{"id":"https://openalex.org/W4385289681","doi":"https://doi.org/10.1145/3594409.3594425","title":"A Subpixel-level Parallel Alignment Technology Oriented to EMU Train Undercarriage Image","display_name":"A Subpixel-level Parallel Alignment Technology Oriented to EMU Train Undercarriage Image","publication_year":2023,"publication_date":"2023-03-03","ids":{"openalex":"https://openalex.org/W4385289681","doi":"https://doi.org/10.1145/3594409.3594425"},"language":"en","primary_location":{"id":"doi:10.1145/3594409.3594425","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594409.3594425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Innovation in Artificial Intelligence","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/A5073480347","display_name":"Peng Sun","orcid":"https://orcid.org/0009-0008-9830-1421"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Sun","raw_affiliation_strings":["Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China"],"affiliations":[{"raw_affiliation_string":"Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084906163","display_name":"Weijiao Zhang","orcid":"https://orcid.org/0009-0006-1990-6322"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijiao Zhang","raw_affiliation_strings":["Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China"],"affiliations":[{"raw_affiliation_string":"Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070287930","display_name":"Kai Yang","orcid":"https://orcid.org/0009-0006-2227-7803"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China"],"affiliations":[{"raw_affiliation_string":"Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100703983","display_name":"Yan Chen","orcid":"https://orcid.org/0009-0005-0674-2675"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Chen","raw_affiliation_strings":["Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China"],"affiliations":[{"raw_affiliation_string":"Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, China","institution_ids":["https://openalex.org/I4210141966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073480347"],"corresponding_institution_ids":["https://openalex.org/I4210141966"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07715759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"39","last_page":"45"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9053999781608582,"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":0.9053999781608582,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7803323268890381},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7217448949813843},{"id":"https://openalex.org/keywords/subpixel-rendering","display_name":"Subpixel rendering","score":0.7046480178833008},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6402930021286011},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6139881610870361},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5262885689735413},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5078769326210022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4624709188938141},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4593435227870941},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.43288731575012207},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3001967668533325},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.2319178581237793},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12167036533355713}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7803323268890381},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7217448949813843},{"id":"https://openalex.org/C68516990","wikidata":"https://www.wikidata.org/wiki/Q452912","display_name":"Subpixel rendering","level":3,"score":0.7046480178833008},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6402930021286011},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6139881610870361},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5262885689735413},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5078769326210022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4624709188938141},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4593435227870941},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.43288731575012207},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3001967668533325},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2319178581237793},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12167036533355713},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594409.3594425","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594409.3594425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Innovation in Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2369528593","https://openalex.org/W2385629811","https://openalex.org/W2638735979","https://openalex.org/W2111510641","https://openalex.org/W2386795888","https://openalex.org/W1968995436","https://openalex.org/W2382389562","https://openalex.org/W2054875742","https://openalex.org/W2056216430","https://openalex.org/W2111946936"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,116],"realize":[3],"efficient":[4],"fault":[5,38,189],"recognition":[6,190],"and":[7,54,100,103,153,171,187,195],"utilization":[8],"of":[9,35,166],"the":[10,29,36,42,59,65,82,136,146,162,172,181],"high-speed":[11],"train":[12],"(EMU)":[13],"undercarriage":[14],"image":[15,20,31,48,66,76,91,126,139,174,183],"obtained":[16],"by":[17],"detection":[18,39],"robot,":[19],"alignment":[21,67,77,92,127,140,169],"technology":[22],"play":[23],"an":[24],"important":[25],"role.":[26],"Firstly,":[27],"research":[28],"original":[30,60],"data":[32],"collection":[33],"process":[34],"EMU":[37],"robot":[40],"in":[41,145,178],"practical":[43],"working":[44],"site;":[45],"secondly,":[46],"perform":[47],"enhancement":[49],"such":[50],"as":[51],"gamma":[52],"transformation":[53,168],"Hanning":[55],"window":[56],"adding":[57],"on":[58,70,112,130,161],"image;":[61],"thirdly,":[62],"compared":[63],"with":[64,85,123,157],"algorithm":[68,78,141],"based":[69,111,129],"feature":[71,131],"extraction,":[72,132],"a":[73,105,198],"Fourier":[74,137],"transform-based":[75,138],"is":[79,93],"proposed;":[80],"finally,":[81],"affine":[83],"processing":[84,175],"global":[86],"transform":[87],"parameters":[88],"acquired":[89],"from":[90],"given.":[94],"This":[95],"paper":[96],"conducts":[97],"experimental":[98],"verification":[99],"comparative":[101],"analysis,":[102],"selects":[104],"convolutional":[106],"neural":[107],"network":[108],"(CNN)":[109],"model":[110],"deep":[113],"residual":[114],"structure":[115],"carry":[117],"out":[118],"this":[119,179],"experiment.":[120],"Through":[121],"comparison":[122],"three":[124],"typical":[125],"algorithms":[128],"it":[133],"shows":[134],"that":[135],"has":[142],"significantly":[143],"improved":[144],"accurate":[147],"alarm":[148,151,155],"rate,":[149,152],"mistaken":[150],"omitted":[154],"rate":[156],"strong":[158],"robustness.":[159],"Based":[160],"intrinsic":[163],"subpixel-level":[164],"precision":[165],"domain":[167],"algorithm,":[170],"parallel":[173],"method":[176],"proposed":[177],"paper,":[180],"on-site":[182],"preprocessing,":[184],"high-precision":[185],"alignment,":[186],"automatic":[188],"can":[191],"be":[192],"realized":[193],"stably":[194],"reliably":[196],"within":[197],"reasonable":[199],"time.":[200]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
