{"id":"https://openalex.org/W4405305488","doi":"https://doi.org/10.1109/icis61260.2024.10778350","title":"Acoustic Tomography of Wood Internal Defects with Convolutional Neural Network","display_name":"Acoustic Tomography of Wood Internal Defects with Convolutional Neural Network","publication_year":2024,"publication_date":"2024-09-20","ids":{"openalex":"https://openalex.org/W4405305488","doi":"https://doi.org/10.1109/icis61260.2024.10778350"},"language":"en","primary_location":{"id":"doi:10.1109/icis61260.2024.10778350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis61260.2024.10778350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS)","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/A5112327988","display_name":"K.T. Tse","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ki Tung Tse","raw_affiliation_strings":["The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047810681","display_name":"Kit Man Tsang","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ka Hei Tsang","raw_affiliation_strings":["The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101525940","display_name":"K. W. Sum","orcid":"https://orcid.org/0009-0003-0964-4005"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"K. W. Sum","raw_affiliation_strings":["The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Dept. of Computer Sci. and Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112327988"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24820847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"100","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10484","display_name":"Wood Treatment and Properties","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10484","display_name":"Wood Treatment and Properties","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9800999760627747,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7498414516448975},{"id":"https://openalex.org/keywords/tomography","display_name":"Tomography","score":0.6264001131057739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.525492250919342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31249088048934937},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13576894998550415},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.12774863839149475}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7498414516448975},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.6264001131057739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.525492250919342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31249088048934937},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13576894998550415},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.12774863839149475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis61260.2024.10778350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis61260.2024.10778350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS)","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":13,"referenced_works":["https://openalex.org/W1589935349","https://openalex.org/W2037521346","https://openalex.org/W2054054553","https://openalex.org/W2070829986","https://openalex.org/W2625522304","https://openalex.org/W2807784297","https://openalex.org/W2895301365","https://openalex.org/W2964700384","https://openalex.org/W2989707403","https://openalex.org/W3016108562","https://openalex.org/W4281384225","https://openalex.org/W4281694844","https://openalex.org/W4313013988"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Acoustic":[0],"tomography":[1,53],"is":[2,19,63],"a":[3,41,45,66],"viable":[4],"approach":[5],"to":[6,22,50,64,89],"assess":[7],"internal":[8,25],"defects":[9],"within":[10,80],"tree":[11,82],"trunks.":[12],"The":[13,60,117,131,160],"Simultaneous":[14],"Iterative":[15],"Reconstruction":[16],"Technique":[17],"(SIRT)":[18],"commonly":[20],"used":[21],"reconstruct":[23],"the":[24,72,81,95,98,104,108,114,136,142,165],"wood":[26],"structure,":[27],"but":[28],"it":[29],"suffers":[30],"from":[31,94],"reduced":[32],"precision.":[33],"To":[34],"address":[35],"this":[36,38],"issue,":[37],"paper":[39],"presents":[40],"method":[42,119,143,168],"that":[43,69,164],"employs":[44],"Convolutional":[46],"Neural":[47],"Network":[48],"(CNN)":[49],"conduct":[51],"acoustic":[52],"on":[54,107],"live":[55],"trees":[56],"with":[57,146],"exceptional":[58],"accuracy.":[59],"primary":[61],"objective":[62],"generate":[65],"tomographic":[67,173],"image":[68,158],"accurately":[70],"indicates":[71],"presence,":[73],"locations,":[74],"and":[75,103,125,133,141,156],"dimensions":[76],"of":[77,100,113,135,154],"any":[78],"cavities":[79],"trunk.":[83],"Non-destructive":[84],"devices":[85],"have":[86],"been":[87],"developed":[88],"facilitate":[90],"easier":[91],"data":[92,127],"acquisition":[93],"trees.":[96],"Furthermore,":[97],"use":[99],"batch":[101],"normalization":[102],"Adam":[105],"optimizer":[106],"CNN":[109],"enables":[110],"fast":[111],"convergence":[112],"training":[115],"process.":[116],"proposed":[118,166],"was":[120,144],"evaluated":[121],"using":[122],"both":[123],"simulation":[124],"experimental":[126],"under":[128],"various":[129],"settings.":[130],"area":[132],"shape":[134],"imaging":[137],"results":[138,162],"were":[139],"analyzed,":[140],"compared":[145],"existing":[147],"techniques,":[148],"such":[149],"as":[150],"SIRT,":[151],"in":[152],"terms":[153],"accuracy":[155],"overall":[157],"quality.":[159],"comparison":[161],"show":[163],"CNN-based":[167],"can":[169],"produce":[170],"much":[171],"better":[172],"reconstructions":[174],"than":[175],"SIRT.":[176]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
