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Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 80-98 doi: 10.1007/s11709-021-0682-3

Abstract: a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesianbelief network (BBN) approach based on an interpretive structural modeling technique.

Keywords: Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral    

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 490-505 doi: 10.1007/s11709-020-0669-5

Abstract: liquefaction potential of soil based on the cone penetration test field case history records using the Bayesianbelief network (BBN) learning software Netica.

Keywords: seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural    

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 414-428 doi: 10.1007/s11705-017-1663-x

Abstract: In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidificationcause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian

Keywords: abnormal aluminum electrolysis cell condition     Fuzzy-Bayesian network     multi-source knowledge solidification    

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1476-1491 doi: 10.1007/s11709-020-0670-z

Abstract: novel comprehensive framework based on select case history records of cone penetration tests using a Bayesianbelief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentialsCompared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction

Keywords: Bayesian belief network     liquefaction-induced damage potential     cone penetration test     soil liquefaction    

A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

Frontiers of Engineering Management 2017, Volume 4, Issue 3,   Pages 338-347 doi: 10.15302/J-FEM-2017032

Abstract: This study aims to solve a typical long-term strategic decision problem on supply chain network designnonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with BayesianA computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented

Keywords: supply chain network     stochastic demand     sampling average approximation     Bayesian bootstrap     Latin hypercube    

Online Monitoring of Welding Status Based on a DBN Model During Laser Welding Article

Yanxi Zhang, Deyong You, Xiangdong Gao, Seiji Katayama

Engineering 2019, Volume 5, Issue 4,   Pages 671-678 doi: 10.1016/j.eng.2019.01.016

Abstract: Based on these real-time quantized features of the welding process, a deep belief network (DBN) is establishedand robustness in monitoring welding status in comparison with a traditional back-propagation neural network

Keywords: Online monitoring     Multiple sensors     Wavelet packet decomposition     Deep belief network    

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian

Keywords: sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 483-489 doi: 10.15302/J-FEM-2017023

Abstract: Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled

Keywords: pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

Hybrid Bayesian Network Method for Predicting Intrusion

Wang Liangmin, Ma Jianfeng

Strategic Study of Chinese Academy of Engineering 2008, Volume 10, Issue 8,   Pages 87-96

Abstract: Secondly, a hybrid Bayesian network model based on this intrusion model is presented to show the casualIn this hybrid Bayesian network model, the connections of the same layer are continuous, but that ofThe algorithm for computing the joint probability distribution of the hybrid Bayesian network is presentedIn the end, the efficiency of the intrusion model and hybrid Bayesian network in predicting intrusionis shown by the experiment with our belief propagation algorithm, and the advantages of this predicting

Keywords: intrusion tolerance     alert correlation     intrusion model     intrusion prediction    

Controller area network node reliability assessment based on observable node information Article

Lei-ming ZHANG, Long-hao TANG, Yong LEI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 615-626 doi: 10.1631/FITEE.1601029

Abstract: Controller area network (CAN) based fieldbus technologies have been widely used in networked manufacturingThe method estimates the transmit error counter (TEC) of any node in the network based on the networkIt considers the sparseness of the distribution of the CAN network errors.learning the differences between the model estimates and the actual values from the observable node, a Bayesiannetwork is developed for the estimation updating mechanism of the observable nodes.

Keywords: Controller area network (CAN)     Transmit error counter (TEC)     TEC value estimation     Bayesian network    

Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network Research Articles

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1234-1246 doi: 10.1631/FITEE.2000426

Abstract: quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesiannetwork with .

Keywords: 软测量;有监督贝叶斯网络;隐变量;局部加权建模;质量预测    

Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of Chinese Academy of Engineering 2007, Volume 9, Issue 5,   Pages 77-80

Abstract: the optimized function,  an optimized L-M algorithm, which can accelerate the training of neural network Bayesian regularization can overcome the over fitting and improve the generalization of ANN.

Keywords: short-term load forecasting(STLF)     ANN     Levenberg-Marquardt     Bayesian regularization     optimized algorithms    

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving Article

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Engineering 2022, Volume 19, Issue 12,   Pages 228-239 doi: 10.1016/j.eng.2021.12.020

Abstract: DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesiannetwork.

Keywords: Autonomous driving     Dynamic Bayesian network     Driving intention recognition     Gaussian process     Vehicle trajectory    

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang,Bin Gong,Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract: Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulationThe application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic casesIn field applications, however, one of the main problems is that the Bayesian prior is falsified, becauseWe then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate

Keywords: Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition Article

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 978-988 doi: 10.1631/FITEE.1600996

Abstract: In the proposed model, deep belief networks are adopted to learn the compact representations of sequential

Keywords: Handwriting recognition     Hidden Markov models     Deep learning     Deep belief networks     Tandem approach    

Title Author Date Type Operation

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

Journal Article

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

Journal Article

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

Journal Article

Online Monitoring of Welding Status Based on a DBN Model During Laser Welding

Yanxi Zhang, Deyong You, Xiangdong Gao, Seiji Katayama

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

Journal Article

Hybrid Bayesian Network Method for Predicting Intrusion

Wang Liangmin, Ma Jianfeng

Journal Article

Controller area network node reliability assessment based on observable node information

Lei-ming ZHANG, Long-hao TANG, Yong LEI

Journal Article

Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network

Journal Article

Short-term Load Forecasting Using Neural Network

Luo Mei

Journal Article

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

Jinxin Liu, Yugong Luo, Zhihua Zhong, Keqiang Li, Heye Huang, Hui Xiong

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang,Bin Gong,Jef Caers

Journal Article

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Journal Article