{"id":"https://openalex.org/W2123482298","doi":"https://doi.org/10.1109/i2mtc.2013.6555389","title":"Principal component analysis preprocessing with Bayesian networks for battery capacity estimation","display_name":"Principal component analysis preprocessing with Bayesian networks for battery capacity estimation","publication_year":2013,"publication_date":"2013-05-01","ids":{"openalex":"https://openalex.org/W2123482298","doi":"https://doi.org/10.1109/i2mtc.2013.6555389","mag":"2123482298"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc.2013.6555389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2013.6555389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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/A5024011877","display_name":"Liessman Sturlaugson","orcid":null},"institutions":[{"id":"https://openalex.org/I23732399","display_name":"Montana State University","ror":"https://ror.org/02w0trx84","country_code":"US","type":"education","lineage":["https://openalex.org/I23732399","https://openalex.org/I4210126032"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Liessman E. Sturlaugson","raw_affiliation_strings":["Department of Computer Science, Montana State University, Bozeman, MT, USA","Dept. of Computer Science, Montana State University, Bozeman, MT, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Montana State University, Bozeman, MT, USA","institution_ids":["https://openalex.org/I23732399"]},{"raw_affiliation_string":"Dept. of Computer Science, Montana State University, Bozeman, MT, USA","institution_ids":["https://openalex.org/I23732399"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072522101","display_name":"John W. Sheppard","orcid":"https://orcid.org/0000-0001-9487-5622"},"institutions":[{"id":"https://openalex.org/I23732399","display_name":"Montana State University","ror":"https://ror.org/02w0trx84","country_code":"US","type":"education","lineage":["https://openalex.org/I23732399","https://openalex.org/I4210126032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John W. Sheppard","raw_affiliation_strings":["Department of Computer Science, Montana State University, Bozeman, MT, USA","Dept. of Computer Science, Montana State University, Bozeman, MT, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Montana State University, Bozeman, MT, USA","institution_ids":["https://openalex.org/I23732399"]},{"raw_affiliation_string":"Dept. of Computer Science, Montana State University, Bozeman, MT, USA","institution_ids":["https://openalex.org/I23732399"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024011877"],"corresponding_institution_ids":["https://openalex.org/I23732399"],"apc_list":null,"apc_paid":null,"fwci":2.0344,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.88665296,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"98","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9894000291824341,"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":0.9894000291824341,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/principal-component-analysis","display_name":"Principal component analysis","score":0.7678767442703247},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.7563375234603882},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7078287601470947},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7072089910507202},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5758964419364929},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5065063238143921},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5018250942230225},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4855029582977295},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47951915860176086},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.4705778956413269},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4568856358528137},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4501594305038452},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.43144920468330383},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4298914074897766},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35755789279937744}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7678767442703247},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.7563375234603882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7078287601470947},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7072089910507202},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5758964419364929},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5065063238143921},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5018250942230225},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4855029582977295},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47951915860176086},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.4705778956413269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4568856358528137},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4501594305038452},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.43144920468330383},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4298914074897766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35755789279937744},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/i2mtc.2013.6555389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2013.6555389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.714.5787","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.714.5787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.montana.edu/sheppard/pubs/i2mtc-2013.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1511986666","https://openalex.org/W1537578550","https://openalex.org/W1543352466","https://openalex.org/W1585743408","https://openalex.org/W1678889691","https://openalex.org/W1980452149","https://openalex.org/W2049864876","https://openalex.org/W2052006582","https://openalex.org/W2069433961","https://openalex.org/W2107074288","https://openalex.org/W2145039203","https://openalex.org/W2751060233","https://openalex.org/W6632681245"],"related_works":["https://openalex.org/W3010890513","https://openalex.org/W3086422166","https://openalex.org/W2053247611","https://openalex.org/W3165117444","https://openalex.org/W2578973671","https://openalex.org/W3016972457","https://openalex.org/W2215058820","https://openalex.org/W2945000716","https://openalex.org/W2097663773","https://openalex.org/W1602184117"],"abstract_inverted_index":{"Bayesian":[0,91],"networks":[1],"(BNs)":[2],"are":[3],"a":[4,19,70],"common":[5],"data-driven":[6],"approach":[7],"for":[8,43],"representing":[9],"and":[10,38],"reasoning":[11],"in":[12,18,33,89],"the":[13,26,29,34,39,48,61,64,97],"presence":[14],"of":[15,28,36,41,50,72],"uncertainty.":[16],"Inference":[17],"BN":[20,65],"can":[21,87],"quickly":[22],"become":[23],"intractable":[24],"as":[25],"complexity":[27],"network":[30,92],"increases,":[31],"specifically":[32],"number":[35,40],"nodes":[37],"states":[42],"each":[44],"node.":[45],"We":[46],"demonstrate":[47],"benefit":[49],"preprocessing":[51,86],"cyclic":[52],"time-series":[53],"measurements":[54],"using":[55],"principal":[56],"component":[57],"analysis":[58],"(PCA),":[59],"evaluating":[60],"technique":[62],"with":[63],"to":[66],"perform":[67],"diagnostics":[68],"on":[69],"set":[71],"lithium-ion":[73],"batteries":[74],"that":[75],"have":[76],"undergone":[77],"repeated":[78],"charging/discharging":[79],"cycles.":[80],"The":[81],"results":[82],"show":[83],"how":[84],"PCA":[85],"result":[88],"simpler":[90],"models":[93],"than":[94],"those":[95],"from":[96],"raw":[98],"data":[99],"while":[100],"still":[101],"achieving":[102],"higher":[103],"accuracy.":[104]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
