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Evidential Networks for Evaluating Predictive Reliability of Mechatronics Systems under Epistemic Uncertainties

Nabil B. Amrani, Laurent Saintis, Driss Sarsri, Mihaela Barreau
2019 Journal of KONBiN  
complex systems with high-reliability precision such as mechatronic systems, uncertainties are inevitable and must be considered in order to predict with a degree of confidence the evaluated reliability  ...  The key point in this study is to use an Evidential Network "EN" based on belief functions and the dynamic Bayesian network.  ...  Later on, Duy [16] used belief functions in the form of p-boxes along with Monte-Carlo simulations to model propagation of uncertainty.  ... 
doi:10.2478/jok-2019-0045 fatcat:cghfor65avgndmitzjftywo5i4

Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

Rui Kang, Qingyuan Zhang, Zhiguo Zeng, Enrico Zio, Xiaoyang Li
2016 Chinese Journal of Aeronautics  
In this paper, a systematic review of non-probabilistic reliability metrics is conducted to assist the selection of appropriate reliability metrics to model the influence of epistemic uncertainty.  ...  metrics caused by epistemic uncertainty, and (2) satisfy the duality axiom, otherwise it might lead to paradoxical and confusing results in engineering applications.  ...  Acknowledgements This work has been performed within the initiative of the Center for Resilience and Safety of Critical Infrastructures (CRESCI, http://cresci.buaa.edu.cn).  ... 
doi:10.1016/j.cja.2016.04.004 fatcat:laxfvebzobfu7lm5gdm7fsf3ty

Special section on ``Applications of probabilistic graphical models in dependability, diagnosis and prognosis''

Philippe Weber, Luigi Portinale
2017 Reliability Engineering & System Safety  
This hybrid method uses belief functions to model and manipulate uncertainty. P-boxes are used to represent basic uncertainties and evidential network to model the system reliability.  ...  System and component reliability are computed based on Dynamic Bayesian Network.  ... 
doi:10.1016/j.ress.2017.04.017 fatcat:xz3c4s62fzattc3trrlgkpio5e

Application of evidential networks in quantitative analysis of railway accidents

Felipe Aguirre, Mohamed Sallak, Walter Schön, Fabien Belmonte
2013 Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability  
The proposed paper presents an original method to account for the human factor by using Evidential Networks and fault tree analysis.  ...  However, human reliability data are very difficult to quantify, thus, qualitative methods are often used in railway system's risk assessments.  ...  BASICS OF VALUATION-BASED SYSTEMS (VBS) AND EVIDENTIAL NETWORKS (ENS The VBSs were introduced by Shenoy in 1989 as general frameworks for managing uncertainty in expert systems [She89] .  ... 
doi:10.1177/1748006x12475044 fatcat:wwvnedi35jhwvk4yix7alde4tm

Lymphoma segmentation from 3D PET-CT images using a deep evidential network [article]

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux
2022 arXiv   pre-print
The evidential layer then uses prototypes in the feature space to compute a belief function at each voxel quantifying the uncertainty about the presence or absence of a lymphoma at this location.  ...  The architecture is composed of a deep feature-extraction module and an evidential layer.  ...  It was carried out in the framework of the Labex MS2T, which was funded by the French Government, through the program "Investments for the future" managed by the National Agency for Research (Reference  ... 
arXiv:2201.13078v2 fatcat:a3s45vbcrjfknbaz2tfg6sungq

Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning

Bart Verheij, Floris Bex, Sjoerd T. Timmer, Charlotte S. Vlek, John-Jules Ch. Meyer, Silja Renooij, Henry Prakken
2015 Law, Probability and Risk  
We discuss a hybrid model that connects arguments and scenarios, a method to probabilistically model possible scenarios in a Bayesian network, a method to extract arguments from a Bayesian network, and  ...  These results have been produced as parts of research projects on the formal and computational modelling of evidence.  ...  Acknowledgments The research reported in this paper has been performed in the context of the project 'Designing and Understanding Forensic Bayesian Networks with Arguments and Scenarios', funded in the  ... 
doi:10.1093/lpr/mgv013 fatcat:x3upvmnbnbh4fdqw7qpimby2iq

Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors

Qing-Hua Zhang, Qin Hu, Guoxi Sun, Xiaosheng Si, Aisong Qin
2013 International Journal of Distributed Sensor Networks  
and evidential theory.  ...  Along with the continuous development of science and technology, the structures of rotating machinery become of larger scale, of higher speed, and more complicated, which results in higher probability  ...  Acknowledgments This work was partially supported by the NSFC under Grant 61174113 and the Natural Science Fund of Guangdong Province under Grant S2011020002735.  ... 
doi:10.1155/2013/472675 fatcat:kju5n5kty5grlm5bf4h7eft43e

Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning*

Zhiguo Zhou, Liyuan Chen, David Sher, Qiongwen Zhang, Jennifer Shah, Nhat-Long Pham, Steve Jiang, Jing Wang
2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
We proposed a hybrid predictive model that combines many-objective radiomics (MO-radiomics) and 3-dimensional convolutional neural network (3D-CNN) through evidential reasoning (ER) approach.  ...  Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimization.  ...  Acknowledgment This work was supported in part by the American Cancer Society (ACS-IRG-02-196) and the US National Institutes of Health (R01 EB020366).  ... 
doi:10.1109/embc.2018.8513070 pmid:30440295 pmcid:PMC7103090 fatcat:finvjiwxnrfjpn5alku3ioqnqe

Improving MC-Dropout Uncertainty Estimates with Calibration Error-based Optimization [article]

Afshar Shamsi, Hamzeh Asgharnezhad, Moloud Abdar, AmirReza Tajally, Abbas Khosravi, Saeid Nahavandi, Henry Leung
2021 arXiv   pre-print
Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.  ...  Our results confirmed the great impact of the new hybrid loss functions for minimising the overlap between the distributions of uncertainty estimates for correct and incorrect predictions without sacrificing  ...  However, for Bayesian Neural Networks with thousands of parameters, computing the posterior is complicated because of the complexity in computing the marginal likelihood [7] .  ... 
arXiv:2110.03260v1 fatcat:bwd332nszrgkvfprncxmil7uci

Intensional Cyberforensics [article]

Serguei A. Mokhov
2014 arXiv   pre-print
This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach.  ...  This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence  ...  Uncertainty, Evidence, and Credibility The works reviewed in this section contribute to the enhancement of the cyberforensic analysis with options to automatically reason in the presence of uncertainty  ... 
arXiv:1312.0466v2 fatcat:q5ovtsdzt5hqbn7rxpiobhjgzi

Assessing the global and local uncertainty in scientific evidence in the presence of model misspecification [article]

Mark L. Taper and Subhash R Lele and José-Miguel Ponciano, Brian Dennis, Christopher L Jerde
2021 arXiv   pre-print
We characterize this uncertainty in the strength of evidence with two different types of confidence intervals, which we term "global" and "local".  ...  We discuss how evidence uncertainty can be used to improve scientific inference and illustrate this with a reanalysis of the model identification problem in a prominent landscape ecology study (Grace and  ...  Box 2 Global and local intervals in Mark/Recapture analysis In ecology, where uncertainty in the study systems is ubiquitous, it is common practice to formulate a scientific hypothesis in the form of a  ... 
arXiv:1911.06421v2 fatcat:sy4hgzevtfc57oibel2aexnsny

The Powerful Use of AI in the Energy Sector: Intelligent Forecasting [article]

Erik Blasch, Haoran Li, Zhihao Ma, Yang Weng
2021 arXiv   pre-print
AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model.  ...  To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing  ...  Acknowledgments The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or  ... 
arXiv:2111.02026v1 fatcat:c6vcwtv7wzgyzisbdt77oa3qsy

Assessing the Global and Local Uncertainty of Scientific Evidence in the Presence of Model Misspecification

Mark L. Taper, Subhash R. Lele, José M. Ponciano, Brian Dennis, Christopher L. Jerde
2021 Frontiers in Ecology and Evolution  
We characterize this uncertainty in the strength of evidence with two different types of confidence intervals, which we term "global" and "local."  ...  We discuss how evidence uncertainty can be used to improve scientific inference and illustrate this with a reanalysis of the model identification problem in a prominent landscape ecology study using structural  ...  A worked example of global and local intervals in a mark recapture analysis can be found in Box 2. BOX 2. Global and local intervals in mark/recapture analysis.  ... 
doi:10.3389/fevo.2021.679155 fatcat:czixpg7bhradjick26ie3xekau

Basic Framework and Main Methods of Uncertainty Quantification

Juan Zhang, Junping Yin, Ruili Wang
2020 Mathematical Problems in Engineering  
, surrogate model, and model uncertainty analysis.  ...  Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry.  ...  TZ2018001), and the Development Program for Defense Ministry of China (Grant no. C1520110002).  ... 
doi:10.1155/2020/6068203 fatcat:im4p2xr5jzdfnh63mlwywnullu

Multisensor data fusion: A review of the state-of-the-art

Bahador Khaleghi, Alaa Khamis, Fakhreddine O. Karray, Saiedeh N. Razavi
2013 Information Fusion  
In addition, several future directions of research in the data fusion community are highlighted and described.  ...  There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application.  ...  Belief functions theory is a popular method to deal with uncertainty and imprecision with a theoretically attractive evidential reasoning framework.  ... 
doi:10.1016/j.inffus.2011.08.001 fatcat:42ca63cpqzea3o2w7wuzwwvy7e
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