A Bayesian Estimation of Confidence Limits for Multi-state System Vulnerability Assessment With IEMI
A Bayesian approach based on the vulnerability distribution is proposed to estimate the confidence limits of the state probability and the threat level of multistate electronic systems interfered by intentional electromagnetic interference (IEMI). The vulnerability distribution is used to describe t...
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Veröffentlicht in: | IEEE transactions on electromagnetic compatibility 2022-08, Vol.64 (4), p.1219-1229 |
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creator | Liu, Yu Du, Peibing Han, Feng Cai, Libing Qi, Hongxin Xia, Hongfu Wang, Jianguo |
description | A Bayesian approach based on the vulnerability distribution is proposed to estimate the confidence limits of the state probability and the threat level of multistate electronic systems interfered by intentional electromagnetic interference (IEMI). The vulnerability distribution is used to describe the state probability function of the multi-state system (MSS) for a given IEMI threat level. When a small number of test samples are under a specific threat level and prior MSS information is known, the posterior estimation of the state probability function can be obtained based on Bayesian theory. Furthermore, to effectively apply the state probability function to assess the vulnerability of the MSS, the uncertainty of the state probability function is discussed. Considering state probabilities under the same threat level as random variables, all these state probabilities also follow the Dirichlet distribution. Thus, the confidence limits of the posteriori state probability and the threat level can be inferred by the marginal distribution of the state probability, which is induced by combining the Dirichlet and vulnerability distributions. Finally, a case study is given to demonstrate the details of the calculation process of the vulnerability distribution, the state probability function, and the confidence limits under a lognormal distribution. |
doi_str_mv | 10.1109/TEMC.2022.3169820 |
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The vulnerability distribution is used to describe the state probability function of the multi-state system (MSS) for a given IEMI threat level. When a small number of test samples are under a specific threat level and prior MSS information is known, the posterior estimation of the state probability function can be obtained based on Bayesian theory. Furthermore, to effectively apply the state probability function to assess the vulnerability of the MSS, the uncertainty of the state probability function is discussed. Considering state probabilities under the same threat level as random variables, all these state probabilities also follow the Dirichlet distribution. Thus, the confidence limits of the posteriori state probability and the threat level can be inferred by the marginal distribution of the state probability, which is induced by combining the Dirichlet and vulnerability distributions. 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(IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-7f4659290942135c5b777073e98456f0d4f61ddade54334c6440d90350661cea3</citedby><cites>FETCH-LOGICAL-c293t-7f4659290942135c5b777073e98456f0d4f61ddade54334c6440d90350661cea3</cites><orcidid>0000-0003-4492-3766 ; 0000-0002-9457-9425 ; 0000-0002-7971-7842</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9772725$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9772725$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Yu</creatorcontrib><creatorcontrib>Du, Peibing</creatorcontrib><creatorcontrib>Han, Feng</creatorcontrib><creatorcontrib>Cai, Libing</creatorcontrib><creatorcontrib>Qi, Hongxin</creatorcontrib><creatorcontrib>Xia, Hongfu</creatorcontrib><creatorcontrib>Wang, Jianguo</creatorcontrib><title>A Bayesian Estimation of Confidence Limits for Multi-state System Vulnerability Assessment With IEMI</title><title>IEEE transactions on electromagnetic compatibility</title><addtitle>TEMC</addtitle><description>A Bayesian approach based on the vulnerability distribution is proposed to estimate the confidence limits of the state probability and the threat level of multistate electronic systems interfered by intentional electromagnetic interference (IEMI). The vulnerability distribution is used to describe the state probability function of the multi-state system (MSS) for a given IEMI threat level. When a small number of test samples are under a specific threat level and prior MSS information is known, the posterior estimation of the state probability function can be obtained based on Bayesian theory. Furthermore, to effectively apply the state probability function to assess the vulnerability of the MSS, the uncertainty of the state probability function is discussed. Considering state probabilities under the same threat level as random variables, all these state probabilities also follow the Dirichlet distribution. Thus, the confidence limits of the posteriori state probability and the threat level can be inferred by the marginal distribution of the state probability, which is induced by combining the Dirichlet and vulnerability distributions. Finally, a case study is given to demonstrate the details of the calculation process of the vulnerability distribution, the state probability function, and the confidence limits under a lognormal distribution.</description><subject>Assessment</subject><subject>Bayes methods</subject><subject>Bayesian analysis</subject><subject>Bayesian method</subject><subject>Confidence limits</subject><subject>Dirichlet problem</subject><subject>Electromagnetic interference</subject><subject>Electronic systems</subject><subject>EMP radiation effects</subject><subject>Estimation</subject><subject>intentional electromagnetic interference (IEMI)</subject><subject>multistate system</subject><subject>Probability</subject><subject>Probability density function</subject><subject>Random variables</subject><subject>Reliability theory</subject><subject>Statistical analysis</subject><subject>Uncertainty</subject><subject>vulnerability</subject><subject>Wires</subject><issn>0018-9375</issn><issn>1558-187X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwAYiNJdYpfsbxskQBKrViQXnsrDQZC1d5FNtZ5O9JVcRqNNK5dzQHoVtKFpQS_bAtNvmCEcYWnKY6Y-QMzaiUWUIz9XWOZoTQLNFcyUt0FcJ-WoVkfIbqJX4sRwiu7HARomvL6PoO9xbnfWddDV0FeO1aFwO2vceboYkuCbGMgN_GEKHFH0PTgS93rnFxxMsQIIQWuog_XfzGq2KzukYXtmwC3PzNOXp_Krb5S7J-fV7ly3VSMc1joqxIpWaaaMEol5XcKaWI4qAzIVNLamFTWtdlDVJwLqpUCFJrwiVJU1pByefo_tR78P3PACGafT_4bjppmCKcCka4nih6oirfh-DBmoOf_vajocQcZZqjTHOUaf5kTpm7U8YBwD-vlWKKSf4L_5hvJA</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Liu, Yu</creator><creator>Du, Peibing</creator><creator>Han, Feng</creator><creator>Cai, Libing</creator><creator>Qi, Hongxin</creator><creator>Xia, Hongfu</creator><creator>Wang, Jianguo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The vulnerability distribution is used to describe the state probability function of the multi-state system (MSS) for a given IEMI threat level. When a small number of test samples are under a specific threat level and prior MSS information is known, the posterior estimation of the state probability function can be obtained based on Bayesian theory. Furthermore, to effectively apply the state probability function to assess the vulnerability of the MSS, the uncertainty of the state probability function is discussed. Considering state probabilities under the same threat level as random variables, all these state probabilities also follow the Dirichlet distribution. Thus, the confidence limits of the posteriori state probability and the threat level can be inferred by the marginal distribution of the state probability, which is induced by combining the Dirichlet and vulnerability distributions. 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subjects | Assessment Bayes methods Bayesian analysis Bayesian method Confidence limits Dirichlet problem Electromagnetic interference Electronic systems EMP radiation effects Estimation intentional electromagnetic interference (IEMI) multistate system Probability Probability density function Random variables Reliability theory Statistical analysis Uncertainty vulnerability Wires |
title | A Bayesian Estimation of Confidence Limits for Multi-state System Vulnerability Assessment With IEMI |
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