{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:32:06Z","timestamp":1774524726972,"version":"3.50.1"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3149324","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:56:38Z","timestamp":1644267398000},"page":"15671-15680","source":"Crossref","is-referenced-by-count":18,"title":["Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7774-3780","authenticated-orcid":false,"given":"Jacob B.","family":"Cloete","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1600-6135","authenticated-orcid":false,"given":"Tinus","family":"Stander","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel N.","family":"Wilke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aeue.2017.01.002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2012.12.006"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/43.644035"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CAE.2019.8709294"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LATW.2018.8349689"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/5075103"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS51556.2021.9401388"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS.2018.8618052"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2005.11.008"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.14311\/NNW.2011.21.010"},{"issue":"6","key":"ref11","first-page":"349","article-title":"Analog filter diagnosis using the oscillation based method","volume":"63","author":"Litovski","year":"2012","journal-title":"J. Electr. Eng."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/VLSI-DAT.2014.6834867"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LATW.2009.4813794"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2010.2062750"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IOLTS.2019.8854371"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106612"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3088489"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP49548.2020.9163437"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/EWDTS.2019.8884395"},{"key":"ref21","volume-title":"Micro-Cap 12 Download","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref23","article-title":"Machine learning and pattern recognition","volume-title":"Information Science Statistics","author":"Bishop","year":"2006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85584-0_5"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref28","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"25","author":"Krizhevsky"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"1","key":"ref32","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref33","first-page":"245","article-title":"Preventing \u2018overfitting\u2019 of cross-validation data","volume-title":"Proc. ICML","volume":"97","author":"Ng"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2011.2163953"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09706148.pdf?arnumber=9706148","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T22:50:41Z","timestamp":1705531841000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9706148\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3149324","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}