Temat: stochastic prediction - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Modelling, identification and prediction of oil spill domains at port and sea water areas
Autorzy:: Dąbrowska, Ewa
Kołowrocki, Krzysztof
Powiązania:: https://bibliotekanauki.pl/articles/2068700.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:: port accident
sea accident
oil spill
oil spill drift
oil spill domain
stochastic modelling
statistical identification
stochastic prediction
Monte Carlo prediction
Opis:: Methods of oil spill domains determination are reviewed and a new method based on a probabilistic approach to the solution of this problem is recommended. A semi-Markov model of the process of changing hydro-meteorological conditions is constructed. To describe the oil spill domain central point position a two-dimensional stochastic process is used. Parametric equations of oil spill domain central point drift trend curve for different kinds of hydro-meteorological conditions are determined. The general model of oil spill domain determination for various hydro-meteorological conditions is proposed. Moreover, statistical methods of this general model unknown parameters estimation are proposed. These methods are presented in the form of algorithms giving successive steps which should be done to evaluate these unknown model parameters on the base of statistical data coming from experiments performed at the sea. Moreover, approximate expected stochastic prediction and Monte Carlo Simulation in real time prediction of the oil spill domain movement are proposed.
Źródło:: Journal of Polish Safety and Reliability Association; 2019, 10, 1; 43--58
2084-5316
Pojawia się w:: Journal of Polish Safety and Reliability Association
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Computer-based voltage dip assessment in transmission and distribution networks
Autorzy:: Martínez-Velasco, J. A.
Powiązania:: https://bibliotekanauki.pl/articles/262803.pdf
Data publikacji:: 2008
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
Tematy:: voltage dips
modelling
simulation
stochastic prediction
distributed generation
Opis:: Digital simulation is a powerful mean to predict the voltage dip performance of a power network. Voltage dip characteristics can be accurately reproduced using present simulation tools and a stochastic prediction procedure that could incorporate the random nature of the voltage dip causes and the behaviour of sensitive equipment during this type of events. This work is aimed at providing a review of techniques that can be applied to voltage dip prediction, assuming that voltage dips are caused by faults. The paper includes a discussion on modelling guidelines to be used for representation of power system components in voltage dip calculations, a summary of the capabilities required in simulation tools applied to voltage dip studies (characterisation, assessment, index calculations) and a representative list of procedures presented to date for voltage dip assessment in both transmission and distribution levels. The last section is aimed at providing some information about the difficulties related to voltage dip assessment when distributed generation is connected to the grid.
Źródło:: Electrical Power Quality and Utilisation. Journal; 2008, 14, 1; 31-38
1896-4672
Pojawia się w:: Electrical Power Quality and Utilisation. Journal
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Fuzzy interpretation for temporal-difference learning in anomaly detection problems
Autorzy:: Sukhanov, A. V.
Kovalev, S. M.
Stýskala, V.
Powiązania:: https://bibliotekanauki.pl/articles/200233.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: anomaly prediction
Markov reward model
hybrid fuzzy-stochastic rules
temporal-difference learning for intrusion detection
przewidywanie anomalii
model Markova
wykrywanie włamań
hybrydowy algorytm stochastyczny
Opis:: Nowadays, information control systems based on databases develop dynamically worldwide. These systems are extensively implemented into dispatching control systems for railways, intrusion detection systems for computer security and other domains centered on big data analysis. Here, one of the main tasks is the detection and prediction of temporal anomalies, which could be a signal leading to significant (and often critical) actionable information. This paper proposes the new anomaly prevent detection technique, which allows for determining the predictive temporal structures. Presented approach is based on a hybridization of stochastic Markov reward model by using fuzzy production rules, which allow to correct Markov information based on expert knowledge about the process dynamics as well as Markov’s intuition about the probable anomaly occurring. The paper provides experiments showing the efficacy of detection and prediction. In addition, the analogy between new framework and temporal-difference learning for sequence anomaly detection is graphically illustrated.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 625-632
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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