{"id":"https://openalex.org/W4391769142","doi":"https://doi.org/10.1109/ccwc60891.2024.10427937","title":"CNN-based Autoencoder and Post-Training Quantization for On-Device Anomaly Detection of Cartesian Coordinate Robots","display_name":"CNN-based Autoencoder and Post-Training Quantization for On-Device Anomaly Detection of Cartesian Coordinate Robots","publication_year":2024,"publication_date":"2024-01-08","ids":{"openalex":"https://openalex.org/W4391769142","doi":"https://doi.org/10.1109/ccwc60891.2024.10427937"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc60891.2024.10427937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc60891.2024.10427937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)","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/A5007839819","display_name":"Hyuntae Cho","orcid":"https://orcid.org/0000-0002-8951-0239"},"institutions":[{"id":"https://openalex.org/I4210104306","display_name":"Tongmyong University","ror":"https://ror.org/01asa8g02","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210104306"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyuntae Cho","raw_affiliation_strings":["Tongmyong University,Dept. of Digital Content,Busan,South Korea","Dept. of Digital Content, Tongmyong University, Busan, South Korea"],"affiliations":[{"raw_affiliation_string":"Tongmyong University,Dept. of Digital Content,Busan,South Korea","institution_ids":["https://openalex.org/I4210104306"]},{"raw_affiliation_string":"Dept. of Digital Content, Tongmyong University, Busan, South Korea","institution_ids":["https://openalex.org/I4210104306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007839819"],"corresponding_institution_ids":["https://openalex.org/I4210104306"],"apc_list":null,"apc_paid":null,"fwci":1.0878,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.79033358,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"0662","last_page":"0666"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9955000281333923,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9835000038146973,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cartesian-coordinate-system","display_name":"Cartesian coordinate system","score":0.8711543083190918},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6912431716918945},{"id":"https://openalex.org/keywords/cartesian-coordinate-robot","display_name":"Cartesian coordinate robot","score":0.6386640667915344},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6249017715454102},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6054407358169556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5986591577529907},{"id":"https://openalex.org/keywords/coordinate-system","display_name":"Coordinate system","score":0.5293156504631042},{"id":"https://openalex.org/keywords/industrial-robot","display_name":"Industrial robot","score":0.44170406460762024},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4340093433856964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39297229051589966},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3700299859046936},{"id":"https://openalex.org/keywords/robot-kinematics","display_name":"Robot kinematics","score":0.2867158055305481},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1523185670375824},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.1012132465839386},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08760780096054077}],"concepts":[{"id":"https://openalex.org/C16038011","wikidata":"https://www.wikidata.org/wiki/Q62912","display_name":"Cartesian coordinate system","level":2,"score":0.8711543083190918},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6912431716918945},{"id":"https://openalex.org/C118635694","wikidata":"https://www.wikidata.org/wiki/Q3268064","display_name":"Cartesian coordinate robot","level":5,"score":0.6386640667915344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6249017715454102},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6054407358169556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5986591577529907},{"id":"https://openalex.org/C80551277","wikidata":"https://www.wikidata.org/wiki/Q11210","display_name":"Coordinate system","level":2,"score":0.5293156504631042},{"id":"https://openalex.org/C2776126113","wikidata":"https://www.wikidata.org/wiki/Q1128980","display_name":"Industrial