{"id":"https://openalex.org/W4405884792","doi":"https://doi.org/10.47116/apjcri.2024.12.30","title":"Deep Learning-Based Performance Enhancement for Autonomous Excavator Systems","display_name":"Deep Learning-Based Performance Enhancement for Autonomous Excavator Systems","publication_year":2024,"publication_date":"2024-12-30","ids":{"openalex":"https://openalex.org/W4405884792","doi":"https://doi.org/10.47116/apjcri.2024.12.30"},"language":"en","primary_location":{"id":"doi:10.47116/apjcri.2024.12.30","doi":"https://doi.org/10.47116/apjcri.2024.12.30","is_oa":true,"landing_page_url":"https://doi.org/10.47116/apjcri.2024.12.30","pdf_url":"http://apjcriweb.org/content/vol10no12/30.pdf","source":{"id":"https://openalex.org/S4210209862","display_name":"Asia-pacific Journal of Convergent Research Interchange","issn_l":"2508-9080","issn":["2508-9080","2671-5325"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asia-pacific Journal of Convergent Research Interchange","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"http://apjcriweb.org/content/vol10no12/30.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074157029","display_name":"Daehyon Kim","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Daehyon Kim","raw_affiliation_strings":["Professor, Department of Culture and Tourism Management , Chonnam National University , Korea,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, Department of Culture and Tourism Management , Chonnam National University , Korea,","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5074157029"],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":null,"apc_paid":null,"fwci":0.1438,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4520137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"12","first_page":"433","last_page":"444"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7562999725341797,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.7562999725341797,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.121799997985363,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10616","display_name":"Smart Agriculture and AI","score":0.019500000402331352,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autonomous-excavator","display_name":"autonomous excavator","score":0.9049999713897705},{"id":"https://openalex.org/keywords/construction-automation","display_name":"construction automation","score":0.8870000243186951},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.8460000157356262},{"id":"https://openalex.org/keywords/multi-model-combination","display_name":"multi-model combination","score":0.3840000033378601},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.49000000953674316}],"concepts":[{"id":"https://openalex.org/C141392084","wikidata":"https://www.wikidata.org/wiki/Q182661","display_name":"Excavator","level":2,"score":0.9070316553115845},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5547027587890625},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4852468967437744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4799268841743469},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2460535764694214},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.09021914005279541}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.47116/apjcri.2024.12.30","doi":"https://doi.org/10.47116/apjcri.2024.12.30","is_oa":true,"landing_page_url":"https://doi.org/10.47116/apjcri.2024.12.30","pdf_url":"http://apjcriweb.org/content/vol10no12/30.pdf","source":{"id":"https://openalex.org/S4210209862","display_name":"Asia-pacific Journal of Convergent Research Interchange","issn_l":"2508-9080","issn":["2508-9080","2671-5325"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asia-pacific Journal of Convergent Research Interchange","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.47116/apjcri.2024.12.30","doi":"https://doi.org/10.47116/apjcri.2024.12.30","is_oa":true,"landing_page_url":"https://doi.org/10.47116/apjcri.2024.12.30","pdf_url":"http://apjcriweb.org/content/vol10no12/30.pdf","source":{"id":"https://openalex.org/S4210209862","display_name":"Asia-pacific Journal of Convergent Research Interchange","issn_l":"2508-9080","issn":["2508-9080","2671-5325"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asia-pacific Journal of Convergent Research Interchange","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405884792.pdf","grobid_xml":"https://content.openalex.org/works/W4405884792.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"Automation":[0],"and":[1,22,33,79,105,158,165,172,178,196,264],"digitization":[2],"in":[3,202,215,233],"the":[4,15,31,45,122,132,173,189,197,210,226,242,254],"construction":[5,49,118,235,245,267],"industry":[6],"are":[7,54,61],"rapidly":[8],"advancing,":[9],"with":[10,96,167],"a":[11,26,40,97,170,249],"significant":[12],"focus":[13,99],"on":[14,100],"automation":[16,236,246],"of":[17,35,47,124,176,185,191,199,212,228,244,257],"excavators":[18],"to":[19,29,161,241],"enhance":[20,225],"productivity":[21],"safety.This":[23],"research":[24,108],"presents":[25],"novel":[27],"approach":[28],"improve":[30],"efficiency":[32],"stability":[34],"excavator":[36,43,259],"operations":[37],"by":[38,70,247],"developing":[39],"vision-based":[41],"autonomous":[42,216],"system.Given":[44],"complexity":[46],"real-time":[48],"sites,":[50,119],"accurate":[51],"predictive":[52,65,134,156,204,255],"models":[53,60,232],"crucial.While":[55],"ANN":[56,88],"(Artificial":[57],"Neural":[58],"Network)":[59],"widely":[62],"used,":[63],"their":[64],"accuracy":[66,157,184],"can":[67,224],"be":[68],"influenced":[69],"various":[71,229],"factors":[72],"such":[73],"as":[74],"input":[75,102,128,144],"variables,":[76,145],"network":[77],"architecture,":[78],"learning":[80],"methodologies.This":[81],"study":[82,238],"introduces":[83],"an":[84,181],"efficient":[85,266],"deep":[86],"learning-based":[87],"training":[89],"model":[90,106,175,213],"for":[91,127,252],"intelligent":[92,258],"automatic":[93],"excavation":[94,217],"systems,":[95,260],"particular":[98],"optimizing":[101],"variable":[103],"processing":[104],"architecture.The":[107],"involved":[109],"extensive":[110],"experimental":[111],"tests":[112],"using":[113],"diverse":[114],"video":[115],"data":[116],"from":[117],"revealing":[120],"that":[121,138,223],"choice":[123],"normalization":[125,151,194],"methods":[126,152,163],"variables":[129],"significantly":[130,146,239],"impacts":[131],"model's":[133],"performance.The":[135],"findings":[136,206],"highlight":[137],"ensemble":[139],"models,":[140],"which":[141],"integrate":[142],"multiple":[143],"outperform":[147],"individual":[148],"models.Specifically,":[149],"non-linear":[150],"(Norm_3)":[153],"showed":[154],"superior":[155],"reliability":[159],"compared":[160],"linear":[162],"(Norm_1":[164],"Norm_2),":[166],"Norm_3":[168,179],"achieving":[169],"91.0%accuracy":[171],"combined":[174],"Norm_2":[177],"reaching":[180],"even":[182],"higher":[183],"92.1%.These":[186],"results":[187],"underscore":[188],"importance":[190],"selecting":[192],"appropriate":[193],"techniques":[195],"potential":[198],"multi-model":[200],"collaboration":[201],"enhancing":[203],"accuracy.The":[205],"not":[207],"only":[208],"advance":[209],"understanding":[211],"optimization":[214],"but":[218],"also":[219],"provide":[220],"valuable":[221],"insights":[222],"performance":[227,256],"AI(Artificial":[230],"Intelligence)":[231],"real-world":[234],"applications.This":[237],"contributes":[240],"field":[243],"offering":[248],"robust":[250],"framework":[251],"improving":[253],"ultimately":[261],"promoting":[262],"safer":[263],"more":[265],"practices.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}