YUAN Jie， WANG Shu， WANG Fu-li， SUN Xiao-hui. Abnormal Condition Recognition Based on Improved Subjective Bayesian Method for Fused Magnesium Furnace[J]. Journal of Northeastern University(Natural Science), 2021, 42(2): 153-159.
[1]Wu Z W， Liu T F， Jiang Z P，et al.Nonlinear control tools for fused magnesium furnaces: design and implementation［J］.IEEE Transactions on Industrial Electronics，2018， 65 (9):7248-7257. [2]李荟，王福利，李鸿儒.电熔镁炉熔炼过程异常工况识别及自愈控制方法［J］.自动化学报，2020，46(7): 1411-1419.(Li Hui， Wang Fu-li， Li Hong-ru.Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process［J］.Acta Automatica Sinica，2020， 46(7):1411-1419.) [3]Ding S Y， Wang Z J， Kong W J，et al.Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism［J］.Neurocomputing，2019， 359:32-40. [4]卢绍文，王克栋，吴志伟， 等.基于深度卷积网络的电熔镁炉欠烧工况在线识别［J］.控制与决策， 2019， 34(7):1537-1544.(Lu Shao-wen， Wang Ke-dong， Wu Zhi-wei，et al.Online detection of semi-molten of fused magnesium furnace based on deep convolutional neural network［J］.Control and Decision，2019， 34(7):1537-1544.) [5]吴高昌，刘强，柴天佑，等.基于时序图像深度学习的电熔镁炉异常工况诊断［J］.自动化学报， 2019， 45(8):1475-1485.(Wu Gao-chang， Liu Qiang， Chai Tian-you，et al.Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces［J］.Acta Automatica Sinica，2019， 45(8):1475-1485.) [6]李鸿儒， 王奕文，邓靖川.基于信息融合的电熔镁炉熔炼异常工况等级识别［J］.东北大学学报(自然科学版)， 2020， 41(2):153-157.(Li Hong-ru， Wang Yi-wen， Deng Jing-chuan.Information fusion based abnormal condition levels recognition of smelting in fused magnesium furnace［J］.Journal of Northeastern University(Natural Science)，2020， 41(2):153-157.) [7]Coulibaly L， Kamsu-Foguem B， Tangara F.Rule-based machine learning for knowledge discovering in weather data［J］.Future Generation Computer Systems，2020， 108: 861-878. [8]Vera J C D， Ortiz G M N， Molina C，et al.Extending knowledge based redundancy in association rules with imprecise knowledge［J］.IEEE Latin America Transactions，2019， 17(4):648-653. [9]Xie T， Gong Z T.A hesitant soft fuzzy rough set and its applications［J］.IEEE Access， 2019， 7: 167766-167783. [10]Zhao G Z， Chen A G， Lu G X，et al.Data fusion algorithm based on fuzzy sets and D-S theory of evidence［J］.Tsinghua Science and Technology，2020， 25(1):12-19. [11]Li L， Yue W C.Dynamic uncertain causality graph based on intuitionistic fuzzy sets and its application to root cause analysis［J］.Applied Intelligence，2020， 50(1):241-255. [12]Gnen M， Johnson W O， Lu Y G，et al.Comparing objective and subjective Bayes factors for the two-sample comparison: the classification theorem in action［J］.The American Statistician，2019， 73(1):22-31. [13]Uzunolu B.An adaptive Bayesian approach with subjective logic reliability networks for preventive maintenance［J］.IEEE Transactions on Reliability，2020， 69(3):916-924.中文.(英文)
