, real life processes of interest do violate the main assumptions which the traditional probability theory requires. At the same time they are seldom deterministic (more precisely, have always uncertainty/noise component which is nondeterministic), creating expert and belief-based possibilistic models is cumbersome and subjective. Despite this, different groups of researchers and practitioners favour and do use one of the above approaches with probability theory being (perhaps) the most widely used one. The proposed new framework TEDA is a systematic methodology which does not require prior assumptions and can be used for development of a range of methods for anomalies and fault detection, image processing, clustering, classification, prediction, control, filtering, regression, etc. In this paper due to the space limitations, only few illustrative examples are provided aiming proof of concept.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2014, 8, 2; 29-35
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A cloud-based urban monitoring system by using a quadcopter and intelligent learning techniques
    Autorzy:
    Khanmohammadi, Sohrab
    Samadi, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/27314186.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    urban monitoring
    cloud computing
    quadcopter
    deep learning
    fuzzy system
    image processing
    pattern recognition
    bayesian network
    intelligent techniques
    learning systems
    Opis:
    The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occurrences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 2; 11--19
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Learning style recognition based on an adjustable three-layer fuzzy cognitive map
    Autorzy:
    Georgiou, D. A.
    Botsios, S.
    Mitropoulou, V.
    Papaioannou, M.
    Schizas, C.
    Tsoulouhas, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91896.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    learning style
    adaptive educational hypermedia systems
    Kolb’s learning cycle
    Fuzzy Cognitive Map
    FCM
    Learning Ability Factors
    Bayesian networks
    cognitive map
    three-layer fuzzy
    Opis:
    Identification of learning styles supports Adaptive Educational Hypermedia Systems compiling and presenting tutorials custom in cognitive characteristics of each individual learner. This work addresses the issue: identifying the learning style of students, following the Kolb’s learning cycle. To this purpose, we propose a three-layers Fuzzy Cognitive Map (FCM) in conjunction with a dynamic Hebbian rule for learning styles recognition. The form of FCMs is designed by humans who determine its weighted interconnections among concepts. But the human factor may not be as reliable as it should be. Thus, a FCM model of the system allowing the adjustment of its weights using additional learners’ characteristics such as the Learning Ability Factors. In this article, two consecutively interconnected FCM (in the form of a three layer FCM) are presented. The schema’s efficiency has been tested and compared to known results after a fine-tuning of the weights of the causal interconnections among concepts. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. The online recognition of learning styles by using threelayer Fuzzy Cognitive Map improves the accuracy of recognition obtained using Bayesian Networks that uses quantitative measurements of learning style taken from statistical samples. This improvement is due to the fuzzy nature of qualitative characterizations (such as learning styles), and the presence of intermediate level nodes representing Learning Ability Factors. Such factors are easily recognizable characteristics of a learner to improve adjustment of weights in edges with one end in the middle-level nodes. This leads to the establishment of a more reliable model, as shown by the results given by the application to a test group of students.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 4; 333-347
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Bayesian optimization for solving high-frequency passive component design problems
    Autorzy:
    Baranowski, Michal
    Fotyga, Grzegorz
    Lamecki, Adam
    Mrozowski, Michal
    Powiązania:
    https://bibliotekanauki.pl/articles/2173688.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    high-frequency design
    machine learning
    Bayesian optimization
    optymalizacja bayesowska
    konstrukcja o wysokiej częstotliwości
    nauczanie maszynowe
    Opis:
    In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all problems. Here, BO was applied to different types of microwave and antenna engineering problems, including matching circuit design, multiband antenna and antenna array design, or microwave filter design. Since each of the presented problems has a different nature and characteristics such as different scales (i.e. number of design variables), we try to address the question about the generality of BO and identify the problem areas for which the technique is or is not recommended.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141595
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Review of Bayesian Networks and Structure Learning
    Autorzy:
    Koski, Timo J.T.
