{"id":"https://openalex.org/W2811160885","doi":"https://doi.org/10.1109/fskd.2017.8392991","title":"The fuzzy neural network based haul truck driver fatigue detection in surface mining","display_name":"The fuzzy neural network based haul truck driver fatigue detection in surface mining","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2811160885","doi":"https://doi.org/10.1109/fskd.2017.8392991","mag":"2811160885"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8392991","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5112124373","display_name":"Enji Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109779","display_name":"China Academy of Safety Sciences and Technology","ror":"https://ror.org/01pwpsm46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210109779"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Enji Sun","raw_affiliation_strings":["Industrial safety research institute, China Academy of Safety Science and Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Industrial safety research institute, China Academy of Safety Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I4210109779"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5112124373"],"corresponding_institution_ids":["https://openalex.org/I4210109779"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.27181728,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1522","last_page":"1526"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.9361000061035156,"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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.9361000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.8669838905334473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5634790658950806},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5051105618476868},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.4983069896697998},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.39471235871315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3279591500759125}],"concepts":[{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.8669838905334473},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5634790658950806},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5051105618476868},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.4983069896697998},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.39471235871315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3279591500759125}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8392991","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6499999761581421,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W101663867","https://openalex.org/W1500731913","https://openalex.org/W1968626407","https://openalex.org/W2060229028","https://openalex.org/W2116646930","https://openalex.org/W2151419026","https://openalex.org/W2390744835","https://openalex.org/W3097096317","https://openalex.org/W3214102110","https://openalex.org/W4403866337","https://openalex.org/W6804270185"],"related_works":["https://openalex.org/W1517019597","https://openalex.org/W1968776045","https://openalex.org/W2296713838","https://openalex.org/W767149399","https://openalex.org/W3036261569","https://openalex.org/W2889950528","https://openalex.org/W575062473","https://openalex.org/W4245592507","https://openalex.org/W2558800934","https://openalex.org/W2358190750"],"abstract_inverted_index":{"Haul":[0],"trucks":[1,18],"are":[2],"one":[3,36],"of":[4,23,29,37,57,83,92,97,105,121,149],"the":[5,10,24,27,30,38,47,54,58,64,81,84,89,93,98,116,134,153,175,180],"most":[6,102],"important":[7,103],"transportations":[8],"in":[9,20,80,173],"surface":[11],"mining.":[12],"The":[13,75,95,107,131],"traffic":[14],"accidents":[15,176],"involved":[16],"haul":[17,59,72,85],"result":[19],"serious":[21],"damages":[22],"vehicles,":[25],"even":[26],"loss":[28],"drivers'":[31],"lives.":[32],"Drive":[33],"fatigue":[34,50,69,118],"is":[35],"key":[39,90],"features":[40,56,91],"that":[41],"cause":[42],"accidents.":[43],"This":[44],"paper":[45],"analyzed":[46],"possible":[48],"driver":[49,99],"reasons":[51],"based":[52,68],"on":[53],"special":[55],"trucks.":[60],"Then,":[61],"it":[62],"proposed":[63],"fuzzy":[65,135],"neural":[66,136],"network":[67,137],"detection":[70,129,138,143],"for":[71],"truck":[73,86],"drivers.":[74,94],"CCD":[76],"camera":[77],"was":[78],"amounted":[79],"cabin":[82],"to":[87],"capture":[88],"eyes":[96],"could":[100,113],"show":[101],"information":[104],"fatigue.":[106,182],"head":[108],"nod":[109],"frequency,":[110],"yawn":[111],"frequency":[112],"also":[114],"reflect":[115],"driver's":[117],"degree.":[119],"Each":[120],"these":[122],"methods":[123],"has":[124,140,170],"its":[125],"own":[126],"limitation":[127],"and":[128,165],"errors.":[130],"results":[132],"shows":[133],"method":[139],"more":[141],"accurate":[142],"by":[144,179],"combining":[145],"with":[146],"PERCLOS":[147],"(Percentage":[148],"eyelid":[150],"closure":[151,160],"over":[152,155],"pupil":[154],"time),":[156],"AECS":[157],"(Average":[158],"eye":[159],"speed),":[161],"NodFreq":[162],"(Nod":[163],"frequency)":[164],"YawnFreq":[166],"(Yawn":[167],"frequency).":[168],"It":[169],"great":[171],"significance":[172],"reducing":[174],"rate":[177],"caused":[178],"drive":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}