{"id":"https://openalex.org/W4398232844","doi":"https://doi.org/10.1145/3652628.3652698","title":"Research on Generating Method of Fiber Pill Images Based on Wasserstein Deep Convolution GAN","display_name":"Research on Generating Method of Fiber Pill Images Based on Wasserstein Deep Convolution GAN","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4398232844","doi":"https://doi.org/10.1145/3652628.3652698"},"language":"en","primary_location":{"id":"doi:10.1145/3652628.3652698","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652628.3652698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering","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/A5030883624","display_name":"Haodong Liang","orcid":"https://orcid.org/0009-0000-9852-9102"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tianjin Polytechnic University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haodong Liang","raw_affiliation_strings":["School of artificial intelligence, Tiangong University, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Tiangong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024267262","display_name":"Hongyi Wang","orcid":"https://orcid.org/0000-0001-7554-8271"},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tianjin Polytechnic University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyi Wang","raw_affiliation_strings":["School of artificial intelligence, Tiangong University, China"],"affiliations":[{"raw_affiliation_string":"School of artificial intelligence, Tiangong University, China","institution_ids":["https://openalex.org/I198091727"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053162922","display_name":"Y. Li","orcid":"https://orcid.org/0009-0000-6892-8620"},"institutions":[{"id":"https://openalex.org/I110109458","display_name":"Tianjin University of Commerce","ror":"https://ror.org/02b6amy98","country_code":"CN","type":"education","lineage":["https://openalex.org/I110109458"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajuan Li","raw_affiliation_strings":["School of Science, Tianjin University of Commerce, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Tianjin University of Commerce, China","institution_ids":["https://openalex.org/I110109458"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030883624"],"corresponding_institution_ids":["https://openalex.org/I198091727"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2231625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"422","last_page":"427"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.98089998960495,"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/T14319","display_name":"Currency Recognition and Detection","score":0.98089998960495,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9757999777793884,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9696000218391418,"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/deep-learning","display_name":"Deep learning","score":0.7296638488769531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6841279864311218},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6662166714668274},{"id":"https://openalex.org/keywords/fiber","display_name":"Fiber","score":0.6616319417953491},{"id":"https://openalex.org/keywords/pill","display_name":"Pill","score":0.5731465220451355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5645990371704102},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5419110059738159},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35905879735946655},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.23726069927215576},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14991697669029236},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.06798294186592102},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.06770142912864685}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7296638488769531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6841279864311218},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6662166714668274},{"id":"https://openalex.org/C519885992","wikidata":"https://www.wikidata.org/wiki/Q161","display_name":"Fiber","level":2,"score":0.6616319417953491},{"id":"https://openalex.org/C81603835","wikidata":"https://www.wikidata.org/wiki/Q3457430","display_name":"Pill","level":2,"score":0.5731465220451355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5645990371704102},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5419110059738159},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35905879735946655},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.23726069927215576},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14991697669029236},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.06798294186592102},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.06770142912864685},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652628.3652698","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3652628.3652698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering","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":9,"referenced_works":["https://openalex.org/W2434741482","https://openalex.org/W2548275288","https://openalex.org/W2605135824","https://openalex.org/W2605287558","https://openalex.org/W2739748921","https://openalex.org/W2963981733","https://openalex.org/W3090052686","https://openalex.org/W3185602146","https://openalex.org/W6674914833"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2057775761","https://openalex.org/W2964074194","https://openalex.org/W2800597160"],"abstract_inverted_index":{"The":[0,111,132],"detection":[1,31,38,43],"of":[2,14,26,36,56,69,78,96,129,144,156,167],"fiber":[3,27,57,64,70,97,130,145,159],"pills":[4,58,146],"has":[5,162],"always":[6],"been":[7],"a":[8,60],"challenging":[9],"task":[10],"in":[11],"the":[12,20,34,42,45,53,67,76,118,125,141,157,164,168],"production":[13],"pharmaceutical":[15],"factories.":[16],"However,":[17,66],"due":[18],"to":[19,86,122,154],"tiny":[21,54],"size":[22],"and":[23,120],"complex":[24],"structure":[25],"on":[28,105],"pills,":[29,160],"traditional":[30],"methods":[32],"face":[33],"problem":[35,128],"low":[37],"accuracy.":[39],"To":[40],"improve":[41],"accuracy,":[44],"deep":[46,79,114],"learning":[47,80],"algorithm":[48],"which":[49,82,161],"could":[50],"accurately":[51],"capture":[52],"features":[55],"is":[59,101],"considerable":[61],"method":[62],"for":[63,75,124],"detection.":[65,89],"number":[68],"pill":[71,98],"samples":[72],"are":[73,151],"insufficient":[74,126],"training":[77],"model,":[81],"makes":[83],"it":[84],"difficult":[85],"achieve":[87],"high-precision":[88],"In":[90],"this":[91],"work,":[92],"an":[93],"exploratory":[94],"study":[95],"image":[99],"generation":[100,165],"carried":[102],"out":[103],"based":[104],"Wasserstein":[106],"Deep":[107],"Convolution":[108],"GAN":[109],"(WDC-GAN).":[110],"WDC-GAN":[112,149],"introduces":[113],"convolutional":[115],"networks":[116],"into":[117],"generator":[119],"discriminator":[121],"compensate":[123],"dataset":[127],"pills.":[131],"Fr\u00e9chet":[133],"Inception":[134],"Distance":[135],"(FID)":[136],"results":[137],"have":[138],"shown":[139],"that":[140,155],"feature":[142],"distribution":[143],"generated":[147],"by":[148],"model":[150],"very":[152],"close":[153],"real":[158],"verified":[163],"ability":[166],"proposed":[169],"method.":[170]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
