{"id":"https://openalex.org/W4200101580","doi":"https://doi.org/10.1109/ictc52510.2021.9621110","title":"A Study on Quality Prediction Failure Cause Analysis in Batch Process","display_name":"A Study on Quality Prediction Failure Cause Analysis in Batch Process","publication_year":2021,"publication_date":"2021-10-20","ids":{"openalex":"https://openalex.org/W4200101580","doi":"https://doi.org/10.1109/ictc52510.2021.9621110"},"language":"en","primary_location":{"id":"doi:10.1109/ictc52510.2021.9621110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc52510.2021.9621110","pdf_url":null,"source":{"id":"https://openalex.org/S4363607766","display_name":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044940773","display_name":"Hye-Jin S. Kim","orcid":"https://orcid.org/0000-0001-5534-6250"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyejin S. Kim","raw_affiliation_strings":["ETRI, Daejeon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETRI, Daejeon, Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063627524","display_name":"Yoon Soo Han","orcid":"https://orcid.org/0000-0002-9763-3239"},"institutions":[{"id":"https://openalex.org/I4210110928","display_name":"Korea Institute of Ceramic Engineering and Technology","ror":"https://ror.org/024t5tt95","country_code":"KR","type":"facility","lineage":["https://openalex.org/I4210110928"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonsoo Han","raw_affiliation_strings":["KICET, Icheon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KICET, Icheon, Korea","institution_ids":["https://openalex.org/I4210110928"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073293745","display_name":"Seungwoog Jung","orcid":"https://orcid.org/0000-0003-4458-3298"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seung-Woog Jung","raw_affiliation_strings":["ETRI, Daejeon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETRI, Daejeon, Korea","institution_ids":["https://openalex.org/I142401562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.23658463,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1757","issue":null,"first_page":"1319","last_page":"1322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14484","display_name":"Technology and Data Analysis","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9307000041007996,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9107000231742859,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.6911591291427612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899589896202087},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6029829382896423},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5379934906959534},{"id":"https://openalex.org/keywords/long-term-prediction","display_name":"Long-term prediction","score":0.5007600784301758},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4957329034805298},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.49292612075805664},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4689953625202179},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.45297402143478394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43787139654159546},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.43760615587234497},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4360150396823883},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4343038499355316},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42762383818626404},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42707398533821106},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4207397401332855},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4126242399215698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3945588171482086},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22550269961357117},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17874956130981445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13316786289215088}],"concepts":[{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.6911591291427612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899589896202087},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6029829382896423},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5379934906959534},{"id":"https://openalex.org/C2776537626","wikidata":"https://www.wikidata.org/wiki/Q4047883","display_name":"Long-term prediction","level":2,"score":0.5007600784301758},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4957329034805298},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.49292612075805664},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4689953625202179},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.45297402143478394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43787139654159546},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.43760615587234497},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4360150396823883},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4343038499355316},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42762383818626404},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42707398533821106},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4207397401332855},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4126242399215698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3945588171482086},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22550269961357117},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17874956130981445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13316786289215088},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc52510.2021.9621110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc52510.2021.9621110","pdf_url":null,"source":{"id":"https://openalex.org/S4363607766","display_name":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5}],"awards":[{"id":"https://openalex.org/G2423972304","display_name":null,"funder_award_id":"20004367","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"}],"funders":[{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2811507150","https://openalex.org/W2980088075","https://openalex.org/W3082005737"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W4285201053","https://openalex.org/W4213040784"],"abstract_inverted_index":{"Quality":[0],"prediction":[1,134],"plays":[2],"a":[3,17,119,125,128],"key":[4],"role":[5],"in":[6,34,40,64,73,108,179],"all":[7],"kinds":[8],"of":[9,52,127],"manufacturing.":[10],"To":[11],"predict":[12,98,124],"quality":[13,126,180],"products":[14],"or":[15],"forecasting":[16],"product":[18],"quality,":[19],"data":[20,79,84,99,106,173],"should":[21,95],"contain":[22],"time":[23],"series":[24],"properties.":[25],"However,":[26],"these":[27,89,167],"properties":[28],"often":[29,49],"fail":[30,96,122],"to":[31,97,123],"be":[32,61],"contained":[33],"the":[35,65,76,112,155,157,163,176],"data.":[36],"This":[37],"usually":[38],"happens":[39],"many":[41,53],"industrial":[42],"manufacturing":[43],"environments.":[44],"For":[45],"example,":[46],"lot":[47,58],"processing":[48],"fails":[50],"because":[51],"reasons:":[54],"parts":[55,68],"having":[56],"different":[57],"numbers":[59],"can":[60],"mixed":[62],"up":[63],"following":[66],"step;":[67],"are":[69,80,85,115,147,153,160],"missing,":[70],"broken,":[71],"etc;":[72],"some":[74],"cases,":[75],"long":[77],"term":[78],"required":[81],"but":[82],"short-term":[83,109],"only":[86],"gathered.":[87],"Without":[88],"properties,":[90],"any":[91],"machine":[92,132],"learning":[93,133],"technique":[94],"quality.":[100],"In":[101],"this":[102],"paper,":[103],"we":[104,121,169],"have":[105],"collected":[107],"period":[110],"although":[111],"total":[113],"samples":[114],"3,000":[116],"and":[117,140,149],"under":[118],"condition,":[120],"sample":[129],"with":[130],"various":[131],"techniques:":[135],"Gaussian":[136],"process":[137],"regression":[138],"method":[139],"auto-regressive":[141],"integrated":[142],"moving":[143],"average.":[144],"Although":[145],"hyper-parameters":[146],"well-optimized":[148],"both":[150],"predicted":[151,158],"results":[152],"almost":[154],"same,":[156],"ones":[159],"far":[161],"from":[162],"ground":[164],"truth.":[165],"Through":[166],"experiments,":[168],"concluded":[170],"that":[171],"well-designed":[172],"collection":[174],"is":[175],"most":[177],"important":[178],"prediction.":[181]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
