{"id":"https://openalex.org/W4383333587","doi":"https://doi.org/10.1109/iciba56860.2023.10164813","title":"Design and research of waveform detection experimental platform","display_name":"Design and research of waveform detection experimental platform","publication_year":2023,"publication_date":"2023-05-26","ids":{"openalex":"https://openalex.org/W4383333587","doi":"https://doi.org/10.1109/iciba56860.2023.10164813"},"language":"en","primary_location":{"id":"doi:10.1109/iciba56860.2023.10164813","doi":"https://doi.org/10.1109/iciba56860.2023.10164813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciba56860.2023.10164813","pdf_url":null,"source":{"id":"https://openalex.org/S4363607965","display_name":"IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5090541821","display_name":"Chunlai Guo","orcid":"https://orcid.org/0009-0001-4848-9621","observed_orcids":["https://orcid.org/0009-0001-4848-9621"]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunlai Guo","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046801024","display_name":"Li Cui","orcid":"https://orcid.org/0000-0003-2205-7051","observed_orcids":["https://orcid.org/0000-0003-2205-7051"]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Cui","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102870640","display_name":"Xianying Cao","orcid":"https://orcid.org/0000-0001-8713-3604","observed_orcids":["https://orcid.org/0000-0001-8713-3604"]},"institutions":[{"id":"https://openalex.org/I4210112884","display_name":"Harbin Institute of Petroleum","ror":"https://ror.org/02b9rcr39","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210112884"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianying Cao","raw_affiliation_strings":["Harbin Instiute of Petroleum, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Instiute of Petroleum, Harbin, China","institution_ids":["https://openalex.org/I4210112884"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lei Shang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Shang","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinru Ni","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinru Ni","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100382961","display_name":"Xinyi Wang","orcid":"https://orcid.org/0009-0001-9863-5105","observed_orcids":["https://orcid.org/0009-0001-9863-5105"]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyi Wang","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109620738","display_name":"Yuexin Qu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280846","display_name":"Harbin Cambridge University","ror":"https://ror.org/03gp7h190","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280846"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuexin Qu","raw_affiliation_strings":["Harbin Cambridge University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Cambridge University, Harbin, China","institution_ids":["https://openalex.org/I4405280846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8023,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59564479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1279","last_page":"1284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.3508000075817108,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.3508000075817108,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10036","display_name":"Advanced Neural Network Applications","score":0.22859999537467957,"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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.09740000218153,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/waveform-detection","display_name":"waveform detection","score":0.9269999861717224},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.7680000066757202},{"id":"https://openalex.org/keywords/pass-fail-classification","display_name":"pass/fail classification","score":0.7020000219345093},{"id":"https://openalex.org/keywords/ultrasonic-testing","display_name":"ultrasonic testing","score":0.5080000162124634},{"id":"https://openalex.org/keywords/machine-vision","display_name":"machine vision","score":0.7269999980926514},{"id":"https://openalex.org/keywords/engineering-inspection","display_name":"engineering inspection","score":0.4650000035762787},{"id":"https://openalex.org/keywords/automatic-feature-extraction","display_name":"automatic feature extraction","score":0.6380000114440918},{"id":"https://openalex.org/keywords/ecg-classification","display_name":"ECG classification","score":0.5849999785423279}],"concepts":[{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.9037599563598633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734184861183167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6691235303878784},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6679419875144958},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6123886704444885},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.572623610496521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5188085436820984},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5113841891288757},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.489705353975296},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44153261184692383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4207320809364319},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iciba56860.2023.10164813","doi":"https://doi.org/10.1109/iciba56860.2023.10164813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciba56860.2023.10164813","pdf_url":null,"source":{"id":"https://openalex.org/S4363607965","display_name":"IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.428600013256073,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W3021599232","https://openalex.org/W3127212255"],"related_works":["https://openalex.org/W2758420372","https://openalex.org/W7204206218","https://openalex.org/W4316041223","https://openalex.org/W4385872159","https://openalex.org/W7143837988","https://openalex.org/W2537804448","https://openalex.org/W2898869085","https://openalex.org/W3090256880","https://openalex.org/W7160203382","https://openalex.org/W4382501045"],"abstract_inverted_index":{"With":[0],"the":[1,21,40,49,65,89,94,106,123,139,152,155,189,230,233,248,253,257],"rise":[2],"of":[3,51,71,109,126,138,151,157,191,209,243,250,260,265],"artificial":[4],"intelligence,":[5],"machine":[6,183],"learning":[7,216],"methods":[8],"have":[9,27,165],"been":[10],"applied":[11],"to":[12,56,87,104,143,187],"classify":[13],"many":[14],"waveform":[15,24,62,172,180,192,202,227],"processes,":[16],"such":[17],"as":[18,232],"ECG.":[19],"Since":[20],"detection":[22,181,203,234,241,249],"signal":[23],"and":[25,37,45,60,73,78,83,92,111,116,119,147,161,224,240,267],"image":[26],"similar":[28],"data":[29,235],"forms,":[30],"there":[31],"is":[32,185],"a":[33,99,114,132,200,207,214,238],"relationship":[34,41],"between":[35,42],"local":[36],"global,":[38],"i.e.,":[39],"low-level":[43,59],"features":[44,50],"high-level":[46],"features.":[47],"Therefore,":[48],"CNN":[52,101,153,254],"can":[53],"be":[54],"used":[55],"abstract":[57],"some":[58,69],"visible":[61],"changes":[63],"in":[64,75,113,154,171,229,263],"diagnosis":[66],"process":[67],"into":[68],"concepts":[70],"passing":[72],"failing":[74],"engineering":[76,145],"inspection,":[77],"realize":[79],"automatic":[80,107],"feature":[81],"extraction":[82],"classification.":[84],"In":[85,247],"order":[86],"reduce":[88],"parameter":[90],"operation":[91],"improve":[93],"efficiency,":[95,159],"this":[96,174],"paper":[97],"designs":[98],"7-layer":[100],"shallow":[102],"model":[103,142],"complete":[105],"classification":[108,124,193],"pass":[110],"fail":[112],"simple":[115],"efficient":[117],"way,":[118],"then":[120],"effectively":[121],"solve":[122],"problem":[125,190],"manual":[127,261],"inspection":[128],"images.":[129],"It":[130],"provides":[131,148],"practical":[133],"basis":[134],"for":[135,168,179,194,222],"further":[136,149],"application":[137],"trained":[140],"optimal":[141],"guide":[144],"practice,":[146],"validation":[150],"development":[156],"both":[158],"accuracy,":[160],"generalizability":[162],"algorithms,":[163],"which":[164,205],"great":[166],"potential":[167],"future":[169],"applications":[170],"detection.In":[173],"paper,":[175],"an":[176],"experimental":[177],"platform":[178],"by":[182],"vision":[184],"established":[186],"address":[188],"ultrasonic":[195],"conduction":[196],"technology":[197],"detection,":[198],"using":[199,225],"self-built":[201],"database,":[204],"contains":[206],"total":[208],"1000":[210],"test":[211],"images,":[212],"applying":[213],"deep":[215],"convolutional":[217],"neural":[218],"network":[219],"(CNN)":[220],"method":[221,255,259],"training,":[223],"700":[226],"images":[228],"period":[231],"set,":[236],"with":[237],"training":[239],"accuracy":[242],"93&":[244],"or":[245],"higher.":[246],"continuous":[251],"waveforms,":[252],"outperforms":[256],"conventional":[258],"reading":[262],"terms":[264],"speed":[266],"accuracy.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2023-07-07T00:00:00"}