Results 31 to 40 of about 7,371,142 (245)
Journal of Asian Architecture and Building Engineering, 2021 The occupational safety risk of high-altitude fall accident in architecture construction engineering is dynamically changeable. Considering these dynamic changes, how to make a qualitative and quantitative analysis of the changing trends of the ...
Xiao-Ping Bai, Yu-Hong Zhao
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Cerebral modeling and dynamic Bayesian networks [PDF]
Artificial Intelligence in Medicine, 2004 The understanding and the prediction of the clinical outcomes of focal or degenerative cerebral lesions, as well as the assessment of rehabilitation procedures, necessitate knowing the cerebral substratum of cognitive or sensorimotor functions. This is achieved by activation studies, where subjects are asked to perform a specific task while data of ...
Vincent Labatut +4 more
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Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network
Applied SciencesBrake systems are subjected to various factors such as wear and fatigue over a long period of time. They bring a great challenge to the reliability analysis of the braking system.
Yingjie Tian, Jing Wen, Shubin Zheng
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A Dynamic Risk Assessment Method for Deep-Buried Tunnels Based on a Bayesian Network
Geofluids, 2020 In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into ...
Yan Wang +5 more
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Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey
IEEE Access, 2021 Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference.
Pedro Shiguihara +2 more
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Independence Decomposition in Dynamic Bayesian Networks [PDF]
, 2007 Dynamic Bayesian networks are a special type of Bayesian network that explicitly incorporate the dimension of time. They can be distinguished into repetitive and non-repetitive networks. Repetitiveness implies that the set of random variables of the network and their independence relations are the same at each time step.
Flesch, I., Lucas, Peter
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Network intrusion intention analysis model based on Bayesian attack graph
Tongxin xuebao, 2020 Aiming at the problem of ignoring the impact of attack cost and intrusion intention on network security in the current network risk assessment model,in order to accurately assess the target network risk,a method of network intrusion intention analysis ...
Zhiyong LUO, Xu YANG, Jiahui LIU, Rui XU
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Learning dynamic Bayesian networks [PDF]
, 1998 Bayesian networks are a concise graphical formalism for describing probabilistic models. We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian networks. In many of the interesting models, beyond the simple linear dynamical system or hidden Markov model, the calculations required for inference are intractable.
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Bayesian Learning of Markov Network Structure [PDF]
, 2006 We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina +3 more
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HyPE: Online Hybrid Pseudo-Bayesian Estimation Method for S-ALOHA-Based Tactical FANETs
IEEE AccessSignificant challenges are involved in tactical flying ad-hoc network (FANET) missions because network environments are very dynamic. In addition, energy-efficient network operation is important in tactical FANETs owing to the limited capacity of the on ...
Jimin Jeon +7 more
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