Results 21 to 30 of about 807,381 (349)
Frontiers in Energy Research, 2022 Knowledge-driven and data-driven methods are the two representative categories of intelligent technologies used in fault diagnosis in nuclear power plants.
Ben Qi +3 more
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A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Advanced Engineering Materials, EarlyView., 2023 Herein, a versatile and efficient beam modeling framework is developed to predict the nonlinear response and failure of cellular, truss‐based, and woven architected materials. It enables the exploration of their design space and the optimization of their mechanical behavior in the nonlinear regime. A variational formulation of a beam model is presented
Konstantinos Karapiperis +3 more
wiley +1 more source
Students' learning style detection using tree augmented naive Bayes [PDF]
Royal Society Open Science, 2018 Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity.
Ling Xiao Li, Siti Soraya Abdul Rahman
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Fault diagnosis of mine drainage system based on fuzzy Bayesian network
Gong-kuang zidonghua, 2022 The mine drainage system is developing towards automation and intelligence. The system's structure and function are becoming more and more complex, and the abnormal function and failure of a single component may cause the failure of the whole system. The
SHI Xiaojuan, YAO Bing, GU Huabei
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A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms
Discrete Dynamics in Nature and Society, 2022 In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based ...
Wei Zhou
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Smart Home IoT Network Risk Assessment Using Bayesian Networks
Entropy, 2022 A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different ...
Miguel Flores +3 more
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Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Charnock, Tom +2 more
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Bayesian Networks in Reliability [PDF]
Reliability Engineering & System Safety, 2007 Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. This article discusses the properties of the modeling framework that are of highest importance for reliability practitioners. Keywords:
Langseth, Helge, Jensen, Finn V.
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Integrating knowledge graph, complex network and Bayesian network for data-driven risk assessment
Chemical Engineering Transactions, 2022 Bayesian network is an effective method for quantitative risk assessment, but most existing studies are either heavily data-dependent or excessively expert-dependent.
Yiping Bai, Yuxuan Xing, Jiansong Wu
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Loop amplitudes from precision networks
SciPost Physics Core, 2023 Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably.
Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn
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