{"id":"https://openalex.org/W4403333153","doi":"https://doi.org/10.1145/3686490.3686502","title":"Deep learning-based method for recognizing violation behaviors of underground personnel","display_name":"Deep learning-based method for recognizing violation behaviors of underground personnel","publication_year":2024,"publication_date":"2024-07-12","ids":{"openalex":"https://openalex.org/W4403333153","doi":"https://doi.org/10.1145/3686490.3686502"},"language":"en","primary_location":{"id":"doi:10.1145/3686490.3686502","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686490.3686502","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Signal Processing and Machine Learning","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/A5004691090","display_name":"Tian Ma","orcid":"https://orcid.org/0000-0002-5583-0074"},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tian Ma","raw_affiliation_strings":["College of Computer Science and Technology, Xi'an University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Xi'an University of Science and Technology, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111362530","display_name":"Wenna Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenna Zhu","raw_affiliation_strings":["College of Computer Science and Technology, Xi'an University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Xi'an University of Science and Technology, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114244890","display_name":"Weilu Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilu Shi","raw_affiliation_strings":["College of Computer Science and Technology, Xi'an University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Xi'an University of Science and Technology, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109800002","display_name":"Jiahui Li","orcid":"https://orcid.org/0009-0008-9829-4030"},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahui Li","raw_affiliation_strings":["College of Computer Science and Technology, Xi'an University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Xi'an University of Science and Technology, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003555754","display_name":"Yuancheng Li","orcid":"https://orcid.org/0000-0002-9185-9974"},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuancheng Li","raw_affiliation_strings":["College of Computer Science and Technology, Xi'an University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Xi'an University of Science and Technology, China","institution_ids":["https://openalex.org/I110440473"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5004691090"],"corresponding_institution_ids":["https://openalex.org/I110440473"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15535469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"82","last_page":"87"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9984999895095825,"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/T10809","display_name":"Occupational Health and Safety Research","score":0.9642000198364258,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9404000043869019,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.5978784561157227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4174412488937378},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4134595990180969}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5978784561157227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4174412488937378},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4134595990180969}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3686490.3686502","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686490.3686502","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1676314349","https://openalex.org/W2194775991","https://openalex.org/W2531409750","https://openalex.org/W6600654476"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,13,70,75,81,96,101],"complex":[3],"coal":[4],"mine":[5],"environment":[6],"resulting":[7],"in":[8],"impaired":[9],"image":[10],"quality":[11],"and":[12,47,62,69,87],"difficulty":[14],"of":[15,19,39,42],"a":[16,34,48,56,63],"fixed":[17],"number":[18],"convolutional":[20],"kernels":[21],"to":[22],"capture":[23],"diverse":[24],"motion":[25],"features,":[26],"coupled":[27],"with":[28],"fast-moving":[29],"personnel":[30,44],"deteriorating":[31],"behavioural":[32],"coherence,":[33],"deep-learning-based":[35],"method":[36,71,97],"for":[37],"recognition":[38,102],"violation":[40,85],"behaviours":[41],"underground":[43],"is":[45,72],"proposed,":[46],"Depth":[49],"Sensing":[50],"Wild":[51],"Attention":[52],"Convolution":[53],"Module":[54,60,65],"(DRFA),":[55],"Multi-dimensional":[57],"Feature":[58],"Extraction":[59],"(MFE),":[61],"Displacement-Aware":[64],"(DAM)":[66],"are":[67,78],"designed,":[68],"implemented":[73],"on":[74,80],"self-constructed":[76],"Experiments":[77],"conducted":[79],"downhole":[82],"track":[83],"area":[84],"dataset,":[86],"its":[88],"TOP-1":[89],"accuracy":[90],"reaches":[91],"95.10%,":[92],"which":[93],"proves":[94],"that":[95],"can":[98],"effectively":[99],"improve":[100],"accuracy.":[103]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
