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Decision Fusion System for Bolted Joint Monitoring

Dong Liang, Shen-fang Yuan
2015 Shock and Vibration  
Finally, three fusion algorithms, which consist of majority voting, Bayesian belief, and multiagent method, are adopted for comparison in a real-world monitoring experiment for the large aviation aluminum  ...  using a developed decision fusion system integrated with Lamb wave propagation based actuator-sensor monitoring method.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (Grant no. 51405409) and the Fundamental Research Funds for the Central Universities.  ... 
doi:10.1155/2015/592043 fatcat:ufzrw7isjbez3cy7p3ex7uvhkq

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Sami Kabir, Raihan Ul Islam, Mohammad Shahadat Hossain, Karl Andersson
2020 Sensors  
Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine.  ...  We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach.  ...  Acknowledgments: This research is based on the Master's Thesis [60] of Sami Kabir, conducted at the Pervasive and Mobile Computing Laboratory, Luleå University of Technology, Skellefteå, Sweden.  ... 
doi:10.3390/s20071956 pmid:32244380 fatcat:efkzbpmr6rgapeqm62qz7otksy

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

Raihan Ul Islam, Xhesika Ruci, Mohammad Shahadat Hossain, Karl Andersson, Ah-Lian Kor
2019 Energies  
This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center.  ...  The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE),  ...  Acknowledgments: This research was conducted as part of the masters thesis [53] of Xhesika Ruci at the Pervasive and Mobile Computing Laboratory, Luleå University of Technology, Sweden.  ... 
doi:10.3390/en12183438 fatcat:axwoqcau5zelhaz2ymf6so345e

Epistemic Uncertainty Quantification of Seismic Damage Assessment [chapter]

Hesheng Tang, Dawei Li, Songtao Xue
2017 Uncertainty Quantification and Model Calibration  
of the propagated belief structure in a system with evidence theory is presented here.  ...  In this work, a methodology based on the evidence theory is presented for quantifying the epistemic uncertainty of damage assessment procedure.  ...  response within each joint interval and construct corresponding joint belief structures. • Given the complete BBA on the output response of interest damage index D, the belief and plausibility functions  ... 
doi:10.5772/intechopen.68153 fatcat:57kziqljircqpfpitmng7ihagi

Constrained Markov Bayesian Polynomial for Efficient Dialogue State Tracking

Kai Yu, Kai Sun, Lu Chen, Su Zhu
2015 IEEE/ACM Transactions on Audio Speech and Language Processing  
Although data-driven statistical approaches are of most interest, there have been attempts of using rule-based methods for DST, due to their simplicity, efficiency and portability.  ...  In this paper, a novel hybrid framework, constrained Markov Bayesian polynomial (CMBP), is proposed to formulate rule-based DST in a general way and allow data-driven rule generation.  ...  In DSTC-1, the simple rule-based system outperformed many discriminative models and was ranked the 5th in the joint goal tracking task on Test3.  ... 
doi:10.1109/taslp.2015.2470597 fatcat:sk5sabthpndlxfb6ra26t3hcau

Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning [article]

Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, Philippe Rigo
2022 arXiv   pre-print
In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure  ...  The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.  ...  Papakonstantinou would further like to acknowledge that this material is also based upon work supported by the U.S. National Science Foundation under Grant No. 1751941. Dr.  ... 
arXiv:2209.01092v1 fatcat:g5yqrpvb3fbbbnkbbm7sp3rhh4

Satisfaction of assumptions is a weak predictor of performance

1988 International Journal of Approximate Reasoning  
This paper demonstrates a methodology for examining the accuracy of uncertain inference systems (UIS) after their parameters have been optimized, and uses it for several common UISs.  ...  Abstracts 345 Much of the controversy about methods for automated decision making has focused on specific calculi for combining beliefs or propagating uncertainty.  ...  Much of the controversy about methods for automated decision making has focused on specific calculi for combining beliefs or propagating uncertainty.  ... 
doi:10.1016/0888-613x(88)90168-5 fatcat:z3h3g5ddm5gf7bg2ystmf6ik4u

