{"id":"https://openalex.org/W4414054635","doi":"https://doi.org/10.1109/acctcs66275.2025.00055","title":"Interpretable Multi-Resolution Mixing Neural Network for Tool Condition Monitoring","display_name":"Interpretable Multi-Resolution Mixing Neural Network for Tool Condition Monitoring","publication_year":2025,"publication_date":"2025-04-23","ids":{"openalex":"https://openalex.org/W4414054635","doi":"https://doi.org/10.1109/acctcs66275.2025.00055"},"language":"en","primary_location":{"id":"doi:10.1109/acctcs66275.2025.00055","doi":"https://doi.org/10.1109/acctcs66275.2025.00055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acctcs66275.2025.00055","pdf_url":null,"source":{"id":"https://openalex.org/S4363608502","display_name":"Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)","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":"2025 5th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5035751788","display_name":"Chenglin Yi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglin Yi","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019169850","display_name":"Qingqing Huang","orcid":"https://orcid.org/0000-0002-8298-5418","observed_orcids":["https://orcid.org/0000-0002-8298-5418"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingqing Huang","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089139551","display_name":"Yan Han","orcid":"https://orcid.org/0000-0001-7799-2659","observed_orcids":["https://orcid.org/0000-0001-7799-2659"]},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Han","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications,Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing,China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24760282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"271","last_page":"276"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.5400000214576721,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.5400000214576721,"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"}},{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.4408999979496002,"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/T13113","display_name":"Engineering Technology and Methodologies","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/tool-condition-monitoring","display_name":"tool condition monitoring","score":0.9860000014305115},{"id":"https://openalex.org/keywords/tool-wear-detection","display_name":"tool wear detection","score":0.8050000071525574},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"wavelet packet decomposition","score":0.984000027179718},{"id":"https://openalex.org/keywords/multi-resolution-feature-extraction","display_name":"multi-resolution feature extraction","score":0.718999981880188},{"id":"https://openalex.org/keywords/interpretable-neural-networks","display_name":"interpretable neural networks","score":0.8009999990463257},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.6240000128746033},{"id":"https://openalex.org/keywords/soft-thresholding","display_name":"soft thresholding","score":0.671999990940094},{"id":"https://openalex.org/keywords/biorthogonal-wavelets","display_name":"biorthogonal wavelets","score":0.7979999780654907},{"id":"https://openalex.org/keywords/multi-head-self-attention","display_name":"multi-head self-attention","score":0.8080000281333923},{"id":"https://openalex.org/keywords/feature-mixing","display_name":"feature mixing","score":0.5230000019073486}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.7146000266075134},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.701200008392334},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.5849999785423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.578499972820282},{"id":"https://openalex.org/C2778869661","wikidata":"https://www.wikidata.org/wiki/Q4915256","display_name":"Biorthogonal wavelet","level":5,"score":0.552299976348877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5098999738693237},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5034999847412109},{"id":"https://openalex.org/C199550912","wikidata":"https://www.wikidata.org/wiki/Q3238415","display_name":"Lifting scheme","level":5,"score":0.4697999954223633},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4490000009536743},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.3982999920845032},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.39419999718666077},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3573000133037567},{"id":"https://openalex.org/C158453530","wikidata":"https://www.wikidata.org/wiki/Q864692","display_name":"Biorthogonal