{"id":"https://openalex.org/W7133294772","doi":"https://doi.org/10.1109/access.2026.3670058","title":"AcousticMoEKAN: Sparse Mixture-of-KAN Experts for Time\u2013Frequency Forecasting of OLTC Acoustic Signals","display_name":"AcousticMoEKAN: Sparse Mixture-of-KAN Experts for Time\u2013Frequency Forecasting of OLTC Acoustic Signals","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7133294772","doi":"https://doi.org/10.1109/access.2026.3670058"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3670058","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3670058","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2026.3670058","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101933714","display_name":"Donghyun Kim","orcid":"https://orcid.org/0000-0002-7325-8585"},"institutions":[{"id":"https://openalex.org/I21028721","display_name":"Eulji University","ror":"https://ror.org/005bty106","country_code":"KR","type":"education","lineage":["https://openalex.org/I21028721"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Donghyun Kim","raw_affiliation_strings":["Department of Medical Artificial Intelligent, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Artificial Intelligent, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I21028721"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028325205","display_name":"Jimyung Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I196471810","display_name":"Korea Electrotechnology Research Institute","ror":"https://ror.org/03ctacd45","country_code":"KR","type":"nonprofit","lineage":["https://openalex.org/I196471810"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jimyung Kang","raw_affiliation_strings":["Korea Electrotechnology Research Institute, Ansan-si, Gyeonggi-do, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-5922-4434","affiliations":[{"raw_affiliation_string":"Korea Electrotechnology Research Institute, Ansan-si, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I196471810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049807325","display_name":"Yeji Hyun","orcid":null},"institutions":[{"id":"https://openalex.org/I144648050","display_name":"Yong In University","ror":"https://ror.org/035qaq447","country_code":"KR","type":"education","lineage":["https://openalex.org/I144648050"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yeji Hyun","raw_affiliation_strings":["MICS Platforms, Yongin-si, Gyeonggi-do, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0003-7566-1137","affiliations":[{"raw_affiliation_string":"MICS Platforms, Yongin-si, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I144648050"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110082823","display_name":"H.C. Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I21028721","display_name":"Eulji University","ror":"https://ror.org/005bty106","country_code":"KR","type":"education","lineage":["https://openalex.org/I21028721"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hochul Kim","raw_affiliation_strings":["Department of Radiological Science, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-6090-9537","affiliations":[{"raw_affiliation_string":"Department of Radiological Science, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I21028721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101933714"],"corresponding_institution_ids":["https://openalex.org/I21028721"],"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":0.36759172,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"40114","last_page":"40136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.15690000355243683,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.15690000355243683,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.09830000251531601,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.09120000153779984,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.8988999724388123},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.5918999910354614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5526999831199646},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5455999970436096},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.5404000282287598},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4406999945640564},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4404999911785126},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4327000081539154},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.412200003862381}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8988999724388123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6601999998092651},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.5918999910354614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5526999831199646},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5455999970436096},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.5404000282287598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47859999537467957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.39489999413490295},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3912000060081482},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.3808000087738037},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3670058","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3670058","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c2bba27e6834449bba099be2eecddfa5","is_oa":false,"landing_page_url":"https://doaj.org/article/c2bba27e6834449bba099be2eecddfa5","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 