{"id":"https://openalex.org/W3137386018","doi":"https://doi.org/10.3390/computers10030034","title":"Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data\u2014A Hybrid Approach and Comparison Study","display_name":"Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data\u2014A Hybrid Approach and Comparison Study","publication_year":2021,"publication_date":"2021-03-18","ids":{"openalex":"https://openalex.org/W3137386018","doi":"https://doi.org/10.3390/computers10030034","mag":"3137386018"},"language":"en","primary_location":{"id":"doi:10.3390/computers10030034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers10030034","pdf_url":"https://www.mdpi.com/2073-431X/10/3/34/pdf?version=1616667419","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-431X/10/3/34/pdf?version=1616667419","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003888744","display_name":"Stefan Bosse","orcid":"https://orcid.org/0000-0002-8774-6141"},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Stefan Bosse","raw_affiliation_strings":["Department Mathematics & Computer Science, University of Bremen, 28359 Bremen, Germany"],"affiliations":[{"raw_affiliation_string":"Department Mathematics & Computer Science, University of Bremen, 28359 Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048309388","display_name":"Dennis M. Weiss","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dennis Weiss","raw_affiliation_strings":["Department Mathematics & Computer Science, University of Bremen, 28359 Bremen, Germany"],"affiliations":[{"raw_affiliation_string":"Department Mathematics & Computer Science, University of Bremen, 28359 Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022600554","display_name":"Daniel Schmidt","orcid":"https://orcid.org/0000-0001-8917-2041"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Schmidt","raw_affiliation_strings":["German Aerospace Center (DLR e. V.), Institute of Composite Structures and Adaptive Systems, 38108 Braunschweig, Germany"],"affiliations":[{"raw_affiliation_string":"German Aerospace Center (DLR e. V.), Institute of Composite Structures and Adaptive Systems, 38108 Braunschweig, Germany","institution_ids":["https://openalex.org/I2898391981"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003888744"],"corresponding_institution_ids":["https://openalex.org/I180437899"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":974,"currency":"EUR","value_usd":1050},"fwci":0.5266,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61508592,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"10","issue":"3","first_page":"34","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9995999932289124,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/structural-health-monitoring","display_name":"Structural health monitoring","score":0.8117440938949585},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7221970558166504},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6967411041259766},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6917968988418579},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5861626863479614},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5802698135375977},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5131514668464661},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4711624085903168},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4110938012599945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3320724070072174},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17100763320922852}],"concepts":[{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.8117440938949585},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7221970558166504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6967411041259766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6917968988418579},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5861626863479614},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5802698135375977},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5131514668464661},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4711624085903168},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4110938012599945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3320724070072174},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17100763320922852},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/computers10030034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers10030034","pdf_url":"https://www.mdpi.com/2073-431X/10/3/34/pdf?version=1616667419","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9106567ef46f4e52bd50889a5dace4ba","is_oa":true,"landing_page_url":"https://doaj.org/article/9106567ef46f4e52bd50889a5dace4ba","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computers, Vol 10, Iss 3, p 34 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/computers10030034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers10030034","pdf_url":"https://www.mdpi.com/2073-431X/10/3/34/pdf?version=1616667419","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3137386018.pdf","grobid_xml":"https://content.openalex.org/works/W3137386018.