{"id":"https://openalex.org/W4318148174","doi":"https://doi.org/10.1109/bigdata55660.2022.10020968","title":"The forecast of the AGV battery discharging via the machine learning methods","display_name":"The forecast of the AGV battery discharging via the machine learning methods","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148174","doi":"https://doi.org/10.1109/bigdata55660.2022.10020968"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020968","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","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/A5004473673","display_name":"Olena Pavliuk","orcid":"https://orcid.org/0000-0003-4561-3874"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Olena Pavliuk","raw_affiliation_strings":["Silesian University of Technology,Gliwice,Poland","Silesian University of Technology, Gliwice, Poland"],"affiliations":[{"raw_affiliation_string":"Silesian University of Technology,Gliwice,Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017554137","display_name":"Tomasz St\u0119clik","orcid":"https://orcid.org/0000-0002-3843-2103"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Tomasz Steclik","raw_affiliation_strings":["Silesian University of Technology,Gliwice,Poland","Silesian University of Technology, Gliwice, Poland"],"affiliations":[{"raw_affiliation_string":"Silesian University of Technology,Gliwice,Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088470700","display_name":"Piotr Biernacki","orcid":"https://orcid.org/0000-0002-0159-4782"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Piotr Biernacki","raw_affiliation_strings":["Silesian University of Technology,Gliwice,Poland","Silesian University of Technology, Gliwice, Poland"],"affiliations":[{"raw_affiliation_string":"Silesian University of Technology,Gliwice,Poland","institution_ids":["https://openalex.org/I119004910"]},{"raw_affiliation_string":"Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004473673"],"corresponding_institution_ids":["https://openalex.org/I119004910"],"apc_list":null,"apc_paid":null,"fwci":3.1269,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93308167,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6315","last_page":"6324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9771000146865845,"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"}},"topics":[{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9771000146865845,"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"}},{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.9509000182151794,"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/normalization","display_name":"Normalization (sociology)","score":0.6123721599578857},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.6039863228797913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5980838537216187},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5302144289016724},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.510758638381958},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.41834020614624023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2785583734512329},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23279693722724915}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6123721599578857},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.6039863228797913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5980838537216187},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5302144289016724},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.510758638381958},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.41834020614624023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2785583734512329},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23279693722724915},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020968","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2625064786","https://openalex.org/W2793867208","https://openalex.org/W2885660591","https://openalex.org/W2899793010","https://openalex.org/W2970706887","https://openalex.org/W2972349723","https://openalex.org/W2998671458","https://openalex.org/W3049468855","https://openalex.org/W3087516877","https://openalex.org/W3138815779","https://openalex.org/W3197591245","https://openalex.org/W4200235087","https://openalex.org/W4205853289","https://openalex.org/W4235929402","https://openalex.org/W4250336478","https://openalex.org/W4280536982","https://openalex.org/W4285157368","https://openalex.org/W6650746780"],"related_works":["https://openalex.org/W3035794324","https://openalex.org/W2787684247","https://openalex.org/W3007689282","https://openalex.org/W4289148071","https://openalex.org/W3100971012","https://openalex.org/W4287178710","https://openalex.org/W4290792893","https://openalex.org/W2556154603","https://openalex.org/W4297807321","https://openalex.org/W2973451922"],"abstract_inverted_index":{"We":[0],"reviewed":[1],"the":[2,10,22,26,35,46,52,55,66,70,82,88,99,103,117,136,140,143,153,164,177,184,194,199,202],"existing":[3],"and":[4,40,51,76,94],"currently":[5],"used":[6],"approach":[7,112],"in":[8],"processing":[9],"residual":[11],"charge":[12],"of":[13,19,34,45,72,135,142,152,168,193,201],"an":[14,30],"AGV":[15,31,104,165,185],"battery.":[16],"The":[17,42,133,149,158],"method":[18],"setting":[20],"up":[21],"experiment":[23],"for":[24,29,54,81,163,210],"collecting":[25],"historical":[27],"data":[28,57,75,77,80],"Formica":[32],"1":[33],"AIUT":[36],"company":[37],"was":[38,59,131,146,172,188],"proposed":[39],"implemented.":[41],"collected":[43,79],"properties":[44],"time":[47,129,144],"series":[48],"were":[49,84,155,196],"analyzed":[50,86],"algorithm":[53,62],"necessary":[56],"pre-processing":[58],"selected.":[60],"This":[61],"includes":[63],"padding":[64],"any":[65,73],"suppression":[67],"spontaneous":[68],"peaks,":[69],"recovery":[71],"lost":[74],"normalization.The":[78],"AGVs":[83],"also":[85,147],"using":[87,127],"correlation":[89],"analysis":[90],"methods":[91],"(Pearson,":[92],"Spearman":[93],"Kendall":[95],"correlations).":[96],"These":[97],"determined":[98],"parameters":[100,151,179],"on":[101,116,139,180],"which":[102,181],"battery":[105,109,170,186],"discharge":[106,110],"depends.":[107],"A":[108],"prediction":[111],"that":[113],"is":[114,122],"based":[115],"quasi-stochastic":[118],"signal's":[119],"probabilistic":[120],"characteristics":[121],"suggested.A":[123],"Multiparameter":[124],"ANN":[125,154],"model":[126],"a":[128,169],"window":[130,145],"developed.":[132],"dependence":[134],"forecast":[137,167],"error":[138,162],"length":[141],"investigated.":[148],"optimal":[150],"selected":[156],"experimentally.":[157],"mean":[159],"absolute":[160],"percentage":[161],"short-term":[166],"discharging":[171,187],"less":[173,189],"than":[174,190],"1%.":[175],"For":[176],"other":[178],"it":[182],"depends,":[183],"9%.":[191],"All":[192],"studies":[195],"conducted":[197],"within":[198],"framework":[200],"\"Automated":[203],"Guided":[204],"Vehicles":[205],"integrated":[206],"with":[207],"Collaborative":[208],"Robots":[209],"Smart":[211],"Industry":[212],"Perspective\"":[213],"project.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
