{"id":"https://openalex.org/W2981289776","doi":"https://doi.org/10.3390/e21101019","title":"State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance","display_name":"State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance","publication_year":2019,"publication_date":"2019-10-21","ids":{"openalex":"https://openalex.org/W2981289776","doi":"https://doi.org/10.3390/e21101019","mag":"2981289776"},"language":"en","primary_location":{"id":"doi:10.3390/e21101019","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21101019","pdf_url":"https://www.mdpi.com/1099-4300/21/10/1019/pdf?version=1571638353","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/21/10/1019/pdf?version=1571638353","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003237783","display_name":"Chaojun Wang","orcid":"https://orcid.org/0000-0002-1632-1728"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaojun Wang","raw_affiliation_strings":["Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007234030","display_name":"Fei He","orcid":"https://orcid.org/0000-0001-9176-6674"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei He","raw_affiliation_strings":["Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007234030"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.3361,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.59954677,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"21","issue":"10","first_page":"1019","last_page":"1019"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9918000102043152,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9918000102043152,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9397000074386597,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/trigonometric-functions","display_name":"Trigonometric functions","score":0.7356859445571899},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5379522442817688},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5340543985366821},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5145308375358582},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49808573722839355},{"id":"https://openalex.org/keywords/process-capability-index","display_name":"Process capability index","score":0.49523892998695374},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.48304563760757446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4724072813987732},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46245431900024414},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45679759979248047},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4247950315475464},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.4109795093536377},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3709699511528015},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20824527740478516},{"id":"https://openalex.org/keywords/work-in-process","display_name":"Work in process","score":0.20505371689796448},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.13035860657691956}],"concepts":[{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.7356859445571899},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5379522442817688},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5340543985366821},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5145308375358582},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49808573722839355},{"id":"https://openalex.org/C190190378","wikidata":"https://www.wikidata.org/wiki/Q1192625","display_name":"Process capability index","level":3,"score":0.49523892998695374},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.48304563760757446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4724072813987732},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46245431900024414},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45679759979248047},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4247950315475464},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.4109795093536377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3709699511528015},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20824527740478516},{"id":"https://openalex.org/C174998907","wikidata":"https://www.wikidata.org/wiki/Q357662","display_name":"Work in process","level":2,"score":0.20505371689796448},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.13035860657691956},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e21101019","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21101019","pdf_url":"https://www.mdpi.com/1099-4300/21/10/1019/pdf?version=1571638353","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4383483831e34f098202c9c0049d0e14","is_oa":true,"landing_page_url":"https://doaj.org/article/4383483831e34f098202c9c0049d0e14","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 21, Iss 10, p 1019 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/21/10/1019/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e21101019","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7514240","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7514240","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e21101019","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21101019","pdf_url":"https://www.mdpi.com/1099-4300/21/10/1019/pdf?version=1571638353","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.41999998688697815,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981289776.pdf","grobid_xml":"https://content.openalex.org/works/W2981289776.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1967989298","https://openalex.org/W1970088130","https://openalex.org/W1981646714","https://openalex.org/W1998940406","https://openalex.org/W2003952658","https://openalex.org/W2022673924","https://openalex.org/W2048598794","https://openalex.org/W2063598178","https://openalex.org/W2104467646","https://openalex.org/W2114909272","https://openalex.org/W2149803014","https://openalex.org/W2190561235","https://openalex.org/W2313268156","https://openalex.org/W2335554950","https://openalex.org/W2357413454","https://openalex.org/W2511077623","https://openalex.org/W2524305182","https://openalex.org/W2565946545","https://openalex.org/W2601946505","https://openalex.org/W2611641075","https://openalex.org/W2615068864","https://openalex.org/W2752448697","https://openalex.org/W2756973318","https://openalex.org/W2766474393","https://openalex.org/W2793056747","https://openalex.org/W2897268107","https://openalex.org/W2905548604","https://openalex.org/W2908658073","https://openalex.org/W2911572979","https://openalex.org/W2942322192","https://openalex.org/W2947566299","https://openalex.org/W2962493332","https://openalex.org/W6755569950"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W126212742","https://openalex.org/W1948687848","https://openalex.org/W2953417386","https://openalex.org/W2473504045","https://openalex.org/W875509673","https://openalex.org/W4313413039","https://openalex.org/W3182441879"],"abstract_inverted_index":{"In":[0,35],"the":[1,12,15,19,83,91,99,102,106,122,126,132,137,141,147,156,169,187,198,213,216],"hot":[2,205],"strip":[3],"rolling":[4,207],"process,":[5,172],"many":[6],"process":[7,20,208],"parameters":[8,21],"are":[9,27,46,96,116,129,194],"related":[10],"to":[11,23,81,149,164,176,196,211],"quality":[13,45,177],"of":[14,86,101,108,159,215],"final":[16],"products.":[17],"Sometimes,":[18],"corresponding":[22],"different":[24,191],"steel":[25,88,120,161,206],"grades":[26,162],"close":[28],"to,":[29],"or":[30],"even":[31],"overlap,":[32],"each":[33,119],"other.":[34],"reality,":[36],"locating":[37],"overlap":[38,74,157,199],"regions":[39],"and":[40,73,90,112,125,140,179,190],"detecting":[41],"products":[42],"with":[43,93],"abnormal":[44,151],"crucial,":[47],"yet":[48],"challenging.":[49],"To":[50],"address":[51],"this":[52,55],"challenge,":[53],"in":[54,155,168],"work,":[56],"a":[57,204],"novel":[58],"method":[59],"named":[60],"kernel":[61],"entropy":[62],"component":[63],"analysis":[64],"(KECA)-weighted":[65],"cosine":[66,114,134,184],"distance":[67,115,135],"is":[68,79,144,174,209],"introduced":[69],"for":[70],"fault":[71],"detection":[72],"region":[75,158],"locating.":[76],"First,":[77],"KECA":[78],"used":[80,195,210],"cluster":[82],"training":[84],"samples":[85,92,154],"multiple":[87,160],"grades,":[89],"incorrect":[94],"classes":[95],"seen":[97],"as":[98,146],"boundary":[100],"sample":[103,139,189],"distribution.":[104],"Next,":[105],"concepts":[107],"recursive-based":[109],"regional":[110,123,142,192],"center":[111,124,143],"weighted":[113,133,183],"introduced.":[117],"For":[118],"grade,":[121],"weight":[127],"coefficients":[128],"determined.":[130],"Finally,":[131],"between":[136,186],"testing":[138,188],"chosen":[145],"index":[148],"judge":[150],"batches.":[152],"The":[153,182],"need":[163],"be":[165],"focused":[166],"on":[167],"real":[170],"production":[171],"which":[173],"conducive":[175],"grade":[178],"combined":[180],"production.":[181],"distances":[185],"centers":[193],"locate":[197],"region.":[200],"A":[201],"dataset":[202],"from":[203],"evaluate":[212],"performance":[214],"proposed":[217],"methods.":[218]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}