{"id":"https://openalex.org/W7154580678","doi":"https://doi.org/10.1109/access.2026.3684750","title":"Spectral Normalization and SVD-Enhanced Deep Kernel Learning Gaussian Process for Loss Prediction Under Out-of-Distribution Conditions in DAB Converters","display_name":"Spectral Normalization and SVD-Enhanced Deep Kernel Learning Gaussian Process for Loss Prediction Under Out-of-Distribution Conditions in DAB Converters","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7154580678","doi":"https://doi.org/10.1109/access.2026.3684750"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3684750","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3684750","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3684750","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133826465","display_name":"Youngkeun Kim","orcid":"https://orcid.org/0009-0002-0139-3435"},"institutions":[{"id":"https://openalex.org/I146429904","display_name":"Incheon National University","ror":"https://ror.org/02xf7p935","country_code":"KR","type":"education","lineage":["https://openalex.org/I146429904"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Youngkeun Kim","raw_affiliation_strings":["Department of Artificial Intelligence, Incheon National University, Incheon, South Korea"],"raw_orcid":"https://orcid.org/0009-0002-0139-3435","affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, Incheon National University, Incheon, South Korea","institution_ids":["https://openalex.org/I146429904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087168370","display_name":"Han-Shin Youn","orcid":"https://orcid.org/0000-0002-7924-5136"},"institutions":[{"id":"https://openalex.org/I146429904","display_name":"Incheon National University","ror":"https://ror.org/02xf7p935","country_code":"KR","type":"education","lineage":["https://openalex.org/I146429904"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Han-Shin Youn","raw_affiliation_strings":["Department of Electrical Engineering, Incheon National University, Incheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-7924-5136","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Incheon National University, Incheon, South Korea","institution_ids":["https://openalex.org/I146429904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041449876","display_name":"Myoung Hoon Lee","orcid":"https://orcid.org/0000-0002-5105-4805"},"institutions":[{"id":"https://openalex.org/I146429904","display_name":"Incheon National University","ror":"https://ror.org/02xf7p935","country_code":"KR","type":"education","lineage":["https://openalex.org/I146429904"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Myoung Hoon Lee","raw_affiliation_strings":["Department of Electrical Engineering, Incheon National University, Incheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5105-4805","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Incheon National University, Incheon, South Korea","institution_ids":["https://openalex.org/I146429904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5133826465"],"corresponding_institution_ids":["https://openalex.org/I146429904"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77294039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"60501","last_page":"60516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.37299999594688416,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.37299999594688416,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10361","display_name":"Silicon Carbide Semiconductor Technologies","score":0.19040000438690186,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.04360000044107437,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7591000199317932},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6859999895095825},{"id":"https://openalex.org/keywords/converters","display_name":"Converters","score":0.6751999855041504},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5217000246047974},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5044000148773193},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4609000086784363},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41830000281333923},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.35499998927116394}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7591000199317932},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6859999895095825},{"id":"https://openalex.org/C2778422915","wikidata":"https://www.wikidata.org/wiki/Q10302051","display_name":"Converters","level":3,"score":0.6751999855041504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.650600016117096},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5217000246047974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5174000263214111},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46320000290870667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41830000281333923},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.32440000772476196},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2988995629","wikidata":"https://www.wikidata.org/wiki/Q2915729","display_name":"Matrix