{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T06:57:30Z","timestamp":1726469850098},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Industry"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.compind.2023.103939","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T09:44:34Z","timestamp":1683884674000},"page":"103939","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Full-cycle data purification strategy for multi-type weld seam classification with few-shot learning"],"prefix":"10.1016","volume":"150","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4908-1038","authenticated-orcid":false,"given":"Hongfei","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2494-8667","authenticated-orcid":false,"given":"Yingzhong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Long","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5954-0421","authenticated-orcid":false,"given":"Yuqian","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9290-3690","authenticated-orcid":false,"given":"Jiecai","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Fengfeng","family":"Xi","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compind.2023.103939_bib1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jenvman.2012.03.035","article-title":"Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach","volume":"104","author":"Arebey","year":"2012","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.compind.2023.103939_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106263","article-title":"Improved window adaptive gray level co-occurrence matrix for extraction and analysis of texture characteristics of pulmonary nodules","volume":"208","author":"Chen","year":"2021","journal-title":"Comput. Meth. Prog. Bio."},{"key":"10.1016\/j.compind.2023.103939_bib3","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.jmapro.2021.08.058","article-title":"Real-time sensing of gas metal arc welding process \u2013 a literature review and analysis","volume":"70","author":"Cheng","year":"2021","journal-title":"J. Manuf. Process"},{"issue":"3","key":"10.1016\/j.compind.2023.103939_bib4","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1109\/TII.2021.3090036","article-title":"Deep metric learning-based for multi-target few-shot pavement distress classification","volume":"18","author":"Dong","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"5\u20138","key":"10.1016\/j.compind.2023.103939_bib5","first-page":"2135","article-title":"Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding","volume":"101","author":"Du","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1\u20134","key":"10.1016\/j.compind.2023.103939_bib6","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s00170-017-0202-8","article-title":"Automatic recognition system of welding seam type based on SVM method","volume":"92","author":"Fan","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"2","key":"10.1016\/j.compind.2023.103939_bib7","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1109\/TII.2019.2919658","article-title":"An initial point alignment and seam-tracking system for narrow weld","volume":"16","author":"Fan","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"10","key":"10.1016\/j.compind.2023.103939_bib8","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TMI.2021.3060551","article-title":"Interactive few-shot learning: limited supervision, better medical image segmentation","volume":"40","author":"Feng","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compind.2023.103939_bib9","unstructured":"Hall-Beyer, M., 2017. GLCM texture: a tutorial v. 3.0 March 2017."},{"key":"10.1016\/j.compind.2023.103939_bib10","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"9","key":"10.1016\/j.compind.2023.103939_bib11","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TNSRE.2016.2644264","article-title":"Combining improved gray-level co-occurrence matrix with high density grid for myoelectric control robustness to electrode shift","volume":"25","author":"He","year":"2017","journal-title":"IEEE Trans. Neur. Syst. Reh. Eng."},{"issue":"1\u20134","key":"10.1016\/j.compind.2023.103939_bib12","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00170-012-3902-0","article-title":"Development of a real-time laser-based machine vision system to monitor and control welding processes","volume":"63","author":"Huang","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"6266","key":"10.1016\/j.compind.2023.103939_bib13","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1126\/science.aab3050","article-title":"Human-level concept learning through probabilistic program induction","volume":"350","author":"Lake","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.compind.2023.103939_bib14","first-page":"62","article-title":"A tactual weld seam tracking method in super narrow gap of thick plates","author":"Lei","year":"2020","journal-title":"Robot. Comput. -Integr. Manuf."},{"key":"10.1016\/j.compind.2023.103939_bib15","first-page":"123","article-title":"A review of vision-aided robotic welding","author":"Lei","year":"2020","journal-title":"Comput. Ind."},{"key":"10.1016\/j.compind.2023.103939_bib16","first-page":"1","article-title":"SCL-MLNet: boosting few-shot remote sensing scene classification via self-supervised contrastive learning","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compind.2023.103939_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2022.108388","article-title":"Multiple weld seam laser vision recognition method based on the IPCE algorithm","volume":"155","author":"Li","year":"2022","journal-title":"Opt. Laser Technol."},{"issue":"9","key":"10.1016\/j.compind.2023.103939_bib18","doi-asserted-by":"crossref","first-page":"7261","DOI":"10.1109\/TIE.2017.2694399","article-title":"Automatic welding seam tracking and identification","volume":"64","author":"Li","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.compind.2023.103939_bib19","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1016\/j.jmapro.2020.04.033","article-title":"An integrated process-performance model of ultrasonic composite welding based on finite element and artificial neural network","volume":"56","author":"Li","year":"2020","journal-title":"J. Manuf. Process"},{"key":"10.1016\/j.compind.2023.103939_bib20","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.jmsy.2020.06.010","article-title":"Smart manufacturing process and system automation \u2013 a critical review of the standards and envisioned scenarios","volume":"56","author":"Lu","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.compind.2023.103939_bib21","first-page":"112","article-title":"Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network","author":"Miao","year":"2019","journal-title":"Comput. Ind."