{"id":"https://openalex.org/W4390188390","doi":"https://doi.org/10.1109/icdl55364.2023.10364543","title":"Enhancing Performance of Multi-Input Neural Networks Using Hadamard Product","display_name":"Enhancing Performance of Multi-Input Neural Networks Using Hadamard Product","publication_year":2023,"publication_date":"2023-11-09","ids":{"openalex":"https://openalex.org/W4390188390","doi":"https://doi.org/10.1109/icdl55364.2023.10364543"},"language":"en","primary_location":{"id":"doi:10.1109/icdl55364.2023.10364543","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icdl55364.2023.10364543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Development and Learning (ICDL)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063375705","display_name":"W.K. Kim","orcid":"https://orcid.org/0009-0001-5930-9892"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won-Joong Kim","raw_affiliation_strings":["Yonsei University,Mechanical Engineering,Seoul,Korea","Mechanical Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University,Mechanical Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Mechanical Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103026472","display_name":"Inwoo Kim","orcid":"https://orcid.org/0000-0001-9334-0124"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Inwoo Kim","raw_affiliation_strings":["Yonsei University,Mechanical Engineering,Seoul,Korea","Mechanical Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University,Mechanical Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Mechanical Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084268081","display_name":"Minsoo Lee","orcid":"https://orcid.org/0000-0002-7189-2494"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minsoo Lee","raw_affiliation_strings":["Yonsei University,Mechanical Engineering,Seoul,Korea","Mechanical Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University,Mechanical Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Mechanical Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102739849","display_name":"Soo-Hong Lee","orcid":"https://orcid.org/0000-0003-2168-642X"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soo-Hong Lee","raw_affiliation_strings":["Yonsei University,Mechanical Engineering,Seoul,Korea","Mechanical Engineering, Yonsei University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yonsei University,Mechanical Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Mechanical Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17324746,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"139","last_page":"143"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10784","display_name":"Muscle activation and electromyography studies","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/hadamard-transform","display_name":"Hadamard transform","score":0.844491183757782},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7275654077529907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7171474695205688},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5024068355560303},{"id":"https://openalex.org/keywords/exoskeleton","display_name":"Exoskeleton","score":0.46676206588745117},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4623177647590637},{"id":"https://openalex.org/keywords/hadamard-matrix","display_name":"Hadamard matrix","score":0.45124244689941406},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4193493127822876},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4160096049308777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3955671191215515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3932323455810547},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33278796076774597},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.20113199949264526},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1342354416847229},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12612992525100708}],"concepts":[{"id":"https://openalex.org/C60292330","wikidata":"https://www.wikidata.org/wiki/Q1014065","display_name":"Hadamard transform","level":2,"score":0.844491183757782},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7275654077529907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7171474695205688},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5024068355560303},{"id":"https://openalex.org/C146549078","wikidata":"https://www.wikidata.org/wiki/Q191944","display_name":"Exoskeleton","level":2,"score":0.46676206588745117},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4623177647590637},{"id":"https://openalex.org/C30463267","wikidata":"https://www.wikidata.org/wiki/Q1422682","display_name":"Hadamard matrix","level":3,"score":0.45124244689941406},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4193493127822876},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4160096049308777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3955671191215515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3932323455810547},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33278796076774597},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.20113199949264526},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1342354416847229},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12612992525100708},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdl55364.2023.10364543","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icdl55364.2023.10364543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Development and Learning (ICDL)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2109606373","https://openalex.org/W2116261113","https://openalex.org/W2949197413","https://openalex.org/W2963037989","https://openalex.org/W3168867926","https://openalex.org/W4289293306","https://openalex.org/W4295129979","https://openalex.org/W4298128217","https://openalex.org/W4378509449","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2038981428","https://openalex.org/W3154819821","https://openalex.org/W1976224891","https://openalex.org/W2768335563","https://openalex.org/W2740724818","https://openalex.org/W1971692815","https://openalex.org/W2054788067","https://openalex.org/W3118766198","https://openalex.org/W2010617738","https://openalex.org/W1995950135"],"abstract_inverted_index":{"Nowadays,":[0],"neural":[1,48,185],"networks":[2],"are":[3],"being":[4],"used":[5,82],"in":[6,40,71,155,170,183],"a":[7,23,31,204],"lot":[8,24],"of":[9,25,105,126,181,190,206],"different":[10,137,142,208],"fields.":[11],"However,":[12],"using":[13,55,140],"them":[14],"on":[15],"mobile":[16],"devices":[17],"is":[18,37,91,160],"tricky":[19],"as":[20],"they":[21],"need":[22],"computing":[26],"power.":[27],"This":[28],"can":[29,96,101,118],"be":[30],"problem":[32],"when":[33],"fast":[34],"reaction":[35],"time":[36,109],"required,":[38],"especially":[39],"an":[41],"exoskeleton.":[42],"In":[43,173],"this":[44,199],"research,":[45],"we":[46,65,81,175],"train":[47],"network":[49,186],"models":[50],"to":[51,67,85,120,162],"predict":[52],"ankle":[53],"movement":[54],"sensor":[56],"data":[57,134,156],"from":[58,135,203],"the":[59,63,69,87,103,127,150,171,179,184,188,191,195],"calf":[60],"muscle.":[61],"From":[62],"results,":[64],"aim":[66],"implement":[68],"results":[70,202],"other":[72],"cases":[73],"like":[74],"model":[75,98,122],"optimization":[76,99,123],"or":[77],"feature":[78],"selection.":[79],"So,":[80],"Hadamard":[83,89,192],"products":[84],"achieve":[86,97],"goals.":[88],"product":[90,193],"very":[92],"light":[93],"so":[94],"it":[95,164],"and":[100,107,124,139,187],"measure":[102],"value":[104],"sensors":[106],"each":[108],"frame":[110],"with":[111,133],"its'":[112],"weight":[113,116],"values.":[114],"These":[115],"values":[117],"lead":[119],"further":[121],"analysis":[125],"data.":[128,172],"We":[129,145,197],"tested":[130,147],"our":[131],"system":[132,151],"12":[136],"people":[138],"64":[141],"input":[143],"channels.":[144],"also":[146],"how":[148,177],"well":[149],"could":[152],"handle":[153],"changes":[154],"over":[157],"time,":[158],"which":[159],"important":[161],"avoid":[163],"getting":[165],"confused":[166],"by":[167,200],"random":[168],"noise":[169],"addition,":[174],"studied":[176],"changing":[178],"number":[180],"layers":[182],"use":[189],"affected":[194],"system.":[196],"did":[198],"comparing":[201],"total":[205],"1,200":[207],"models.":[209]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
