{"id":"https://openalex.org/W4416251535","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228464","title":"The impact of Sensing Parameters on Vibration Anomaly Detection Using Tiny DNNs","display_name":"The impact of Sensing Parameters on Vibration Anomaly Detection Using Tiny DNNs","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251535","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228464"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5041590923","display_name":"Edoardo Ragusa","orcid":"https://orcid.org/0000-0002-5527-6325"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Edoardo Ragusa","raw_affiliation_strings":["DITEN University of Genoa,Genoa,Italy,16145"],"affiliations":[{"raw_affiliation_string":"DITEN University of Genoa,Genoa,Italy,16145","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050594256","display_name":"Federica Zonzini","orcid":"https://orcid.org/0000-0002-2429-1469"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Federica Zonzini","raw_affiliation_strings":["DEI-ARCES University of Bologna,Bologna,Italy,40136"],"affiliations":[{"raw_affiliation_string":"DEI-ARCES University of Bologna,Bologna,Italy,40136","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071758029","display_name":"Luca De Marchi","orcid":"https://orcid.org/0000-0003-0637-9472"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca De Marchi","raw_affiliation_strings":["DEI-ARCES University of Bologna,Bologna,Italy,40136"],"affiliations":[{"raw_affiliation_string":"DEI-ARCES University of Bologna,Bologna,Italy,40136","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000985243","display_name":"Paolo Gastaldo","orcid":"https://orcid.org/0000-0002-5748-3942"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Gastaldo","raw_affiliation_strings":["DITEN University of Genoa,Genoa,Italy,16145"],"affiliations":[{"raw_affiliation_string":"DITEN University of Genoa,Genoa,Italy,16145","institution_ids":["https://openalex.org/I83816512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041590923"],"corresponding_institution_ids":["https://openalex.org/I83816512"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37623182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9218999743461609,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9218999743461609,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T12682","display_name":"Smart Materials for Construction","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.008899999782443047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6190000176429749},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5511000156402588},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5471000075340271},{"id":"https://openalex.org/keywords/structural-health-monitoring","display_name":"Structural health monitoring","score":0.5249000191688538},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5078999996185303},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.47690001130104065},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.44209998846054077},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4293000102043152},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.42500001192092896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700000166893005},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6190000176429749},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5471000075340271},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5358999967575073},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4293000102043152},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4090999960899353},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3192000091075897},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C199822604","wikidata":"https://www.wikidata.org/wiki/Q557120","display_name":"Duty cycle","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C111185680","wikidata":"https://www.wikidata.org/wiki/Q3866206","display_name":"Sensor node","level":5,"score":0.3034000098705292},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.27950000762939453},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.unibo.it:11585/1029010","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/11228464","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2491471302","https://openalex.org/W2587696068","https://openalex.org/W2605194071","https://openalex.org/W2905113794","https://openalex.org/W2996834926","https://openalex.org/W3010947518","https://openalex.org/W3100815891","https://openalex.org/W4200585074","https://openalex.org/W4220747418","https://openalex.org/W4220769521","https://openalex.org/W4220796147","https://openalex.org/W4283068167","https://openalex.org/W4283206147","https://openalex.org/W4293194164","https://openalex.org/W4309287541","https://openalex.org/W4320730661","https://openalex.org/W4385324340","https://openalex.org/W4385453482","https://openalex.org/W4386025563","https://openalex.org/W4386232244","https://openalex.org/W4388405532","https://openalex.org/W4389385925","https://openalex.org/W4390819076","https://openalex.org/W4392611940","https://openalex.org/W4396852839","https://openalex.org/W4400035737","https://openalex.org/W4402572025"],"related_works":[],"abstract_inverted_index":{"Smart":[0],"sensors":[1],"with":[2,186],"extreme-edge":[3],"data":[4,38,52,192],"processing":[5],"capabilities":[6],"allow":[7],"to":[8,28,37,43,51,138,144],"optimize":[9],"the":[10,29,34,48,58,65,73,88,91,94,98,120,124,134,141,146,156,161,165,168,172,178,197,201,213,233,238,247],"energy":[11,62,198,257],"budget":[12,199],"of":[13,61,90,102,123,167,237],"current":[14],"Structural":[15],"Health":[16],"Monitoring":[17],"(SHM)":[18],"systems.":[19],"Moving":[20],"inference":[21],"and":[22,77,97,143,194,241,256],"Deep":[23],"Neural":[24,129],"Networks":[25],"(DNNs)":[26],"directly":[27],"sensing":[30,203],"unit":[31],"can":[32,118,181,245],"bypass":[33],"constraints":[35],"due":[36,50],"transmission,":[39],"but":[40],"it":[41,85],"has":[42],"be":[44],"properly":[45,231],"balanced":[46],"against":[47],"expenditure":[49],"sampling,":[53],"which":[54],"might":[55],"easily":[56],"become":[57],"main":[59],"source":[60],"consumption":[63,258],"when":[64],"duty":[66],"cycling":[67],"is":[68,79,136],"not":[69],"chosen":[70],"appropriately.":[71],"Finding":[72],"optimal":[74,202],"sampling":[75,162,234,242],"time":[76],"frequency":[78],"a":[80,107,126,149,218],"non":[81],"trivial":[82],"task":[83],"because":[84],"depends":[86],"on":[87,133,164,171,217,223],"peculiarities":[89],"target":[92],"facility,":[93],"surrounding":[95],"environment,":[96],"highly":[99],"non-linear":[100],"behavior":[101],"DNNs.":[103],"This":[104],"paper":[105],"presents":[106],"fully":[108],"decentralized":[109],"scheme":[110],"for":[111],"vibration":[112],"damage":[113],"detection":[114],"where":[115],"each":[116],"sensor":[117,221],"predict":[119],"health":[121],"status":[122],"structure;":[125],"tiny":[127],"Convolutional":[128],"Network":[130],"(CNN)":[131],"hosted":[132],"node":[135],"entitled":[137],"autonomously":[139],"process":[140],"measurements":[142],"complete":[145],"inference.":[147],"As":[148],"further":[150],"contribution,":[151],"an":[152,224],"empirical":[153],"study":[154],"proves":[155],"major":[157],"role":[158],"played":[159],"by":[160,230],"parameters":[163,204,235],"performance":[166],"networks.":[169],"Experiments":[170],"Z24":[173],"bridge":[174],"benchmark":[175],"show":[176,228],"that":[177],"small-size":[179],"CNN":[180,215],"achieve":[182],"classification":[183,251],"scores":[184],"comparable":[185],"state-of-the-art":[187],"models":[188],"while":[189],"i)":[190],"avoiding":[191],"transmission":[193],"ii)":[195],"improving":[196],"if":[200],"are":[205,209],"chosen.":[206],"These":[207],"findings":[208],"obtained":[210],"after":[211],"deploying":[212],"sought":[214],"model":[216],"wireless":[219],"accelerometer":[220],"based":[222],"STM32L496":[225],"processor.":[226],"Results":[227],"that,":[229],"tuning":[232],"(length":[236],"acquisition":[239],"window":[240],"frequency),":[243],"one":[244],"reach":[246],"best":[248],"trade-off":[249],"between":[250],"performances":[252],"(accuracy":[253],"above":[254],"96%)":[255],"(less":[259],"than":[260],"40":[261],"mJ/h).":[262]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}