algebra","level":3,"score":0.29980000853538513},{"id":"https://openalex.org/C2983668108","wikidata":"https://www.wikidata.org/wiki/Q280453","display_name":"Spectral analysis","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2838999927043915},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C147788027","wikidata":"https://www.wikidata.org/wiki/Q2718101","display_name":"Band-pass filter","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C2988922011","wikidata":"https://www.wikidata.org/wiki/Q5449244","display_name":"Filtering theory","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3684750","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3684750","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:410683519b8249a88c6a96c398a824e1","is_oa":true,"landing_page_url":"https://doaj.org/article/410683519b8249a88c6a96c398a824e1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 60501-60516 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3684750","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3684750","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,233],"of":[2,15,66,155],"conduction":[3],"and":[4,13,28,55,82,94,104,121,161,190,196,227],"switching":[5],"losses":[6],"is":[7],"essential":[8],"for":[9,230],"optimizing":[10],"the":[11,63,67,125,129,143,152,166,169,213],"design":[12],"efficiency":[14],"dual":[16],"active":[17],"bridge":[18,165],"(DAB)":[19],"converters.":[20],"Conventional":[21],"physics-based":[22],"approaches":[23],"rely":[24],"on":[25,89],"expert":[26],"heuristics":[27],"conservative":[29],"margins,":[30],"whereas":[31],"data-driven":[32],"models":[33],"often":[34],"degrade":[35],"under":[36,193],"out-of-distribution":[37,106],"(OOD)":[38],"conditions.":[39],"This":[40],"paper":[41],"presents":[42],"a":[43,116,122,175,181,225],"deep":[44],"kernel":[45],"learning":[46],"Gaussian":[47],"process":[48],"(DKLGP)":[49],"model":[50,86,171,186],"enhanced":[51],"with":[52,115,220],"spectral":[53],"normalization":[54,61],"singular":[56],"value":[57],"decomposition":[58],"(SN\u2013DKLGP\u2013SVD).":[59],"Spectral":[60],"constrains":[62],"Lipschitz":[64],"continuity":[65],"feature":[68],"extractor":[69],"to":[70,79],"preserve":[71],"latent-space":[72],"geometry,":[73],"while":[74,136],"SVD":[75],"exploits":[76],"inter-output":[77],"correlations":[78],"reduce":[80],"redundancy":[81],"computational":[83,222],"cost.":[84],"The":[85,184],"was":[87,172],"trained":[88],"2,300":[90],"PLECS-generated":[91],"operating":[92],"points":[93],"evaluated":[95],"using":[96,174],"200":[97],"in-distribution":[98,195],"(R<sub":[99,107],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[100,108],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">DS(on)</sub>":[101,109],"10\u2013100":[102],"m\u03a9)":[103,111],"300":[105],"100\u2013140":[110],"test":[112],"samples.":[113],"Compared":[114],"feedforward":[117],"neural":[118],"network":[119],"(FFNN)":[120],"standard":[123],"DKLGP,":[124],"proposed":[126,214],"method":[127],"achieved":[128,151],"lowest":[130],"OOD":[131,146,200,218],"error":[132],"(MAPE":[133],"=":[134],"0.0241%)":[135],"maintaining":[137],"well-calibrated":[138,191],"uncertainty":[139,192],"estimates.":[140],"Even":[141],"in":[142,206,234],"most":[144],"challenging":[145],"region":[147],"(130\u2013140":[148],"m\u03a9),":[149],"it":[150],"minimum":[153],"MAE":[154],"0.0589":[156],"W,":[157],"demonstrating":[158],"strong":[159,217],"robustness":[160],"reliability.":[162],"To":[163],"further":[164],"simulation-to-real":[167],"gap,":[168],"pretrained":[170],"fine-tuned":[173,185],"small-scale":[176],"experimental":[177],"dataset":[178],"collected":[179],"from":[180],"DAB":[182],"prototype.":[183],"maintained":[187],"stable":[188],"accuracy":[189],"both":[194],"previously":[197],"unseen":[198],"device-level":[199],"conditions,":[201],"confirming":[202],"its":[203],"practical":[204],"applicability":[205],"real-world":[207],"environments.":[208],"These":[209],"results":[210],"demonstrate":[211],"that":[212],"framework":[215],"provides":[216],"generalization":[219],"high":[221],"efficiency,":[223],"offering":[224],"scalable":[226],"reliable":[228],"solution":[229],"high-fidelity":[231],"loss":[232],"power":[235],"electronic":[236],"systems.":[237]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-04-17T00:00:00"}