    Noble, John
    Powiązania:
    https://bibliotekanauki.pl/articles/748766.pdf
    Data publikacji:
    2012
    Wydawca:
    Polskie Towarzystwo Matematyczne
    Tematy:
    Bayesian networks, directed acyclic graph, Arthur Cayley, intervention calculus, graphical Markov model, Markov equivalence, structure learning
    Opis:
    Artykuł jest przegladem problemów analizowanych przy pomocy sieci bayesowskich. Siec bayesowska jest acyklicznym grafem skierowanym, w którym wezły oznaczaja zmienne, a krawedzie prawdopodobienstwa warunkowe czyli wpływy jednych zmiennych na inne. Autor przedstawia zaleznosc miedzy d-separowalnoscia a niezaleznoscia. Znaczna czesc pracy poswiecona jest dyskusji idei zawartych w pracy Arthura Cayley'a [8], która zawiera szereg pojec i pomysłów wykorzystywanych w teorii sieci bayesowskich takich jak faktoryzacja rozkładu, zaszumione bramki „LUB" oraz zastosowanie geometrii algebraicznej. Autor omawia równiez „calculus of intervention", pomysł pochodzacy od Pearla, gdy acykliczny graf skierowany (DAG) przedstawia przyczynowo-skutkowa strukture zaleznosci, oraz zwiazki pomiedzy pracami Cayley'a i Pearla.Wiekszosc zawartego w artykule materiału poswiecona jest rozpoznawaniu i wykrywaniu zaleznosci miedzy zmiennymi w oparciu o dwie główne metodologie: przeszukiwania i klasyfikacji oraz realizacji ograniczen. Algorytmy oparte na kontroli ograniczen czesto opieraja sie na załozeniu, ze dane do których algorytm jest stosowany pochodza z rozkładu spełniajacego załozenie wiernosci oznaczajacego równowaznosc d-separowalnosci i niezaleznosci. W pracy prezentowane sa rozwazania dla algorytmów opartych na realizacji ograniczen w  przypadkach gdy załozenie wiernosci nie jest spełnione. Przeprowadzono krótka dyskusje kontrowersji zwiazanych z wykrywaniem przypadkowych powiazan.
    This article reviews the topic of Bayesian networks. A Bayesian network  is a factorisation of a probability distribution along a directed acyclic graph. The relation between graphical d-separation and independence is described. A short article by Arthur Cayley (1853) [7] is discussed, which laid ideas later used in Bayesian networks: factorisation, the noisy `or' gate, applications of algebraic geometry to Bayesian networks. The ideas behind Pearl's intervention calculus when the DAG represents a causal dependence structure; the relation between the work of Cayley and Pearl is commented on.Most of the discussion is about structure learning, outlining the two main approaches; search and score versus constraint based. Constraint based algorithms often rely on the assumption of faithfulness, that the data to which the algorithm is applied is generated from distributions satisfying a faithfulness assumption where graphical d- separation and independence are equivalent. The article presents some considerations for constraint based algorithms based on recent data analysis, indicating a variety of situations where the faithfulness assumption does not hold.
    Źródło:
    Mathematica Applicanda; 2012, 40, 1
    1730-2668
    2299-4009
    Pojawia się w:
    Mathematica Applicanda
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel reliability estimation method of multi-state system based on structure learning algorithm
    Nowatorska metoda oceny niezawodności systemów wielostanowych w oparciu o algorytm uczenia struktury
    Autorzy:
    Li, Zhifeng
    Wang, Zili
    Ren, Yi
    Yang, Dezhen
    Lv, Xing
    Powiązania:
    https://bibliotekanauki.pl/articles/301718.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    reliability analysis
    Bayesian network
    structure learning
    multi-state system (MSS)
    dependent failure
    analiza niezawodności
    sieć bayesowska
    uczenie struktury
    system wielostanowy
    uszkodzenie zależne
    Opis:
    Traditional reliability models, such as fault tree analysis (FTA) and reliability block diagram (RBD), are typically constructed with reference to the function principle graph that is produced by system engineers, which requires substantial time and effort. In addition, the quality and correctness of the models depend on the ability and experience of the engineers and the models are difficult to verify. With the development of data acquisition, data mining and system modeling techniques, the operational data of a complex system considering multi-state, dependent behavior can be obtained and analyzed automatically. In this paper, we present a method that is based on the K2 algorithm for establishing a Bayesian network (BN) for estimating the reliability of a multi-state system with dependent behavior. Facilitated by BN tools, the reliability modeling and the reliability estimation can be conducted automatically. An illustrative example is used to demonstrate the performance of the method.
    Tradycyjne modele niezawodności, takie jak analiza drzewa błędów (FTA) czy schemat blokowy niezawodności (RBD), buduje się zazwyczaj w oparciu o tworzone przez inżynierów systemowych schematy zasad działania systemu, których przygotowanie wymaga dużych nakładów czasu i pracy. Jakość i poprawność tych modeli zależy od umiejętności i doświadczenia inżynierów, a same modele są trudne do zweryfikowania. Dzięki rozwojowi technik akwizycji i eksploracji danych oraz modelowania systemów, dane operacyjne złożonego systemu uwzględniające jego zależne, wielostanowe zachowania mogą być pozyskiwane i analizowane automatycznie. W artykule przedstawiono metodę konstrukcji sieci bayesowskiej (BN) opartą na algorytmie K2, która pozwala na ocenę niezawodności systemu wielostanowego o zachowaniach zależnych. Dzięki narzędziom BN, modelowanie i szacowanie niezawodności może odbywać się automatycznie. Działanie omawianej metody zilustrowano na podstawie przykładu.
    Źródło:
    Eksploatacja i Niezawodność; 2020, 22, 1; 170-178
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-15 z 15

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