40114-40136 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3670058","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3670058","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3963487752","display_name":null,"funder_award_id":"No. 2022202090010A","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"},{"id":"https://openalex.org/G5376705139","display_name":null,"funder_award_id":"2022202090010A","funder_id":"https://openalex.org/F4320335199","funder_display_name":"Korea Institute of Energy Technology Evaluation and Planning"},{"id":"https://openalex.org/G652899675","display_name":null,"funder_award_id":"No. 2022202090010A","funder_id":"https://openalex.org/F4320335199","funder_display_name":"Korea Institute of Energy Technology Evaluation and Planning"}],"funders":[{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"},{"id":"https://openalex.org/F4320335199","display_name":"Korea Institute of Energy Technology Evaluation and Planning","ror":"https://ror.org/02zq38y32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1409921370","https://openalex.org/W2100652790","https://openalex.org/W2803329755","https://openalex.org/W2940704786","https://openalex.org/W2950114214","https://openalex.org/W2961621345","https://openalex.org/W2968047354","https://openalex.org/W3009350560","https://openalex.org/W3031085662","https://openalex.org/W3045563552","https://openalex.org/W3049546269","https://openalex.org/W3098270842","https://openalex.org/W3131799324","https://openalex.org/W4294833347","https://openalex.org/W4306317966","https://openalex.org/W4316038821","https://openalex.org/W4360983672","https://openalex.org/W4375862915","https://openalex.org/W4382203079","https://openalex.org/W4385665964","https://openalex.org/W4387005249","https://openalex.org/W4387819594","https://openalex.org/W4390467845","https://openalex.org/W4393353234","https://openalex.org/W4401768216","https://openalex.org/W4402482275","https://openalex.org/W4403834410","https://openalex.org/W4404258856","https://openalex.org/W4404268360","https://openalex.org/W4404527247","https://openalex.org/W4404528076","https://openalex.org/W4405374406","https://openalex.org/W4407307115","https://openalex.org/W4407507408","https://openalex.org/W4408358637","https://openalex.org/W4410640883","https://openalex.org/W4411142862","https://openalex.org/W4413181943","https://openalex.org/W4414472695","https://openalex.org/W4417050048"],"related_works":[],"abstract_inverted_index":{"On-load":[0],"tap-changers":[1],"(OLTCs)":[2],"regulate":[3],"distribution":[4],"voltage":[5],"and":[6,58,94,130,147,161,172,192,203],"undergo":[7],"frequent":[8],"mechanical":[9],"operations":[10],"that":[11,77,121,199],"can":[12],"accelerate":[13],"wear.":[14],"Acoustic":[15],"monitoring":[16],"is":[17,28,45,61],"non-intrusive,":[18],"but":[19],"forecasting":[20,215],"abnormal":[21],"behavior":[22],"directly":[23],"from":[24,82],"44.1":[25],"kHz":[26],"waveforms":[27],"computationally":[29],"prohibitive":[30],"for":[31,105,182,185,211],"multi-step":[32],"prediction.":[33],"We":[34,67],"formulate":[35],"OLTC":[36,212],"acoustic":[37,213],"prognostics":[38],"as":[39],"time\u2013frequency":[40],"forecasting:":[41],"each":[42],"tap-changing":[43,118],"event":[44],"converted":[46],"to":[47,62,90,170,176],"a":[48,112],"short-time":[49],"Fourier":[50],"transform":[51],"(STFT)":[52],"magnitude":[53],"spectrogram":[54,65],"with":[55,99,187],"513":[56],"frequency-bins,":[57],"the":[59,79,83,127,177,217],"task":[60],"predict":[63],"future":[64],"evolution.":[66],"propose":[68],"AcousticMoEKAN,":[69],"an":[70,208],"acoustic-optimized":[71],"mixture":[72],"of":[73],"Kolmogorov\u2013Arnold":[74],"networks":[75,140,144],"(KAN)":[76],"expands":[78],"expert":[80,108],"pool":[81],"four":[84,131],"analytic":[85],"bases":[86],"used":[87],"in":[88,216],"MoEKAN":[89,171],"ten":[91],"complementary":[92],"experts":[93],"replaces":[95],"dense":[96],"Transformer":[97],"gating":[98,104,206],"sparse":[100,204],"Mixture-of-Block":[101],"Attention":[102],"(MoBA)":[103],"efficient":[106],"top\u2011k":[107],"routing.":[109],"Experiments":[110],"on":[111],"large-scale":[113],"accelerated-aging":[114],"dataset":[115],"comprising":[116],"195,994":[117],"events":[119],"show":[120],"AcousticMoEKAN":[122,150],"improves":[123],"accuracy":[124],"averaged":[125],"over":[126],"DC":[128],"component":[129],"equal-width":[132],"frequency":[133,194],"bands,":[134],"outperforming":[135],"linear":[136],"models,":[137],"convolutional":[138],"neural":[139,143],"(CNNs),":[141],"graph":[142],"(GNNs),":[145],"Transformers,":[146],"KAN":[148,201],"baselines.":[149],"reduces":[151],"average":[152],"mean":[153,157,162],"squared":[154],"error":[155,159,165],"(MSE),":[156],"absolute":[158,163],"(MAE),":[160],"percentage":[164],"(MAPE)":[166],"by":[167,173],"20.6%/9.5%/11.6%":[168],"relative":[169,175],"25.1%/11.0%/12.0%":[174],"best":[178],"non-KAN":[179],"baselines":[180],"(RLinear":[181],"MSE,":[183],"STID":[184],"MAE/MAPE),":[186],"consistent":[188],"gains":[189],"across":[190],"low":[191],"high":[193],"bands.":[195],"These":[196],"results":[197],"indicate":[198],"expert-augmented":[200],"modeling":[202],"MoBA":[205],"provide":[207],"effective":[209],"framework":[210],"condition":[214],"STFT":[218],"domain.":[219]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-03-04T00:00:00"}