{"id":"https://openalex.org/W2904038175","doi":null,"title":"Data Sampling and Reasoning: Harnessing Optimization and Machine Learning for Design and System Identification","display_name":"Data Sampling and Reasoning: Harnessing Optimization and Machine Learning for Design and System Identification","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2904038175","mag":"2904038175"},"language":"en","primary_location":{"id":"pmh:oai:opencommons.uconn.edu:dissertations-8257","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1990","pdf_url":"https://digitalcommons.lib.uconn.edu/cgi/viewcontent.cgi?article=8257&context=dissertations","source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://digitalcommons.lib.uconn.edu/cgi/viewcontent.cgi?article=8257&context=dissertations","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Pei Cao","orcid":"https://orcid.org/0000-0003-4768-4844","observed_orcids":["https://orcid.org/0000-0003-4768-4844"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pei Cao","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4768-4844","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.8779000043869019,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.8779000043869019,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.06539999693632126,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.01940000057220459,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"multi-objective optimization","score":0.753000020980835},{"id":"https://openalex.org/keywords/sampling-based-optimization","display_name":"sampling optimization","score":0.6549999713897705},{"id":"https://openalex.org/keywords/system-identification","display_name":"system identification","score":0.699999988079071},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"simulated annealing","score":0.6740000247955322},{"id":"https://openalex.org/keywords/piezoelectric-transducers","display_name":"piezoelectric transducers","score":0.7889999747276306},{"id":"https://openalex.org/keywords/damage-detection","display_name":"damage detection","score":0.8029999732971191},{"id":"https://openalex.org/keywords/inverse-analysis","display_name":"inverse analysis","score":0.6119999885559082},{"id":"https://openalex.org/keywords/l0-norm-sparsity","display_name":"l0-norm sparsity","score":0.8050000071525574},{"id":"https://openalex.org/keywords/direct-algorithm","display_name":"DIRECT algorithm","score":0.7210000157356262},{"id":"https://openalex.org/keywords/gear-fault-diagnosis","display_name":"gear fault diagnosis","score":0.8799999952316284},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.7919999957084656},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.996999979019165},{"id":"https://openalex.org/keywords/data-driven-engineering","display_name":"data-driven engineering","score":0.3840000033378601},{"id":"https://openalex.org/keywords/mechanical-component-design","display_name":"mechanical component design","score":0.4300000071525574},{"id":"https://openalex.org/keywords/structural-health-monitoring","display_name":"structural health monitoring","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5938979387283325},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5852839946746826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5780297517776489},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.527032196521759},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44601255655288696},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1101619303226471},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:opencommons.uconn.edu:dissertations-8257","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1990","pdf_url":"https://digitalcommons.lib.uconn.edu/cgi/viewcontent.cgi?article=8257&context=dissertations","source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"pmh:oai:digitalcommons.lib.uconn.edu:dissertations-8257","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.lib.uconn.edu/dissertations/1990","pdf_url":null,"source":{"id":"https://openalex.org/S4306401180","display_name":"OpenCommons at University of Connecticut (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"mag:2904038175","doi":null,"is_oa":false,"landing_page_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8257&context=dissertations","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:opencommons.uconn.edu:dissertations-8257","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1990","pdf_url":"https://digitalcommons.lib.uconn.edu/cgi/viewcontent.cgi?article=8257&context=dissertations","source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2904038175.pdf","grobid_xml":"https://content.openalex.org/works/W2904038175.grobid-xml"},"referenced_works_count":177,"referenced_works":["https://openalex.org/W11034078","https://openalex.org/W49657471","https://openalex.org/W112854523","https://openalex.org/W186577860","https://openalex.org/W375527499","https://openalex.org/W603961186","https://openalex.org/W625540141","https://openalex.org/W849878864","https://openalex.org/W1487583988","https://openalex.org/W1503030868","https://openalex.org/W1504943474","https://openalex.org/W1513657552","https://openalex.org/W1514632510","https://openalex.org/W1531455566","https://openalex.org/W1532701078","https://openalex.org/W1536647479","https://openalex.org/W1549353496","https://openalex.org/W1570584007","https://openalex.org/W1581422098","https://openalex.org/W1595498733","https://openalex.org/W1662894842","https://openalex.org/W1746819321","https://openalex.org/W1832693441","https://openalex.org/W1846834309","https://openalex.org/W1907282891","https://openalex.org/W1935926926","https://openalex.org/W1964073743","https://openalex.org/W1965624190","https://openalex.org/W1967559043","https://openalex.org/W1967734163","https://openalex.org/W1969483458","https://openalex.org/W1970368082","https://openalex.org/W1970590690","https://openalex.org/W1971784203","https://openalex.org/W1973333099","https://openalex.org/W1973441240","https://openalex.org/W1974008473","https://openalex.org/W1974937407","https://openalex.org/W1975748424","https://openalex.org/W1979425637","https://openalex.org/W1984078293","https://openalex.org/W1985110503","https://openalex.org/W1989380511","https://openalex.org/W1989723858","https://openalex.org/W1990057974","https://openalex.org/W1990865608","https://openalex.org/W1996445475","https://openalex.org/W2002812501","https://openalex.org/W2003412293","https://openalex.org/W2003708116","https://openalex.org/W2003837369","https://openalex.org/W2005091691","https://openalex.org/W2005934359","https://openalex.org/W2006850854","https://openalex.org/W2008813646","https://openalex.org/W2011039300","https://openalex.org/W2011406035","https://openalex.org/W2014064671","https://openalex.org/W2018978992","https://openalex.org/W2022528239","https://openalex.org/W2024060531","https://openalex.org/W2025158395","https://openalex.org/W2026735869","https://openalex.org/W2027456229","https://openalex.org/W2030113781","https://openalex.org/W2032711440","https://openalex.org/W2039951318","https://openalex.org/W2047957546","https://openalex.org/W2048325170","https://openalex.org/W2048366656","https://openalex.org/W2048424596","https://openalex.org/W2053967096","https://openalex.org/W2054944014","https://openalex.org/W2056605897","https://openalex.org/W2057085895","https://openalex.org/W2057513520","https://openalex.org/W2057593450","https://openalex.org/W2058510050","https://openalex.org/W2059387105","https://openalex.org/W2060024451","https://openalex.org/W2060203194","https://openalex.org/W2060304859","https://openalex.org/W2061834266","https://openalex.org/W2063375245","https://openalex.org/W2066636721","https://openalex.org/W2067508429","https://openalex.org/W2069300623","https://openalex.org/W2070458991","https://openalex.org/W2071947137","https://openalex.org/W2073191554","https://openalex.org/W2073441514","https://openalex.org/W2074052772","https://openalex.org/W2076866295","https://openalex.org/W2085137213","https://openalex.org/W2085830763","https://openalex.org/W2086726443","https://openalex.org/W2089018504","https://openalex.org/W2089314366","https://openalex.org/W2095705004","https://openalex.org/W2098459033","https://openalex.org/W2100522523","https://openalex.org/W2104492856","https://openalex.org/W2106334424","https://openalex.org/W2107020172","https://openalex.org/W2107726111","https://openalex.org/W2108567935","https://openalex.org/W2109880321","https://openalex.org/W2110905216","https://openalex.org/W2113347106","https://openalex.org/W2113741278","https://openalex.org/W2114725328","https://openalex.org/W2116661285","https://openalex.org/W2120482366","https://openalex.org/W2126105956","https://openalex.org/W2126726281","https://openalex.org/W2128459200","https://openalex.org/W2131065845","https://openalex.org/W2137028279","https://openalex.org/W2140759234","https://openalex.org/W2143185749","https://openalex.org/W2144187919","https://openalex.org/W2145096794","https://openalex.org/W2145479420","https://openalex.org/W2148154358","https://openalex.org/W2149933564","https://openalex.org/W2151267926","https://openalex.org/W2152679156","https://openalex.org/W2154579312","https://openalex.org/W2162691597","https://openalex.org/W2163178194","https://openalex.org/W2163605009","https://openalex.org/W2169528473","https://openalex.org/W2169878732","https://openalex.org/W2171330332","https://openalex.org/W2185903149","https://openalex.org/W2197519229","https://openalex.org/W2263076435","https://openalex.org/W2283268937","https://openalex.org/W2287029277","https://openalex.org/W2288585628","https://openalex.org/W2296616510","https://openalex.org/W2298644941","https://openalex.org/W2309693750","https://openalex.org/W2326740504","https://openalex.org/W2345111710","https://openalex.org/W2418691539","https://openalex.org/W2431407304","https://openalex.org/W2461729787","https://openalex.org/W2478027467","https://openalex.org/W2499720122","https://openalex.org/W2506071044","https://openalex.org/W2512027896","https://openalex.org/W2556345765","https://openalex.org/W2559743179","https://openalex.org/W2560109630","https://openalex.org/W2561840918","https://openalex.org/W2577761826","https://openalex.org/W2585874231","https://openalex.org/W2611243847","https://openalex.org/W2612216687","https://openalex.org/W2731372149","https://openalex.org/W2734897736","https://openalex.org/W2739500354","https://openalex.org/W2761154946","https://openalex.org/W2762736364","https://openalex.org/W2763806382","https://openalex.org/W2766819698","https://openalex.org/W2766977507","https://openalex.org/W2767988841","https://openalex.org/W2774799914","https://openalex.org/W2804011612","https://openalex.org/W2807344737","https://openalex.org/W2911964244","https://openalex.org/W2963542991","https://openalex.org/W2964121744","https://openalex.org/W3022683862","https://openalex.org/W4249678223"],"related_works":["https://openalex.org/W7161471595","https://openalex.org/W2772339188","https://openalex.org/W3084788384","https://openalex.org/W3022952367","https://openalex.org/W2781579237","https://openalex.org/W4224056081","https://openalex.org/W4246504226","https://openalex.org/W2042828149","https://openalex.org/W7220777430","https://openalex.org/W2898359225"],"abstract_inverted_index":{"The":[0,128,289,346,376],"recently":[1],"rapid":[2],"advancements":[3,49],"in":[4,14,50,136,163,201],"sensing":[5],"devices":[6],"and":[7,19,24,40,53,58,83,89,141,187,271,286,343,388,417],"computational":[8],"power":[9],"have":[10,209],"caused":[11],"paradigm":[12],"shift":[13],"engineering":[15,93],"analyses:":[16],"data":[17,51,72],"driven":[18],"sampling-based":[20,31],"approaches":[21],"play":[22],"more":[23,25],"important":[26],"roles.":[27],"For":[28],"example,":[29],"with":[30,70,118,153,170],"global":[32],"optimization":[33,82,133,149,266],"techniques,":[34],"mechanical":[35],"components":[36,112],"can":[37,44,64,368],"be":[38,45,65,115,370],"refined":[39],"redesigned;":[41],"system":[42,96,237,250,374,382],"parameters":[43],"accurately":[46],"identified.":[47],"With":[48,268],"mining":[52],"machine":[54,84],"learning,":[55],"underlying":[56],"input":[57],"output":[59],"relations":[60],"of":[61,105,125,130,167,206,223,236,244,291,313,352,378,425,454],"complex":[62],"systems":[63],"developed":[66,88],"to":[67,74,91,98,114,159,181,228,321],"make":[68],"predictions":[69,285],"unseen":[71],"or":[73],"expedite":[75],"analytical":[76],"process.":[77],"In":[78,214],"this":[79,183,215,339],"dissertation,":[80],"advanced":[81],"learning":[85,436],"techniques":[86],"are":[87,143,157,395,403],"employed":[90],"multi-facet":[92],"tasks":[94],"from":[95],"design":[97,148],"identification.