{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:34:55Z","timestamp":1769765695419,"version":"3.49.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11042-023-15641-1","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T04:01:49Z","timestamp":1683086509000},"page":"45489-45501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid sampling feature enhancement: a few-shot learning method for substation equipment fault recognition"],"prefix":"10.1007","volume":"82","author":[{"given":"Yongjie","family":"Zhai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6969-9951","authenticated-orcid":false,"given":"Zhedong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaru","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"15641_CR1","doi-asserted-by":"crossref","unstructured":"Alfassy A, Karlinsky L, Aides A, Shtok J, Harary S, Feris R, Giryes R, Bronstein AM (2019) Laso: Label-set operations networks for multi-label few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6548\u20136557","DOI":"10.1109\/CVPR.2019.00671"},{"issue":"6","key":"15641_CR2","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/electronics9061030","volume":"9","author":"SS Ali","year":"2020","unstructured":"Ali SS, Choi BJ (2020) State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics 9(6):1030","journal-title":"Electronics"},{"key":"15641_CR3","doi-asserted-by":"crossref","unstructured":"Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340","DOI":"10.1007\/978-3-030-01424-7_58"},{"key":"15641_CR4","unstructured":"Bengio Y, Mesnil G, Dauphin Y, Rifai S (2013) Better mixing via deep representations. In: International Conference on Machine Learning, pp. 552\u2013560. PMLR"},{"issue":"3","key":"15641_CR5","first-page":"364","volume":"13","author":"J Ding","year":"2016","unstructured":"Ding J, Chen B, Liu H, Huang M (2016) Convolutional neural network with data augmentation for sar target recognition. IEEE Geosci Remote Sens Lett 13(3):364\u2013368","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"15641_CR6","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"15641_CR7","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/MPE.2019.2908122","volume":"17","author":"R Hunt","year":"2019","unstructured":"Hunt R, Flynn B, Smith T (2019) The substation of the future: Moving toward a digital solution. IEEE Power Ener Maga 17(4):47\u201355","journal-title":"IEEE Power Ener Maga"},{"key":"15641_CR8","volume":"54","author":"P Kreimel","year":"2020","unstructured":"Kreimel P, Eigner O, Mercaldo F, Santone A, Tavolato P (2020) Anomaly detection in substation networks. J Inform Sec Appli 54:102527","journal-title":"J Inform Sec Appli"},{"key":"15641_CR9","first-page":"8577","volume":"33","author":"B Li","year":"2019","unstructured":"Li B, Liu Y, Wang X (2019) Gradient harmonized single-stage detector. Proc AAAI Conf Artificial Int 33:8577\u20138584","journal-title":"Proc AAAI Conf Artificial Int"},{"issue":"2","key":"15641_CR10","first-page":"337","volume":"45","author":"Y Liu","year":"2019","unstructured":"Liu Y, Xu Z, Li G, Xia Y, Gao S (2019) Review on applications of artificial intelligence driven data analysis technology in condition-based maintenance of power transformers. High Voltage Engin 45(2):337\u2013348","journal-title":"High Voltage Engin"},{"key":"15641_CR11","unstructured":"Lowe R, Wu YI, Tamar A, Harb J, Pieter Abbeel O, Mordatch I (2017) Multi-agent actor-critic for mixed cooperative-competitive environments. Adv Neural Inf Proces Syst 30"},{"key":"15641_CR12","unstructured":"Ma P, Fan Y (2020) Small sample smart substation power equipment component detection based on deep transfer learning. Power System Technology 12"},{"key":"15641_CR13","doi-asserted-by":"publisher","first-page":"012157","DOI":"10.1088\/1742-6596\/1544\/1\/012157","volume":"1544","author":"L Shouguo","year":"2020","unstructured":"Shouguo L, Liu K, Qiao Y, Wang Q, Yang S, Li Z (2020) Automatic defect detection based on improved faster rcnn for substation equipment. J Phys Conf Ser 1544:012157 IOP Publishing","journal-title":"J Phys Conf Ser"},{"key":"15641_CR14","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761\u2013769","DOI":"10.1109\/CVPR.2016.89"},{"key":"15641_CR15","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"15641_CR16","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Proces Syst 30"},{"key":"15641_CR17","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"15641_CR18","doi-asserted-by":"crossref","unstructured":"Upchurch P, Gardner J, Pleiss G, Pless R, Snavely N, Bala K, Weinberger K (2017) Deep feature interpolation for image content changes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7064\u20137073","DOI":"10.1109\/CVPR.2017.645"},{"key":"15641_CR19","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al (2016) Matching networks for one shot learning. Adv Neural Inf Proces Syst 29"},{"key":"15641_CR20","unstructured":"Yi-jia W, Xiong H, Li-Rong W, Hong-Bo C (2021) Method of substation equipment defect detection based on attention mechanism learning. Comput Modern (02), 7"},{"key":"15641_CR21","doi-asserted-by":"crossref","unstructured":"Zhang K, Huang W, Li H, Li R (2020) A multi-scale attention networks for substation equipment image defect detection. In: The Purple Mountain Forum on Smart Grid Protection and Control, pp. 210\u2013220. Springer","DOI":"10.1007\/978-981-15-9746-6_17"},{"issue":"4","key":"15641_CR22","first-page":"627","volume":"14","author":"L Zhao","year":"2019","unstructured":"Zhao L, Liming Z, Meixiao G et al (2019) Object detection of high-voltage cable based on improved faster r-cnn. CAAI Trans Int Syst 14(4):627\u2013634","journal-title":"CAAI Trans Int Syst"},{"issue":"10","key":"15641_CR23","first-page":"205","volume":"40","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Li Y, Qi Y, Kong Y, Nie L (2020) Insulator defect detection method based on dynamic focus loss function and sample balance method. Elect Power Auto Equip 40(10):205\u2013211","journal-title":"Elect Power Auto Equip"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15641-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15641-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15641-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T10:19:17Z","timestamp":1701166757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15641-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":23,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["15641"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15641-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]},"assertion":[{"value":"19 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that they have no conflicts of interest in this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}