Results 61 to 70 of about 5,728,670 (281)
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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Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference.
Yang Xiang, Hanwen Zheng
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dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
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Dependency Parsing with Dynamic Bayesian Network
CoRR, 2007 6 ...
Virginia Savova, Leonid Peshkin
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Dynamic Bayesian Networks for Musical Interaction [PDF]
, 2017 We describe in this chapter a consistent set of temporal models that we have developed over the years for analyzing movement in real-time musical interaction. These models are probabilistic and can be unified and generalized under the formalism of dynamic Bayesian networks (DBNs).
Caramiaux, Baptiste +2 more
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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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Transmembrane topology and signal peptide prediction using dynamic bayesian networks. [PDF]
PLoS Computational Biology, 2008 Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction.
Sheila M Reynolds +4 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
Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]
, 2009 Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D. +3 more
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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