{"id":"https://openalex.org/W4415180315","doi":"https://doi.org/10.23919/ecc65951.2025.11187195","title":"Robust vision-in-the-loop system through NN fine-tuning using digital twins","display_name":"Robust vision-in-the-loop system through NN fine-tuning using digital twins","publication_year":2025,"publication_date":"2025-06-24","ids":{"openalex":"https://openalex.org/W4415180315","doi":"https://doi.org/10.23919/ecc65951.2025.11187195"},"language":"en","primary_location":{"id":"doi:10.23919/ecc65951.2025.11187195","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ecc65951.2025.11187195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 European Control Conference (ECC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.tue.nl/ws/files/390966997/Robust_vision-in-the-loop_system_through_NN_fine-tuning_using_digital_twins.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026304916","display_name":"Vibhor Jain","orcid":"https://orcid.org/0000-0003-1836-522X"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Vibhor Jain","raw_affiliation_strings":["Eindhoven University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119998323","display_name":"Chris Wegter","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Chris Wegter","raw_affiliation_strings":["Eindhoven University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032436846","display_name":"Sander Stuijk","orcid":"https://orcid.org/0000-0002-2518-6847"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Sander Stuijk","raw_affiliation_strings":["Eindhoven University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049989469","display_name":"Dip Goswami","orcid":"https://orcid.org/0000-0002-2268-0014"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dip Goswami","raw_affiliation_strings":["Eindhoven University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24898047,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3095","last_page":"3100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9807999730110168,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/robustness","display_name":"Robustness (evolution)","score":0.8277000188827515},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48410001397132874},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41760000586509705},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.3582000136375427},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.3319000005722046},{"id":"https://openalex.org/keywords/control-system","display_name":"Control system","score":0.3287000060081482},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.3075999915599823}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8277000188827515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027000188827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5482000112533569},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4090999960899353},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3582000136375427},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.3075999915599823},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29260000586509705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2849999964237213},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2549000084400177},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/ecc65951.2025.11187195","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ecc65951.2025.11187195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 European Control Conference (ECC)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/028372fe-6572-49e3-be37-c083d52d999b","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/028372fe-6572-49e3-be37-c083d52d999b","pdf_url":"https://pure.tue.nl/ws/files/390966997/Robust_vision-in-the-loop_system_through_NN_fine-tuning_using_digital_twins.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jain, V, Wegter, C, Stuijk, S & Goswami, D 2025, Robust vision-in-the-loop system through NN fine-tuning using digital twins. in 2025 European Control Conference, ECC 2025., 11187195, Institute of Electrical and Electronics Engineers, pp. 3095-3100, 2025 European Control Conference, ECC 2025, Thessaloniki, Greece, 24/06/25. https://doi.org/10.23919/ECC65951.2025.11187195","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/028372fe-6572-49e3-be37-c083d52d999b","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/028372fe-6572-49e3-be37-c083d52d999b","pdf_url":"https://pure.tue.nl/ws/files/390966997/Robust_vision-in-the-loop_system_through_NN_fine-tuning_using_digital_twins.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jain, V, Wegter, C, Stuijk, S & Goswami, D 2025, Robust vision-in-the-loop system through NN fine-tuning using digital twins. in 2025 European Control Conference, ECC 2025., 11187195, Institute of Electrical and Electronics Engineers, pp. 3095-3100, 2025 European Control Conference, ECC 2025, Thessaloniki, Greece, 24/06/25. https://doi.org/10.23919/ECC65951.2025.11187195","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415180315.pdf","grobid_xml":"https://content.openalex.org/works/W4415180315.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many":[0],"autonomous":[1],"systems":[2],"are":[3],"increasingly":[4],"adopting":[5],"Neural":[6],"Networks":[7],"(NNs)":[8],"based":[9],"perception":[10,38],"in":[11,52,66,94,201],"vision-in-the-loop":[12],"(VIL)":[13],"control":[14],"systems.":[15,97],"In":[16,98],"many":[17,95],"industrial":[18,96],"applications,":[19],"the":[20,27,37,43,59,81,107,150,198,202],"features":[21,130],"(shape,":[22],"size":[23],"and":[24,91,124,134,159,180,189],"texture)":[25],"of":[26,29,42,73,80,183,185,204],"object":[28,132],"interest":[30],"varies,":[31],"which":[32],"imposes":[33,46],"robustness":[34,79,160],"requirements":[35],"on":[36,161],"algorithm.":[39],"Further,":[40],"performance":[41,158],"VIL":[44,53,111,173,195],"system":[45,54,177,196],"strict":[47],"latency":[48],"requirements.":[49],"Using":[50],"NNs":[51,108],"poses":[55],"two":[56],"challenges.":[57],"First,":[58],"NN":[60,83,141],"models":[61],"should":[62],"be":[63],"lightweight":[64,82,140],"resulting":[65],"a":[67,139,162,172],"low":[68],"closed-loop":[69],"latency.":[70],"Second,":[71],"availability":[72],"representative":[74],"training":[75,87,106,126,154],"data":[76,88,127,155],"for":[77,105,110,121],"ensuring":[78],"models.":[84],"Collecting":[85],"such":[86],"is":[89,119,169],"expensive":[90],"often,":[92],"infeasible":[93],"this":[99],"work":[100],"we":[101],"propose":[102],"an":[103],"approach":[104,146,168],"used":[109,120],"applications":[112],"using":[113,152],"digital":[114],"twins":[115],"(DT).":[116],"The":[117,167,194],"DT":[118],"automatically":[122],"generating":[123],"labelling":[125],"representing":[128],"various":[129],"like":[131],"shapes":[133],"directional":[135],"lighting.":[136],"Starting":[137],"from":[138],"base":[142],"model,":[143],"our":[144],"proposed":[145],"fine-tunes":[147],"or":[148],"retrains":[149],"model":[151],"DT-generated":[153],"achieving":[156],"desired":[157],"different":[163],"target":[164],"operating":[165],"condition.":[166],"validated":[170],"considering":[171],"semiconductor":[174],"motion":[175],"stage":[176],"with":[178,211],"square":[179],"rectangular":[181],"dies":[182],"dimension":[184],"(0.5cm":[186,190],"\u00d7":[187,191],"0.5cm)":[188],"+1cm)":[192],"respectively.":[193],"limits":[197],"positioning":[199,209],"error":[200,210],"range":[203],"2%":[205],"compared":[206],"to":[207],"12%":[208],"no":[212],"vision":[213],"feedback.":[214]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-15T00:00:00"}