system","level":4,"score":0.3402000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3321000039577484},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.32190001010894775},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2962999939918518},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2906000018119812},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.28540000319480896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25200000405311584},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acctcs66275.2025.00055","doi":"https://doi.org/10.1109/acctcs66275.2025.00055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acctcs66275.2025.00055","pdf_url":null,"source":{"id":"https://openalex.org/S4363608502","display_name":"Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)","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":"2025 5th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7718963816","display_name":null,"funder_award_id":"2022YFE0114300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2900529838","https://openalex.org/W3094917921","https://openalex.org/W4287377576","https://openalex.org/W4323316412","https://openalex.org/W4389202900","https://openalex.org/W4392135139","https://openalex.org/W4394675531","https://openalex.org/W4399942854","https://openalex.org/W4403382984"],"related_works":["https://openalex.org/W2373038869","https://openalex.org/W2375908649","https://openalex.org/W2614837930","https://openalex.org/W165228748","https://openalex.org/W1517180214","https://openalex.org/W2082780921","https://openalex.org/W1968967501","https://openalex.org/W2368589579","https://openalex.org/W2391251536","https://openalex.org/W2961085424"],"abstract_inverted_index":{"Drawing":[0],"inspiration":[1],"from":[2,88,135],"wavelet":[3,8,50,59,82,168,197],"analysis,":[4,83],"we":[5],"propose":[6],"a":[7,67,72,76,149],"packet":[9,60],"decomposition-based":[10],"layer":[11,52,178],"designed":[12],"for":[13,25,39],"multi-resolution":[14,133],"analysis":[15],"and":[16,29,41,75,93,132,146,188,219,230],"noise":[17,42,141,220],"reduction":[18],"of":[19,32,78,122,130],"input":[20,136],"signals.":[21,137],"This":[22,125,191],"approach":[23,85,126],"accounts":[24],"both":[26,186],"the":[27,37,54,101,112,120,128,162,172,176,183,206],"gradual":[28],"abrupt":[30],"features":[31,134],"tool":[33,45,238],"wear":[34,46,239],"while":[35,118,232],"addressing":[36],"need":[38],"interpretability":[40,117,218],"robustness":[43,221],"in":[44,58,210,237],"monitoring":[47,240],"tasks.":[48,241],"The":[49,213],"decomposition":[51,169],"maintains":[53],"tree":[55],"structure":[56],"inherent":[57,209],"decomposition.":[61],"It":[62],"achieves":[63],"this":[64,84],"by":[65,105,194],"stacking":[66],"deep":[68],"1D":[69],"CNN":[70],"with":[71],"shared":[73],"filter":[74,113,123],"stride":[77],"2.":[79],"Unlike":[80],"traditional":[81],"moves":[86],"away":[87],"reliance":[89],"on":[90,143,161],"fixed":[91],"wavelets,":[92],"it":[94],"learns":[95],"end-to-end":[96],"multi-channel":[97],"filters":[98],"instead.":[99],"Additionally,":[100],"extra":[102],"loss":[103],"constructed":[104],"biorthogonal":[106],"wavelets":[107],"is":[108,154,179,192],"used":[109],"to":[110,156,171,181,223],"constrain":[111],"training":[114],"process,":[115],"ensuring":[116],"enhancing":[119],"flexibility":[121],"optimization.":[124],"facilitates":[127],"extraction":[129],"multi-view":[131],"To":[138],"further":[139],"diminish":[140],"based":[142],"signal":[144],"characteristics":[145],"task-specific":[147],"requirements,":[148],"gated":[150],"linear":[151],"soft-shrink":[152],"function":[153],"employed":[155],"perform":[157],"adaptive":[158],"soft":[159],"thresholding":[160],"high-frequency":[163],"detail":[164],"coefficient":[165,198],"obtained":[166],"through":[167],"according":[170],"gating":[173],"coefficient.":[174],"Furthermore,":[175],"mix-res":[177],"introduced":[180],"enhance":[182],"interactivity":[184],"between":[185],"intra-layer":[187],"inter-layer":[189],"features.":[190,212],"achieved":[193],"mixing":[195],"threshold-processed":[196],"matrices":[199],"using":[200],"dual":[201],"multi-head":[202],"self-attention,":[203],"which":[204],"leverages":[205],"contextual":[207],"relationships":[208],"these":[211],"proposed":[214],"method":[215],"demonstrates":[216],"superior":[217],"compared":[222],"existing":[224],"approaches,":[225],"such":[226],"as":[227],"LSTM,":[228],"ResNet18,":[229],"Transformer,":[231],"also":[233],"achieving":[234],"advanced":[235],"performance":[236]},"counts_by_year":[],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2025-10-10T00:00:00"}