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W100404349","https://openalex.org/W1564524412","https://openalex.org/W1984749989","https://openalex.org/W1987111416","https://openalex.org/W2005385149","https://openalex.org/W2022856590","https://openalex.org/W2048773302","https://openalex.org/W2083681515","https://openalex.org/W2150869884","https://openalex.org/W2154234219","https://openalex.org/W2212370391","https://openalex.org/W2486438534","https://openalex.org/W2503666253","https://openalex.org/W2791957585","https://openalex.org/W3000058979","https://openalex.org/W3004017446","https://openalex.org/W3020988111","https://openalex.org/W3115075809","https://openalex.org/W3147968667"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4328117340","https://openalex.org/W2997921738","https://openalex.org/W4220926404","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W3123344745"],"abstract_inverted_index":{"Structural":[0],"health":[1],"monitoring":[2],"(SHM)":[3],"is":[4,34,74],"a":[5,15,134,180,218,235,272],"promising":[6],"technique":[7],"for":[8,83,173],"in-service":[9],"inspection":[10],"of":[11,18,71,77,126,133,217,248,260,277],"technical":[12],"structures":[13],"in":[14,20,131,148,274],"broad":[16],"field":[17],"applications":[19],"order":[21,276],"to":[22,145,234],"reduce":[23],"maintenance":[24],"efforts":[25],"as":[26,28,43],"well":[27],"the":[29,56,75,127,249,267,275,278],"overall":[30],"structural":[31],"weight.":[32],"SHM":[33],"basically":[35],"an":[36,124,186],"inverse":[37],"problem":[38],"deriving":[39],"physical":[40],"properties":[41],"such":[42],"damages":[44],"or":[45,121],"material":[46,220],"inhomogeneity":[47],"(target":[48],"features)":[49],"from":[50,211],"sensor":[51,150,171,209,279],"data.":[52],"Often":[53],"models":[54],"defining":[55],"relationship":[57],"between":[58],"predictable":[59],"features":[60],"and":[61,88,95,99,103,123,161,165,169,175,185,207,243,258,263],"sensors":[62],"are":[63,163,202],"required":[64],"but":[65],"not":[66],"available.":[67],"The":[68,110,137],"main":[69],"objective":[70],"this":[72],"work":[73],"investigation":[76],"model-free":[78],"distributed":[79,268],"machine":[80,158],"learning":[81,159,191],"(DML)":[82],"damage":[84,118,129,139,177,240,250,261],"diagnostics":[85,140],"under":[86],"resource":[87],"failure":[89],"constraints":[90],"by":[91,252,266],"using":[92],"multi-instance":[93,269],"ensemble":[94],"model":[96],"fusion":[97],"strategies":[98],"featuring":[100],"improved":[101,239],"scaling":[102],"stability":[104],"compared":[105,166],"with":[106,152,225,238,271],"centralised":[107],"single-instance":[108],"approaches.":[109],"diagnostic":[111],"system":[112],"delivers":[113],"two":[114],"features:":[115],"A":[116],"binary":[117],"classification":[119],"(damaged":[120],"non-damaged)":[122],"estimation":[125],"spatial":[128],"position":[130],"case":[132],"damaged":[135],"structure.":[136],"proposed":[138],"architecture":[141,237,270],"should":[142],"be":[143,146,232],"able":[144],"used":[147,203],"low-resource":[149,184],"networks":[151,201],"soft":[153],"real-time":[154],"capabilities.":[155],"Two":[156],"different":[157],"methodologies":[160],"architectures":[162,196,230],"evaluated":[164],"posing":[167],"low-":[168,174],"high-resolution":[170,176],"processing":[172],"diagnostics,":[178],"i.e.,":[179],"dedicated":[181],"supervised":[182],"trained":[183,188],"unsupervised":[187],"high-resource":[189],"deep":[190],"approach,":[192],"respectively.":[193],"In":[194],"both":[195,229,253],"state-based":[197],"recurrent":[198],"artificial":[199],"neural":[200],"that":[204],"process":[205],"spatially":[206],"time-resolved":[208],"data":[210],"experimental":[212],"ultrasonic":[213],"guided":[214],"wave":[215],"measurements":[216],"hybrid":[219,236],"(carbon":[221],"fibre":[222],"laminate)":[223],"plate":[224],"pseudo":[226],"defects.":[227],"Finally,":[228],"can":[231],"fused":[233],"detection":[241,262],"accuracy":[242,259],"reliability.":[244],"An":[245],"extensive":[246],"evaluation":[247],"prediction":[251],"systems":[254],"shows":[255],"high":[256],"reliability":[257],"localisation,":[264],"even":[265],"resolution":[273],"distance.":[280]},"counts_by_year":[{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}