Results 21 to 30 of about 39,041 (304)
Modeling of Wastewater Treatment Processes Using Dynamic Bayesian Networks Based on Fuzzy PLS
IEEE Access, 2020 The complicated characteristics of wastewater treatment plants (WWTPs) significantly hinder the monitoring of industrial processes, and thus much attention has been paid to process modeling and prediction.
Hongbin Liu +4 more
doaj +1 more source
Frontiers in Psychology, 2022 An overarching mission of the educational assessment community today is strengthening the connection between assessment and learning. To support this effort, researchers draw variously on developments across technology, analytic methods, assessment ...
Younyoung Choi, Robert J. Mislevy
doaj +1 more source
Non-homogeneous dynamic Bayesian networks for continuous data [PDF]
, 2011 : Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed.
Husmeier, D. +5 more
core +1 more source
Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
Applied Sciences, 2021 Outliers are observations suspected of not having been generated by the underlying process of the remaining data. Many applications require a way of identifying interesting or unusual patterns in multivariate time series (MTS), now ubiquitous in many ...
Jorge L. Serras +2 more
doaj +1 more source
Dynamic Bayesian inference method for structural fatigue crack propagation based on particle filter
Hangkong gongcheng jinzhan, 2023 Accurately predicting the fatigue crack propagation process of aircraft structure is the basis for conducting life monitoring and residual life estimation of individual aircraft.
QI Xin +4 more
doaj +1 more source
Asynchronous Dynamic Bayesian Networks
CoRR, 2012 Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves.
Avi Pfeffer, Terry Tai
openaire +3 more sources
Bayesian Inference of Stochastic Dynamical Networks
CoRR, 2022 Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks.
Yasen Wang +2 more
openaire +2 more sources
Searching multiregression dynamic models of resting-state fMRI networks using integer programming [PDF]
, 2015 A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the ...
Smith, Jim +9 more
core +1 more source
Applied Sciences, 2023 Urban roads face significant challenges from the unpredictable and destructive characteristics of natural or man-made disasters, emphasizing the importance of modeling and evaluating their resilience for emergency management. Resilience is the ability to
Gang Yu +3 more
doaj +1 more source
Articulatory feature recognition using dynamic Bayesian networks [PDF]
, 2007 We describe a dynamic Bayesian network for articulatory feature recognition. The model is intended to be a component of a speech recognizer that avoids the problems of conventional ``beads-on-a-string'' phoneme-based models. We demonstrate that the model
Simon King +8 more
core +1 more source