{"id":"https://openalex.org/W4312199262","doi":"https://doi.org/10.1109/ifuzzy55320.2022.9985221","title":"A Novel Method Combining Grayscale Images and Convolutional Neural Networks for Non-Intrusive Load Identifications","display_name":"A Novel Method Combining Grayscale Images and Convolutional Neural Networks for Non-Intrusive Load Identifications","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4312199262","doi":"https://doi.org/10.1109/ifuzzy55320.2022.9985221"},"language":"en","primary_location":{"id":"doi:10.1109/ifuzzy55320.2022.9985221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ifuzzy55320.2022.9985221","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","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/A5026214108","display_name":"Men\u2010Shen Tsai","orcid":"https://orcid.org/0000-0002-6097-4953"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Men-Shen Tsai","raw_affiliation_strings":["Graduate Institute of Automation Tech. Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors National Taipei University of Tech.,Taipei,Taiwan","Graduate Institute of Automation Tech. Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors National Taipei University of Tech., Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Automation Tech. Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors National Taipei University of Tech.,Taipei,Taiwan","institution_ids":["https://openalex.org/I118292597"]},{"raw_affiliation_string":"Graduate Institute of Automation Tech. Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors National Taipei University of Tech., Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075731426","display_name":"Yuan\u2010Hao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yuan-Hao Li","raw_affiliation_strings":["Graduate Institute of Automation Tech. National Taipei University of Tech.,Taipei,Taiwan","Graduate Institute of Automation Tech. National Taipei University of Tech., Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Automation Tech. National Taipei University of Tech.,Taipei,Taiwan","institution_ids":["https://openalex.org/I118292597"]},{"raw_affiliation_string":"Graduate Institute of Automation Tech. National Taipei University of Tech., Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042859586","display_name":"Yen\u2010Kuang Lin","orcid":"https://orcid.org/0000-0002-7027-9508"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yen-Kuang Lin","raw_affiliation_strings":["College of Mechanical & Electrical Engineering National Taipei University of Tech.,Taipei,Taiwan"],"affiliations":[{"raw_affiliation_string":"College of Mechanical & Electrical Engineering National Taipei University of Tech.,Taipei,Taiwan","institution_ids":["https://openalex.org/I118292597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026214108"],"corresponding_institution_ids":["https://openalex.org/I118292597"],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4206326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9998000264167786,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9998000264167786,"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/T14011","display_name":"Elevator Systems and Control","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9897000193595886,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.8421180248260498},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.7545340061187744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7204043865203857},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6999557018280029},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6135427355766296},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5285353064537048},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5166304707527161},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46332260966300964},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43120241165161133},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2076319456100464},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09721368551254272}],"concepts":[{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.8421180248260498},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.7545340061187744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204043865203857},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6999557018280029},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6135427355766296},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5285353064537048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5166304707527161},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46332260966300964},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43120241165161133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2076319456100464},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09721368551254272},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ifuzzy55320.2022.9985221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ifuzzy55320.2022.9985221","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6421975136","display_name":null,"funder_award_id":"MOST 110-3116-F-006-001,MOST 110-2221-E-027-055","funder_id":"https://openalex.org/F4320309618","funder_display_name":"Ministry of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"},{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2010314082","https://openalex.org/W2081314233","https://openalex.org/W2123910460","https://openalex.org/W2255051834","https://openalex.org/W2554032856","https://openalex.org/W2666419487","https://openalex.org/W2904341746","https://openalex.org/W2905981407","https://openalex.org/W2906352818","https://openalex.org/W2946892792"],"related_works":["https://openalex.org/W115686965","https://openalex.org/W1974895211","https://openalex.org/W2129841057","https://openalex.org/W2768918307","https://openalex.org/W2176409448","https://openalex.org/W3040712279","https://openalex.org/W2364769705","https://openalex.org/W2056136368","https://openalex.org/W2110031805","https://openalex.org/W2374664672"],"abstract_inverted_index":{"-":[0],"In":[1,27,59,146],"the":[2,7,12,18,24,31,40,52,87,91,97,101,105,110,118,126,147,150,155,164,167,185,190,194],"non-intrusive":[3],"load":[4,22,56,74],"identification":[5,19],"field,":[6],"accuracy":[8],"of":[9,14,21,34,42,54,157,196],"event":[10,165],"detection,":[11],"uniqueness":[13],"feature":[15],"extraction,":[16],"and":[17,37,68,84,104,135],"rate":[20],"affect":[23],"system":[25],"performance.":[26],"recent":[28],"years,":[29],"as":[30,171,173],"sampling":[32,44],"frequency":[33,45],"smart":[35],"meters":[36],"instruments":[38],"progreses,":[39],"technology":[41],"high":[43],"(HSF)":[46],"has":[47],"been":[48],"widely":[49],"used":[50,123,153],"in":[51],"field":[53],"non-Intrusive":[55,73],"monitoring":[57,75],"(NILM).":[58],"this":[60],"paper,":[61],"a":[62,132,137,143,158,203],"novel":[63],"method":[64],"combining":[65],"grayscale":[66,186],"images":[67,187],"convolutional":[69,159,191],"neural":[70,160,192],"networks":[71],"for":[72,154,166,188],"was":[76,122],"proposed.":[77],"The":[78,113,179],"separated":[79,106],"waveform":[80,103,108,120],"is":[81,93,116,129,140,152],"acquired,":[82],"normalized,":[83],"compared":[85],"with":[86,169],"sine":[88],"waveform.":[89],"Then,":[90],"comparison":[92],"done":[94],"by":[95,183],"calculating":[96],"Euclidean":[98],"distance":[99],"between":[100],"standard":[102],"current":[107,119],"at":[109],"first":[111,127],"step.":[112],"second":[114],"step":[115],"that":[117,121,182],"to":[124,162],"obtain":[125],"derivative":[128],"arranged":[130],"into":[131,142],"two-dimensional":[133,138],"array,":[134],"then":[136],"array":[139],"converted":[141],"gray-scale":[144],"image.":[145],"final":[148],"step,":[149],"image":[151],"input":[156],"network":[161],"detect":[163],"loads":[168],"normal":[170],"well":[172],"low":[174],"or":[175],"slow-climbing":[176],"power":[177],"conditions.":[178],"result":[180],"shows":[181],"applying":[184],"training":[189],"network,":[193],"operation":[195],"appliances":[197],"can":[198],"be":[199],"detected":[200],"without":[201],"using":[202],"threshold.":[204]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}