},{"issue":"5","key":"10.1016\/j.compind.2023.103939_bib22","first-page":"1","article-title":"Image texture feature extraction using GLCM approach","volume":"3","author":"Mohanaiah","year":"2013","journal-title":"Int. J. Sci. Res. Publ."},{"issue":"12","key":"10.1016\/j.compind.2023.103939_bib23","doi-asserted-by":"crossref","first-page":"9641","DOI":"10.1109\/TIE.2019.2896165","article-title":"A smart monitoring system for automatic welding defect detection","volume":"66","author":"Sassi","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"4","key":"10.1016\/j.compind.2023.103939_bib24","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/21.35336","article-title":"Joint recognition and tracking for robotic arc welding","volume":"19","author":"Sicard","year":"1989","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"3","key":"10.1016\/j.compind.2023.103939_bib25","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jamcollsurg.2014.11.027","article-title":"Texture analysis of preoperative CT images for prediction of postoperative hepatic insufficiency: a preliminary study","volume":"220","author":"Simpson","year":"2015","journal-title":"J. Am. Coll. Surg."},{"issue":"4","key":"10.1016\/j.compind.2023.103939_bib26","doi-asserted-by":"crossref","first-page":"3588","DOI":"10.1109\/TIE.2020.2977553","article-title":"Few-shot learning for domain-specific fine-grained image classification","volume":"68","author":"Sun","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.compind.2023.103939_bib27","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/CVPR.2018.00131","article-title":"Learning to compare","author":"Sung","year":"2018","journal-title":"Relat. Netw. Few-Shot Learn. 2018 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit."},{"issue":"4","key":"10.1016\/j.compind.2023.103939_bib28","doi-asserted-by":"crossref","first-page":"5402","DOI":"10.1109\/JSEN.2020.3034382","article-title":"Automatic identification of multi-type weld seam based on vision sensor with silhouette-mapping","volume":"21","author":"Tian","year":"2021","journal-title":"IEEE Sens. J."},{"issue":"4","key":"10.1016\/j.compind.2023.103939_bib29","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s40436-020-00325-y","article-title":"Robust identification of weld seam based on region of interest operation","volume":"8","author":"Tian","year":"2020","journal-title":"Adv. Manuf."},{"key":"10.1016\/j.compind.2023.103939_bib30","first-page":"155","article-title":"Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions","author":"Wang","year":"2021","journal-title":"Mech. Syst. Signal Proc."},{"key":"10.1016\/j.compind.2023.103939_bib31","first-page":"61","article-title":"A robust weld seam recognition method under heavy noise based on structured-light vision","author":"Wang","year":"2020","journal-title":"Robot. Comput. -Integr. Manuf."},{"key":"10.1016\/j.compind.2023.103939_bib32","first-page":"297","article-title":"An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding","author":"Xiao","year":"2019","journal-title":"Sens. Actuator A-Phys."},{"issue":"21","key":"10.1016\/j.compind.2023.103939_bib33","doi-asserted-by":"crossref","first-page":"8631","DOI":"10.1109\/JSEN.2018.2867581","article-title":"A high-speed seam extraction method based on the novel structured-light sensor for arc welding robot: a review","volume":"18","author":"Yang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.compind.2023.103939_bib34","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2016.02.061","article-title":"Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix","volume":"197","author":"Yang","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compind.2023.103939_bib35","first-page":"138","article-title":"Fabric defect classification using prototypical network of few-shot learning algorithm","author":"Zhan","year":"2022","journal-title":"Comput. Ind."},{"key":"10.1016\/j.compind.2023.103939_bib36","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.jmapro.2018.08.014","article-title":"Identification of the deviation of seam tracking and weld cross type for the derusting of ship hulls using a wall-climbing robot based on three-line laser structural light","volume":"35","author":"Zhang","year":"2018","journal-title":"J. Manuf. Process"},{"issue":"9","key":"10.1016\/j.compind.2023.103939_bib37","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1109\/TGRS.2018.2812778","article-title":"Development of a gray-level co-occurrence matrix-based texture orientation estimation method and its application in sea surface wind direction retrieval from SAR imagery","volume":"56","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"10.1016\/j.compind.2023.103939_bib38","doi-asserted-by":"crossref","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","article-title":"Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems","volume":"17","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Computers in Industry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0166361523000891?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0166361523000891?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:45:05Z","timestamp":1711597505000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0166361523000891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":38,"alternative-id":["S0166361523000891"],"URL":"https:\/\/doi.org\/10.1016\/j.compind.2023.103939","relation":{},"ISSN":["0166-3615"],"issn-type":[{"type":"print","value":"0166-3615"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Full-cycle data purification strategy for multi-type weld seam classification with few-shot learning","name":"articletitle","label":"Article Title"},{"value":"Computers in Industry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compind.2023.103939","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"103939"}}