{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:08:48Z","timestamp":1784563728226,"version":"3.55.0"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115536","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:26:58Z","timestamp":1782890818000},"page":"115536","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P4","title":["A novel monitoring method for molten iron flow state at the blast furnace taphole by fusing image and operating state data"],"prefix":"10.1016","volume":"181","author":[{"given":"Zhaohui","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuekun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1876-3108","authenticated-orcid":false,"given":"Dong","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Gui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115536_b1","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"issue":"3","key":"10.1016\/j.engappai.2026.115536_b2","first-page":"3695","article-title":"Reslt: Residual learning for long-tailed recognition","volume":"45","author":"Cui","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.115536_b3","series-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.engappai.2026.115536_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111724","article-title":"Dynamic anomaly detection in blast furnace operations using a transformer-enhanced isolation forest framework","volume":"159","author":"Duan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115536_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109558","article-title":"A novel anomaly detection and classification algorithm for application in tuyere images of blast furnace","volume":"139","author":"Duan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115536_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107865","article-title":"A new filter feature selection algorithm for classification task by ensembling pearson correlation coefficient and mutual information","volume":"131","author":"Gong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.engappai.2026.115536_b7","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.engappai.2026.115536_b8","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"},{"key":"10.1016\/j.engappai.2026.115536_b9","first-page":"1","article-title":"Detection method of molten iron flow velocity at blast furnace taphole combining visual perception and jet mechanism","volume":"73","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115536_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105849","article-title":"A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification","volume":"120","author":"Jiang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115536_b11","doi-asserted-by":"crossref","unstructured":"Kim, K., Seo, B., Rhee, S.-H., Lee, S., Woo, S.S., 2019. Deep learning for blast furnaces: Skip-dense layers deep learning model to predict the remaining time to close tap-holes for blast furnaces. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. pp. 2733\u20132741.","DOI":"10.1145\/3357384.3357803"},{"issue":"1","key":"10.1016\/j.engappai.2026.115536_b12","doi-asserted-by":"crossref","DOI":"10.1002\/srin.201700071","article-title":"Review on modeling and simulation of blast furnace","volume":"89","author":"Kuang","year":"2018","journal-title":"Steel Res. Int."},{"key":"10.1016\/j.engappai.2026.115536_b13","doi-asserted-by":"crossref","unstructured":"Li, T., Wang, L., Wu, G., 2021. Self supervision to distillation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 630\u2013639.","DOI":"10.1109\/ICCV48922.2021.00067"},{"key":"10.1016\/j.engappai.2026.115536_b14","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P., 2017. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"issue":"10","key":"10.1016\/j.engappai.2026.115536_b15","doi-asserted-by":"crossref","DOI":"10.1002\/srin.202100142","article-title":"Experimental study of blast furnace hearth drainage based on image analysis","volume":"92","author":"Liu","year":"2021","journal-title":"Steel Res. Int."},{"issue":"5","key":"10.1016\/j.engappai.2026.115536_b16","doi-asserted-by":"crossref","first-page":"465","DOI":"10.17159\/2411-9717\/2016\/v116n5a12","article-title":"The tap-hole-key to furnace performance","volume":"116","author":"Nelson","year":"2016","journal-title":"J. South. Afr. Inst. Min. Met."},{"issue":"10","key":"10.1016\/j.engappai.2026.115536_b17","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.2355\/isijinternational.45.1496","article-title":"Effect of various in-furnace conditions on blast furnace hearth drainage","volume":"45","author":"Nishoka","year":"2005","journal-title":"ISIJ Int."},{"issue":"10","key":"10.1016\/j.engappai.2026.115536_b18","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1109\/TIM.2018.2880061","article-title":"Temperature measurement and compensation method of blast furnace molten iron based on infrared computer vision","volume":"68","author":"Pan","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"10.1016\/j.engappai.2026.115536_b19","doi-asserted-by":"crossref","first-page":"7056","DOI":"10.1109\/TII.2020.2972332","article-title":"Compensation method for molten iron temperature measurement based on heterogeneous features of infrared thermal images","volume":"16","author":"Pan","year":"2020","journal-title":"IEEE Trans. Ind. Informatics"},{"issue":"8","key":"10.1016\/j.engappai.2026.115536_b20","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3390\/pr7080519","article-title":"Principal component analysis of blast furnace drainage patterns","volume":"7","author":"Roche","year":"2019","journal-title":"Processes"},{"issue":"4","key":"10.1016\/j.engappai.2026.115536_b21","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s11663-020-01857-1","article-title":"Drainage model of multi-taphole blast furnaces","volume":"51","author":"Roche","year":"2020","journal-title":"Met. Mater. Trans. B"},{"issue":"2","key":"10.1016\/j.engappai.2026.115536_b22","doi-asserted-by":"crossref","first-page":"228","DOI":"10.2355\/isijinternational.51.228","article-title":"A simulation study of blast furnace hearth drainage using a two-phase flow model of the taphole","volume":"51","author":"Shao","year":"2011","journal-title":"ISIJ Int."},{"issue":"5","key":"10.1016\/j.engappai.2026.115536_b23","doi-asserted-by":"crossref","first-page":"438","DOI":"10.2355\/isijinternational.40.438","article-title":"Model of the state of the blast furnace hearth","volume":"40","author":"Torrkulla","year":"2000","journal-title":"ISIJ Int."},{"key":"10.1016\/j.engappai.2026.115536_b24","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"12","key":"10.1016\/j.engappai.2026.115536_b25","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.115536_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109583","article-title":"Cross-attention interaction learning network for multi-model image fusion via transformer","volume":"139","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115536_b27","series-title":"European Conference on Computer Vision","first-page":"247","article-title":"Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification","author":"Xiang","year":"2020"},{"key":"10.1016\/j.engappai.2026.115536_b28","doi-asserted-by":"crossref","unstructured":"Yan, J., Liu, Y., Sun, J., Jia, F., Li, S., Wang, T., Zhang, X., 2023. Cross modal transformer: Towards fast and robust 3d object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 18268\u201318278.","DOI":"10.1109\/ICCV51070.2023.01675"},{"issue":"4","key":"10.1016\/j.engappai.2026.115536_b29","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s12613-013-0733-4","article-title":"Study on the early warning mechanism for the security of blast furnace hearths","volume":"20","author":"Zhao","year":"2013","journal-title":"Int. J. Miner. Met. Mater."},{"issue":"1","key":"10.1016\/j.engappai.2026.115536_b30","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/TIE.2020.2967708","article-title":"Data-driven monitoring and diagnosing of abnormal furnace conditions in blast furnace ironmaking: An integrated PCA-ICA method","volume":"68","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018208?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018208?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:42:30Z","timestamp":1784562150000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626018208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":30,"alternative-id":["S0952197626018208"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115536","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel monitoring method for molten iron flow state at the blast furnace taphole by fusing image and operating state data","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115536","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":"115536"}}