\\nFirst":[99],"research":[100],"task":[101,348],"concerns":[102,349],"a":[103,224,229,241,264,310,336,373,379,422,430,451],"type":[104],"configuration":[106],"designs,":[107],"where":[108,326],"the":[109,123,139,147,164,207,220,234,253,259,280,292,302,323,327,385,389,406],"volume":[110],"occupied":[111],"is":[113,134,199,279,295,319,329,356],"minimized":[116],"along":[117],"other":[119,407],"objectives":[120,140],"such":[121,247],"as":[122,138,273,297],"length":[124],"connectivity":[126],"lines.":[127],"formulation":[129],"computationally":[131],"tractable":[132],"difficult":[135],"practice":[137],"constraints":[142,155],"usually":[144,151],"complex.":[145],"Moreover,":[146],"problems":[150],"come":[152],"demanding":[154],"that":[156,191,251],"hard":[158],"satisfy,":[160],"which":[161,355,402,438],"results":[162],"critical":[165],"challenge":[166],"balancing":[168],"feasibility":[169],"optimality.":[171],"We":[172,428],"develop":[173],"an":[174,248],"enhanced":[175],"multi-objective":[176,265,316],"simulated":[177],"annealing":[178],"approach,":[179,437],"MOSA/R,":[180],"solve":[182],"problem.":[184],"A":[185,315],"versatile":[186],"efficient":[188],"re-seed":[189],"scheme":[190],"allows":[192],"biased":[193],"search":[194],"while":[195],"avoiding":[196],"pre-mature":[197],"convergence":[198],"designed":[200],"MOSA/R.":[202],"Some":[203],"generalization":[204],"studies":[205],"algorithm":[208,317],"also":[210,448],"been":[211],"carried":[212],"out.":[213],"second":[216],"task,":[217],"we":[218,257],"exploit":[219],"impedance/admittance":[221],"change":[222],"piezoelectric":[225],"transducer":[226],"bonded":[227],"host":[230],"structure,":[231],"aiming":[232],"at":[233,363,372],"identification":[235,261],"damage.":[238],"To":[239],"find":[240],"small":[242,311,452],"set":[243,424,453],"solutions":[245],"for":[246],"under-determined":[249],"indicates":[252],"true":[254],"damage":[255,260,269,306],"scenario,":[256],"cast":[258],"problem":[262],"into":[263],"framework.":[267],"locations":[270],"severities":[272],"unknown":[274,293],"variables,":[275],"one":[276],"objective":[277,299],"function":[278],"discrepancy":[281],"between":[282],"first-principal":[283],"model":[284],"actual":[287],"measurements.":[288],"sparsity":[290,328],"variables":[294],"chosen":[296],"another":[298],"function,":[300],"deliberately,":[301],"l0":[303],"norm,":[304],"because":[305,358],"occurrence":[307],"generally":[308],"affects":[309],"number":[312],"elements.":[314],"(DIRECT)":[318],"devised":[320],"facilitate":[322],"inverse":[324],"analysis":[325],"further":[330],"emphasized":[331],"by":[332],"sigmoid":[333],"transformation.":[334],"As":[335],"deterministic":[337],"technique,":[338],"approach":[340],"yields":[341],"repeatable":[342],"conclusive":[344],"results.":[345],"third":[347],"early":[350],"diagnosis":[351,381],"gear":[353,359],"transmission,":[354],"challenging":[357],"faults":[360],"occur":[361],"primarily":[362],"microstructure":[364],"but":[365,447],"their":[366],"effects":[367],"only":[369,440,450],"observed":[371],"level.":[375],"performance":[377],"fault":[380],"depends":[383],"on":[384,399],"features":[386,394],"extracted":[387],"classifier":[390],"subsequently":[391],"applied.":[392],"Fault-related":[393],"conventionally":[396],"identified":[397],"based":[398],"domain":[400],"expertise,":[401],"system-specific.":[404],"On":[405],"hand,":[408],"although":[409],"deep":[410,431],"neural":[411,433],"networks":[412],"enjoy":[413],"adaptive":[414,444],"feature":[415,445],"extractions":[416],"inherent":[418],"classifications,":[419],"they":[420],"require":[421],"substantial":[423],"training":[426,455],"data.":[427,456],"present":[429],"convolutional":[432],"network-based":[434],"transfer":[435],"not":[439],"entertains":[441],"preprocessing":[442],"free":[443],"extractions,":[446],"requires":[449]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2018-12-22T00:00:00"}