{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T07:59:57Z","timestamp":1778572797991,"version":"3.51.4"},"reference-count":21,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Project of Hunan Natural Science Foundation of China","award":["2022JJ30748"],"award-info":[{"award-number":["2022JJ30748"]}]},{"name":"General Project of Hunan Natural Science Foundation of China","award":["61903325"],"award-info":[{"award-number":["61903325"]}]},{"name":"General Project of Hunan Natural Science Foundation of China","award":["61621062"],"award-info":[{"award-number":["61621062"]}]},{"name":"National Natural Science Foundation for Young Scholars of China","award":["2022JJ30748"],"award-info":[{"award-number":["2022JJ30748"]}]},{"name":"National Natural Science Foundation for Young Scholars of China","award":["61903325"],"award-info":[{"award-number":["61903325"]}]},{"name":"National Natural Science Foundation for Young Scholars of China","award":["61621062"],"award-info":[{"award-number":["61621062"]}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JJ30748"],"award-info":[{"award-number":["2022JJ30748"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903325"],"award-info":[{"award-number":["61903325"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61621062"],"award-info":[{"award-number":["61621062"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial\u2013temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.<\/jats:p>","DOI":"10.3390\/s22166245","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"6245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics"],"prefix":"10.3390","volume":"22","author":[{"given":"Pan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}]},{"given":"Zhipeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}]},{"given":"Weihua","family":"Gui","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}]},{"given":"Chunhua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mikhailova, U., Kalugina, O., Afanasyeva, M., and Konovalov, M. 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