{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:05:53Z","timestamp":1781193953652,"version":"3.54.1"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2024A1515011976"],"award-info":[{"award-number":["2024A1515011976"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12171114"],"award-info":[{"award-number":["12171114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132025","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:20:12Z","timestamp":1774545612000},"page":"132025","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Geometry-constrained open set recognition with frozen foundation model features for industrial inspection"],"prefix":"10.1016","volume":"320","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8189-8118","authenticated-orcid":false,"given":"Yingjun","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9652-1349","authenticated-orcid":false,"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3157-5284","authenticated-orcid":false,"given":"Luyu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9272-4804","authenticated-orcid":false,"given":"Siyuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2588-626X","authenticated-orcid":false,"given":"Xiangjun","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7376-2925","authenticated-orcid":false,"given":"Lingxi","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132025_bib0001","article-title":"Cost-sensitive machine learning for predicting production defects: A novel approach based on metacost","volume":"299","author":"Alsulaiman","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132025_bib0002","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"1563","article-title":"Towards open set deep networks","author":"Bendale","year":"2016"},{"key":"10.1016\/j.eswa.2026.132025_bib0003","first-page":"12345","article-title":"Learning uniform hyperspherical centers for open and closed set recognition","volume":"12","author":"Cevikalp","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132025_bib0004","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision workshops (ICCVW)","first-page":"3359","article-title":"Sam-adapter: Adapting segment anything in underperformed scenes","author":"Chen","year":"2023"},{"key":"10.1016\/j.eswa.2026.132025_bib0005","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"4690","article-title":"Arcface: Additive angular margin loss for deep face recognition","author":"Deng","year":"2019"},{"issue":"2","key":"10.1016\/j.eswa.2026.132025_bib0006","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1049\/trit.2019.0019","article-title":"TDD-Net: A tiny defect detection network for printed circuit boards","volume":"4","author":"Ding","year":"2019","journal-title":"CAAI Transactions on Intelligence Technology"},{"key":"10.1016\/j.eswa.2026.132025_bib0007","series-title":"Proceedings of the international conference on learning representations (ICLR)","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"10","key":"10.1016\/j.eswa.2026.132025_bib0008","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent advances in open set recognition: A survey","volume":"43","author":"Geng","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132025_bib0009","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"5","key":"10.1016\/j.eswa.2026.132025_bib0010","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","article-title":"An end-to-end steel surface defect detection approach via fusing multiple hierarchical features","volume":"69","author":"He","year":"2020","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.132025_bib0011","series-title":"Proceedings of the international conference on learning representations (ICLR)","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"Hendrycks","year":"2017"},{"key":"10.1016\/j.eswa.2026.132025_bib0012","article-title":"Decision fusion approach for detecting unknown wafer bin map patterns based on a deep multitask learning model","volume":"215","author":"Jang","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"22","key":"10.1016\/j.eswa.2026.132025_bib0013","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.3390\/electronics13224467","article-title":"Hybrid-DC: A hybrid framework using resnet-50 and vision transformer for steel surface defect classification in the rolling process","volume":"13","author":"Lee","year":"2024","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.eswa.2026.132025_bib0014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TDEI.2018.007315","article-title":"Defect recognition for partial discharge patterns of gas insulated switchgear and cable joint based on deep learning methods","volume":"31","author":"Li","year":"2024","journal-title":"IEEE Transactions on Dielectrics and Electrical Insulation"},{"key":"10.1016\/j.eswa.2026.132025_bib0015","series-title":"Advances in neural information processing systems (neurIPS)","article-title":"Energy-based out-of-distribution detection","author":"Liu","year":"2020"},{"issue":"6","key":"10.1016\/j.eswa.2026.132025_bib0016","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.3390\/s20061562","article-title":"Deep metallic surface defect detection: The new benchmark and detection network","volume":"20","author":"Lv","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2026.132025_bib0017","unstructured":"Oquab, M. et al. (2023). Dinov2: Learning robust visual features without supervision. arXiv: 2304.07193,."},{"key":"10.1016\/j.eswa.2026.132025_bib0018","series-title":"Proceedings of the international conference on machine learning (ICML)","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.eswa.2026.132025_bib0019","series-title":"International conference on machine learning (ICML)","first-page":"4393","article-title":"Deep one-class classification","author":"Ruff","year":"2018"},{"key":"10.1016\/j.eswa.2026.132025_bib0020","series-title":"2024 International conference on advances in electrical and communication technologies (ICAECOT)","first-page":"409","article-title":"Deep learning for partial discharge classification: A CNN-based approach","author":"Sahnoune","year":"2024"},{"issue":"7","key":"10.1016\/j.eswa.2026.132025_bib0021","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","article-title":"Toward open set recognition","volume":"35","author":"Scheirer","year":"2013","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132025_bib0022","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","article-title":"A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects","volume":"285","author":"Song","year":"2013","journal-title":"Applied Surface Science"},{"issue":"2","key":"10.1016\/j.eswa.2026.132025_bib0023","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1049\/ipr2.12647","article-title":"Review of surface defect detection of steel products based on machine vision","volume":"17","author":"Tang","year":"2023","journal-title":"IET Image Processing"},{"issue":"11\u201313","key":"10.1016\/j.eswa.2026.132025_bib0024","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","article-title":"Support vector domain description","volume":"20","author":"Tax","year":"1999","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.eswa.2026.132025_bib0025","unstructured":"University, L. (2024). Prpd dataset (Version 4). Roboflow Universehttps:\/\/universe.roboflow.com\/laghouat-university\/prpd\/dataset\/4."},{"key":"10.1016\/j.eswa.2026.132025_bib0026","series-title":"Proceedings of the international conference on learning representations (ICLR)","article-title":"Open-set recognition: A good closed-set classifier is all you need","author":"Vaze","year":"2022"},{"key":"10.1016\/j.eswa.2026.132025_bib0027","first-page":"1","article-title":"Sphere loss: Learning discriminative features for scene classification in a hyperspherical feature space","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132025_bib0028","series-title":"European conference on computer vision (ECCV)","first-page":"499","article-title":"A discriminative feature learning approach for deep face recognition","author":"Wen","year":"2016"},{"issue":"8","key":"10.1016\/j.eswa.2026.132025_bib0029","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.3390\/s25082417","article-title":"Leveraging vision foundation model via PConv-based fine-tuning with automated prompter for defect segmentation","volume":"25","author":"Xiao","year":"2025","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.eswa.2026.132025_bib0030","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TII.2021.3070324","article-title":"Deep-learning-based open set fault diagnosis by extreme value theory","volume":"18","author":"Yu","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"6","key":"10.1016\/j.eswa.2026.132025_bib0031","first-page":"2890","article-title":"Partial discharge pattern recognition based on morphological features and faster R-CNN-alexnet for generator stator","volume":"31","author":"Zhang","year":"2024","journal-title":"IEEE Transactions on Dielectrics and Electrical Insulation"},{"key":"10.1016\/j.eswa.2026.132025_bib0032","article-title":"GDALR: Global dual attention and local representations in vision transformer for surface defect detection","volume":"224","author":"Zhou","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.eswa.2026.132025_bib0033","article-title":"Dataset of phase-resolved images of internal, corona and surface partial discharges in electrical generators","volume":"52","author":"Zorrilla Henao","year":"2024","journal-title":"Data in Brief"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426009383?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426009383?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:53:48Z","timestamp":1781193228000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426009383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":33,"alternative-id":["S0957417426009383"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132025","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Geometry-constrained open set recognition with frozen foundation model features for industrial inspection","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132025","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132025"}}