Results 51 to 60 of about 42,084 (260)
Prediction of fatigue crack propagation based on dynamic Bayesian network
Advances in Mechanical Engineering, 2022 To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper.
Wei Wang +4 more
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Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents
Promet (Zagreb), 2021 Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation ...
Chenyu Zhou +3 more
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Probabilistic Prognosis with Dynamic Bayesian Networks
International Journal of Prognostics and Health Management, 2015 This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats
Gregory Bartram, Sankaran Mahadevan
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Dependency Parsing with Dynamic Bayesian Network
CoRR, 2007 6 ...
Virginia Savova, Leonid Peshkin
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Multisensory integration using dynamical Bayesian networks [PDF]
Frontiers in Computational Neuroscience, 2015 Multisensory Integration (MSI) is the study of how information coming from different sensory modalities, such as vision, audition and etc. are being integrated by the nervous system (Stein et al., 2009) as a complex system. MSI is one of the most important aspects of neuroscience which has a great influence on our decision making system. It plays a key
Taher Abbas Shangari +3 more
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Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source
International Journal of Distributed Sensor Networks, 2016 This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model.
Pao-Kuan Wu, Tsung-Chih Hsiao, Ming Xiao
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