robot","level":3,"score":0.44170406460762024},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4340093433856964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39297229051589966},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3700299859046936},{"id":"https://openalex.org/C74222875","wikidata":"https://www.wikidata.org/wiki/Q16000312","display_name":"Robot kinematics","level":4,"score":0.2867158055305481},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1523185670375824},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.1012132465839386},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08760780096054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc60891.2024.10427937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc60891.2024.10427937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327819","display_name":"Ministry of SMEs and Startups","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W607315870","https://openalex.org/W1495862233","https://openalex.org/W1986487640","https://openalex.org/W2070109803","https://openalex.org/W2114246891","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2593397865","https://openalex.org/W2743138268","https://openalex.org/W2773131928","https://openalex.org/W2783230551","https://openalex.org/W2887959589","https://openalex.org/W2910068345","https://openalex.org/W2940727201","https://openalex.org/W2982294822","https://openalex.org/W3091096675","https://openalex.org/W3127932753","https://openalex.org/W3132850250","https://openalex.org/W3133926174","https://openalex.org/W3185547778","https://openalex.org/W3211787299","https://openalex.org/W4235298171","https://openalex.org/W4246760065","https://openalex.org/W4282946540","https://openalex.org/W4292290851","https://openalex.org/W4386065704","https://openalex.org/W6748102297","https://openalex.org/W6758101687","https://openalex.org/W6795991173","https://openalex.org/W6797854001","https://openalex.org/W6798630390"],"related_works":["https://openalex.org/W2112736455","https://openalex.org/W1020620338","https://openalex.org/W1036286070","https://openalex.org/W2372486955","https://openalex.org/W2138013202","https://openalex.org/W4385733071","https://openalex.org/W2247326821","https://openalex.org/W2966740236","https://openalex.org/W4212946320","https://openalex.org/W2612035765"],"abstract_inverted_index":{"Robots":[0],"are":[1],"usually":[2],"conducting":[3],"difficult":[4],"and":[5,27,37,81,147,156],"laborious":[6],"work":[7],"on":[8,127,154],"behalf":[9],"of":[10,49,73,114,137,140,176],"humans":[11],"in":[12,22,30,41,63],"various":[13],"industrial":[14,32,64],"fields.":[15],"Specially,":[16],"a":[17,23,55,74,79,95,105,202],"Cartesian":[18,24,50,67,75,116,141],"coordinate":[19,25,51,68,76,117,142],"robot":[20,77,92],"operates":[21],"system":[26],"is":[28,54,85,108],"used":[29],"many":[31],"sites":[33,65],"with":[34,122],"little":[35],"error":[36],"very":[38],"high":[39],"precision":[40],"work.":[42],"However,":[43],"due":[44],"to":[45,58,88,110,144,151,165,181,187],"the":[46,71,112,115,134,167,174,183,199],"low-price":[47],"requirements":[48],"robots,":[52,143],"there":[53],"tendency":[56],"not":[57],"apply":[59,152,182],"fault":[60],"diagnosis":[61],"technology":[62,107,125,186],"using":[66,172],"robots.":[69],"In":[70,179],"case":[72],"having":[78],"precise":[80],"low":[82],"error,":[83],"it":[84],"more":[86],"sensitive":[87],"failure":[89,97,113],"than":[90],"other":[91],"systems":[93],"because":[94],"slight":[96],"may":[98],"cause":[99],"malfunction":[100],"or":[101],"product":[102],"defects.":[103],"Therefore,":[104],"low-cost":[106],"needed":[109],"diagnose":[111,145],"robot.":[118],"This":[119,160],"paper":[120,161],"deals":[121],"deep":[123,177,184],"learning":[124,185],"based":[126],"convolutional":[128],"neural":[129],"network":[130],"(CNN)":[131],"that":[132],"recognizes":[133],"anomalous":[135],"sound":[136,169],"sliding":[138],"rails":[139],"failures,":[146],"then":[148],"post-training":[149,194],"quantization":[150,195],"them":[153],"small":[155,188],"cheap":[157],"embedded":[158],"devices.":[159],"also":[162,191],"describes":[163],"how":[164],"manipulate":[166],"raw":[168],"data":[170],"for":[171],"as":[173],"input":[175],"learning.":[178],"order":[180],"systems,":[189],"I":[190],"evaluate":[192],"several":[193],"methods":[196],"after":[197],"converting":[198],"model":[200],"into":[201],"lightweight":[203],"model.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}