The Experts below are selected from a list of 47166 Experts worldwide ranked by ideXlab platform
Taixiong Zheng - One of the best experts on this subject based on the ideXlab platform.
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Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian Theory
Nonlinear Dynamics, 2016Co-Authors: Xiao Jiang, Hao Zhu, Srinivas Peeta, Taixiong ZhengAbstract:Considering the chaotic characteristics of traffic flow, this study proposes a Bayesian Theory-based multiple measures chaotic time series prediction algorithm. In particular, a time series of three traffic measures, i.e., speed, occupancy, and flow, obtained from different sources is used to reconstruct the phase space using the phase space reconstruction Theory. Then, data from the multiple sources are combined using Bayesian estimation Theory to identify the chaotic characteristics of traffic flow. In addition, a radial basis function (RBF) neural network is designed to predict the traffic flow. Compared to the consideration of a single source, results from numerical experiments demonstrate the improved effectiveness of the proposed multi-measure method in terms of accuracy and timeliness for the short-term traffic flow prediction.
Jie Lai - One of the best experts on this subject based on the ideXlab platform.
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Integrated assessment of concrete structure using Bayesian Theory and ultrasound tomography
Construction and Building Materials, 2021Co-Authors: Zi-rong Niu, Wei Wang, Xiaohan Huang, Jie LaiAbstract:Abstract Ultrasound tomography is an important nondestructive testing (NDT) technique that can be used to visualize the internal behavior of a concrete structure. Different ultrasound transmission parameters, such as travel time, wave attenuation, and wave frequency, have been implemented in both numerical and experimental applications with different accuracies. The primary aim of this study was to provide a more reliable assessment method using multiple ultrasound transmission parameters rather than a single information source. Therefore, two types of information sources, namely the travel time and wave attenuation, were integrated based on the Bayesian Theory to assess the internal defects of concrete structures. The advantage was that tomography could be performed without additional measurements. Both numerical and experimental studies on concrete specimens with different assumed defects were performed to validate the proposed approach. The results demonstrated that the proposed method has better accuracy than the single information source method.
Ziyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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Sequential Dependence Modeling Using Bayesian Theory and D-Vine Copula and Its Application on Chemical Process Risk Prediction
Industrial & Engineering Chemistry Research, 2014Co-Authors: Xiang Ren, Ziyang ZhangAbstract:An emerging kind of prediction model for sequential data with multiple time series is proposed. Because D-vine copula provides more flexibility in dependence modeling, accounting for conditional dependence, asymmetries, and tail dependence, it is employed to describe sequential dependence between variables in the sample data. A D-vine model with the form of a time window is created to fit the correlation of variables well. To describe the randomness dynamically, Bayesian Theory is also applied. As an application, a detailed modeling of prediction of abnormal events in a chemical process is given. Statistics (e.g., mean, variance, skewness, kurtosis, confidence interval, etc.) of the posterior predictive distribution are obtained by Markov chain Monte Carlo simulation. It is shown that the model created in this paper achieves a prediction performance better than that of some other system identification methods, e.g., autoregressive moving average model and back propagation neural network.
Xiao Jiang - One of the best experts on this subject based on the ideXlab platform.
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Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian Theory
Nonlinear Dynamics, 2016Co-Authors: Xiao Jiang, Hao Zhu, Srinivas Peeta, Taixiong ZhengAbstract:Considering the chaotic characteristics of traffic flow, this study proposes a Bayesian Theory-based multiple measures chaotic time series prediction algorithm. In particular, a time series of three traffic measures, i.e., speed, occupancy, and flow, obtained from different sources is used to reconstruct the phase space using the phase space reconstruction Theory. Then, data from the multiple sources are combined using Bayesian estimation Theory to identify the chaotic characteristics of traffic flow. In addition, a radial basis function (RBF) neural network is designed to predict the traffic flow. Compared to the consideration of a single source, results from numerical experiments demonstrate the improved effectiveness of the proposed multi-measure method in terms of accuracy and timeliness for the short-term traffic flow prediction.
Robert A. Canfield - One of the best experts on this subject based on the ideXlab platform.
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Comparison of evidence Theory and Bayesian Theory for uncertainty modeling
Reliability Engineering & System Safety, 2004Co-Authors: Prabhu Soundappan, Efstratios Nikolaidis, Raphael T. Haftka, Ramana V. Grandhi, Robert A. CanfieldAbstract:Abstract This paper compares Evidence Theory (ET) and Bayesian Theory (BT) for uncertainty modeling and decision under uncertainty, when the evidence about uncertainty is imprecise. The basic concepts of ET and BT are introduced and the ways these theories model uncertainties, propagate them through systems and assess the safety of these systems are presented. ET and BT approaches are demonstrated and compared on challenge problems involving an algebraic function whose input variables are uncertain. The evidence about the input variables consists of intervals provided by experts. It is recommended that a decision-maker compute both the Bayesian probabilities of the outcomes of alternative actions and their plausibility and belief measures when evidence about uncertainty is imprecise, because this helps assess the importance of imprecision and the value of additional information. Finally, the paper presents and demonstrates a method for testing approaches for decision under uncertainty in terms of their effectiveness in making decisions.
