{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:58:24Z","timestamp":1780365504764,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T00:00:00Z","timestamp":1565222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4182038"],"award-info":[{"award-number":["4182038"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671054"],"award-info":[{"award-number":["61671054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the China Central Universities of USTB","award":["FRF-BR-17-004A, FRF-GF-17-B49"],"award-info":[{"award-number":["FRF-BR-17-004A, FRF-GF-17-B49"]}]},{"name":"Open Project Program of the National Laboratory of Pattern Recognition","award":["201800027"],"award-info":[{"award-number":["201800027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The stockline, which describes the measured depth of the blast furnace (BF) burden surface with time, is significant to the operator executing an optimized charging operation. For the harsh BF environment, noise interferences and aberrant measurements are the main challenges of stockline detection. In this paper, a novel encoder\u2013decoder architecture that consists of a convolution neural network (CNN) and a long short-term memory (LSTM) network is proposed, which suppresses the noise interferences, classifies the distorted signals, and regresses the stockline in a learning way. By leveraging the LSTM, we are able to model the longer historical measurements for robust stockline tracking. Compared to traditional hand-crafted denoising processing, the time and efforts could be greatly saved. Experiments are conducted on an actual eight-radar array system in a blast furnace, and the effectiveness of the proposed method is demonstrated on the real recorded data.<\/jats:p>","DOI":"10.3390\/s19163470","type":"journal-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T11:05:32Z","timestamp":1565262332000},"page":"3470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Improving Stockline Detection of Radar Sensor Array Systems in Blast Furnaces Using a Novel Encoder\u2013Decoder Architecture"],"prefix":"10.3390","volume":"19","author":[{"given":"Xiaopeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Instrument Science &amp; Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4113-6994","authenticated-orcid":false,"given":"Xianzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2288-7901","authenticated-orcid":false,"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation &amp; Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4356","DOI":"10.1016\/j.energy.2009.04.008","article-title":"Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China","volume":"35","author":"Guo","year":"2010","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7141","DOI":"10.1109\/TIE.2017.2686369","article-title":"Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation","volume":"64","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.neucom.2013.09.067","article-title":"Multi-model control of blast furnace burden surface based on fuzzy SVM","volume":"148","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, D., Li, Z., Chen, X., Wang, Z., and Wu, J. (2016). A dielectric-filled waveguide antenna element for 3D imaging radar in high temperature and excessive dust conditions. Sensors, 16.","DOI":"10.3390\/s16081339"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5893","DOI":"10.1109\/JSEN.2015.2445494","article-title":"BLASTDAR\u2014A large radar sensor array system for blast furnace burden surface imaging","volume":"15","author":"Zankl","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.conengprac.2015.11.006","article-title":"Process monitoring of iron-making process in a blast furnace with PCA-based methods","volume":"47","author":"Zhou","year":"2016","journal-title":"Control. Eng. Pract."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1109\/TIE.2011.2159693","article-title":"Modeling of the thermal state change of blast furnace hearth with support vector machines","volume":"59","author":"Gao","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1109\/TIE.2012.2206336","article-title":"Binary coding SVMs for the multiclass problem of blast furnace system","volume":"60","author":"Jian","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.asoc.2005.09.001","article-title":"A genetic algorithms based multi-objective neural net applied to noisy blast furnace data","volume":"7","author":"Pettersson","year":"2007","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, S., Yin, Y., Xiao, W., and Zhang, J. (2017). A novel online sequential extreme learning machine for gas utilization ratio prediction in blast furnaces. Sensors, 17.","DOI":"10.3390\/s17081847"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1179\/174328108X369107","article-title":"Effect of burden material size on blast furnace stockline profile of bell-less blast furnace","volume":"36","author":"Liang","year":"2009","journal-title":"Ironmak. Steelmak."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qingwen, H., Xianzhong, C., and Ping, C. (2015, January 28\u201330). Radar data processing of blast furnace stock-line based on spatio-temporal data association. Proceedings of the 2015 34th Chinese Control Conference (CCC), Hangzhou, China.","DOI":"10.1109\/ChiCC.2015.7260351"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.2355\/isijinternational.52.2048","article-title":"3-Dimension imaging system of burden surface with 6-radars array in a blast furnace","volume":"52","author":"Chen","year":"2012","journal-title":"ISIJ Int."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, A., Huang, J., Qiu, J., Ke, Y., Zheng, L., and Chen, X. (2016, January 6\u201310). Signal processing for a FMCW material level measurement system. Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China.","DOI":"10.1109\/ICSP.2016.7877833"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"533","DOI":"10.2355\/isijinternational.47.533","article-title":"Microwave technology in steel and metal industry, an overview","volume":"47","author":"Malmberg","year":"2007","journal-title":"ISIJ Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1109\/TASE.2016.2538560","article-title":"Multistep forecasting models of the liquid level in a blast furnace hearth","volume":"14","author":"Gomes","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1016\/j.ces.2004.05.007","article-title":"Novel model for estimation of liquid levels in the blast furnace hearth","volume":"59","author":"Saxen","year":"2004","journal-title":"Chem. Eng. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1179\/1743281214Y.0000000258","article-title":"Blast furnace stockline measurement using radar","volume":"42","author":"Wei","year":"2015","journal-title":"Ironmak. Steelmak."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 6\u201311). Show, attend and tell: Neural image caption generation with visual attention. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015, January 19\u201324). Convolutional, long short-term memory, fully connected deep neural networks. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E., Nicolaou, M.A., Schuller, B., and Zafeiriou, S. (2016, January 20\u201325). Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472669"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9549","DOI":"10.1109\/TIE.2017.2711530","article-title":"Building occupancy estimation with environmental sensors via CDBLSTM","volume":"64","author":"Chen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1049\/ip-f-2.1992.0048","article-title":"Linear FMCW radar techniques","volume":"Volume 139","author":"Stove","year":"1992","journal-title":"IEE Proceedings F (Radar and Signal Processing)"},{"key":"ref_25","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kendall, A., and Cipolla, R. (2017, January 21\u201326). Geometric loss functions for camera pose regression with deep learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.694"},{"key":"ref_30","unstructured":"Kendall, A., and Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision?. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","unstructured":"Diederik, P., and Kingma, J.B. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_32","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916), Savannah, GA, USA."},{"key":"ref_34","first-page":"59","article-title":"An introduction to the Kalman filter","volume":"8","author":"Bishop","year":"2001","journal-title":"Proc. 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