An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with MDL Restriction

Shuisheng Ye, Hong Cai, Rongguan Sun
2008 2008 Fourth International Conference on Natural Computation  
Finally, proposed algorithm compared with other structure learning algorithms based on classification accuracy and construction time on valuable databases.  ...  In this paper, we introduced Simulated Annealing algorithm with complete details as new method for BBNs structure learning.  ...  Bayesian Belief Networks: Bayesian belief networks or BBN is named based on studies of Thomas Bayes (1702-and the parameter learning is a method for learning in 1761) in the field of probability theory  ... 
doi:10.1109/icnc.2008.658 dblp:conf/icnc/YeCS08 fatcat:72nhkekqmzhyhgn27djeegc7zy

Distributed inference in wireless sensor networks

V. V. Veeravalli, P. K. Varshney
2011 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the  ...  Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized  ...  The authors would like to thank Dr Engin Masazade for his help with the preparation of this paper.  ... 
doi:10.1098/rsta.2011.0194 pmid:22124084 fatcat:jtwubvrlnnfgpkcu46jvqim3ra

A Deep Learning Inspired Belief Rule-based Expert System

Raihan Ul Islam, Mohammad Shahadat Hossain, Karl Andersson
2020 IEEE Access  
Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data.  ...  from Beijing city and the power generation dataset of a combined cycle power plant.  ...  [25] proposed a joint optimisation method for BRBES by taking into account of both structure and learning parameters.  ... 
doi:10.1109/access.2020.3031438 fatcat:liclyxskynbfzb4bvfztndgloe

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Wei He, Chuan-Qiang Yu, Guo-Hui Zhou, Zhi-Jie Zhou, Guan-Yu Hu
2019 IEEE Access  
In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB).  ...  The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters.  ...  [17] proposed a fault diagnosis and prediction method for complex systems based on a hidden Markov model.  ... 
doi:10.1109/access.2019.2922677 fatcat:4p6ugdjjmbfhbf6wap5cgbqd2i

Optimal Inspection and Maintenance Planning for Deteriorating Structures through Dynamic Bayesian Networks and Markov Decision Processes [article]

P. G. Morato, K.G. Papakonstantinou, C.P. Andriotis, J.S. Nielsen, P. Rigo
2020 arXiv   pre-print
In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite  ...  The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for  ...  ..., o t ) (8) Based on the gathered observations, the beliefs are updated by applying Bayes' rule.  ... 
arXiv:2009.04547v1 fatcat:oss53ootzbhpxeqeoksakjllha

Statistical Dialogue Management using Intention Dependency Graph

Koichiro Yoshino, Shinji Watanabe, Jonathan Le Roux, John R. Hershey
2013 International Joint Conference on Natural Language Processing  
In this way, we combine the deterministic graph structure of a conventional rule-based system with a statistical dialogue framework.  ...  The IDG also provides a reasonable constraint on a user simulation model, which is used when learning a policy function in POMDP and dialogue evaluation.  ...  Belief update We consider a belief update equation based on the graphical model shown in Figure 2 , assuming that the system actions a 1:t are given.  ... 
dblp:conf/ijcnlp/YoshinoWRH13 fatcat:m5ae7w6ubvab3hgfwku2ditcgi

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model

Sam Hamzeloo, Mansoor Zolghadri
2018 International Journal of Advanced Computer Science and Applications  
This method employs a fuzzy model to estimate the benefit of communication for each possible situation. This specifies minimal communication that is necessary for successful joint behavior.  ...  In this paper, we introduce an algorithm to develop a communication strategy for cooperative multi-agent systems in which the communication is limited.  ...  tune the structure and parameters of a fuzzy classifier.  ... 
doi:10.14569/ijacsa.2018.090822 fatcat:rwld75dp7jhd5bnv5nlj7tdrae

Bayesian learning theory applied to human cognition

Robert A. Jacobs, John K. Kruschke
2010 Wiley Interdisciplinary Reviews: Cognitive Science  
Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity.  ...  Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task.  ...  Dayan and C. Kemp for commenting on earlier versions of this manuscript. This work was supported by NSF research grant DRL-0817250.  ... 
doi:10.1002/wcs.80 pmid:26301909 fatcat:hzpseinb55fz5edlwoco2sn3bu
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