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Frontiers of Chemical Science and Engineering 2017年第3期11卷 414-428页

作者： weichao yue Xiaofang Chen Weihua Gui Yongfang Xie Hongliang Zhang School of Information Science and Engineering Central South University Changsha 410083 China School of Metallurgy and Environment Central South University Changsha 410083 China

Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

关键词： abnormal aluminum electrolysis cell condi- tion Fuzzy-Bayesian network multi-source knowledge solidification and reasoning root cause analysis

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

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Engineering 2019年第6期5卷 1060-1076页

作者： weichao yue Weihua Gui Xiaofang Chen Zhaohui Zeng Yongfang Xie School of Information Science and Engineering Central South UniversityChangsha 410083China

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

关键词： AlF3 addition Fuzzy cognitive maps Learning algorithms State transition algorithm Fuzzy decision trees

A Regional Local Search and Memory based Evolutionary Algorithm for Dynamic Multi-objective Optimization

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A Regional Local Search and Memory based Evolutionary Algori...

Early Classification Based on Shapelet Learning for Aluminum Electrolysis Process

第三十九届中国控制会议

作者： Sanyi Li Yanfeng Wang weichao yue School of Electrical and Information Engineering Zhengzhou University of Light Industry

This paper presents a novel dynamic multi-objective optimization algorithm based on region local search and memory（DMOA-RLSM）. Firstly, the NSGA2-DM stores useful information（memory） to guide population initialization in the future;secondly, in the stage of population regeneration, DMOA-RLSM get corner points and sparse point according to the results of non-dominated sorting of current populations, define these points as the centers of border areas and sparse area respectively;thirdly, search around the corner points and sparse point ***-RLSM adopts extreme optimization strategy and random search strategy simultaneously to improve the quality of solutions and convergence rate. Performance of DMOA-RLSM is compared with two reported dynamic multi-objective optimization algorithms（DMOAs） for dMOP functions and FDA functions. Results show that the DMOA-RLSM performs better than the compared algorithms because of its low computational complexity.

关键词： dynamic multi-objective optimization local search memory

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Early Classification Based on Shapelet Learning for Aluminum...

生物合成纳米金与MWCNTs/L-Cys复合修饰玻碳电极测定左旋多巴

第43届中国控制会议

作者： Xiaoxue Wan Lihui Cen Xiaofang Chen Yongfang Xie weichao yue School of Automation Central South University School of Electrical and Information Engineering Zhengzhou University of Light Industry

Anode effect in aluminum electrolysis cells occurs quickly and is very *** existing cell condition identification methods are difficult to identify anode effect in a timely,accurate,and interpretable *** combining shapelet learning and early classification methods,we propose a new interpretable cell condition identification method for real-time voltage signals.A novel loss function that comprehensively consider accuracy and earliness is proposed in this *** the same time,early shapelets can be optimized in this loss *** experiment in the aluminum electrolysis process illustrate the superiority performance of the proposed methodology.

关键词： Shapelet Learning Early Classification Aluminum Electrolysis Process Anode Effect

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高等学校化学学报 2014年第2期35卷 250-256页

作者：岳伟超蔡卓蒋翠文叶丹妮广西大学化学化工学院南宁530004

利用荷叶萃取液生物合成纳米金,并与多壁碳纳米管/L-半胱氨酸复合成修饰电极材料,研究了左旋多巴在该修饰电极上的电化学行为.在0.2 mol/L乙酸-乙酸钠体系(pH=2.6)中,氧化峰电流与左旋多巴浓度在0.6~40μmol/L及60~120μmol/L范围内呈... 详细信息

利用荷叶萃取液生物合成纳米金,并与多壁碳纳米管/L-半胱氨酸复合成修饰电极材料,研究了左旋多巴在该修饰电极上的电化学行为.在0.2 mol/L乙酸-乙酸钠体系(pH=2.6)中,氧化峰电流与左旋多巴浓度在0.6~40μmol/L及60~120μmol/L范围内呈良好的线性关系,检出限达5.2×10-8mol/L.实验结果表明,生物合成纳米金复合多壁碳纳米管/L-半胱氨酸修饰电极具有良好的稳定性和高灵敏度,对实际样品测定的回收率在91.2%~102.5%之间.

关键词：生物合成荷叶多壁碳纳米管左旋多巴

曲克芦丁对Belousov-Zhabotinskii振荡体系的扰动及其含量测定

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高等学校化学学报 2016年第1期37卷 31-36页

作者：叶丹妮蔡卓蒋翠文岳伟超广西大学化学化工学院南宁530004

基于曲克芦丁对Belousov-Zhabotinskii振荡体系的扰动,建立了一种测定曲克芦丁的新方法.在优化条件下,振幅改变量ΔE与曲克芦丁的浓度在0.808~40.4μmol/L范围内呈现良好的线性关系,检出限为2.14×10^(-7)mol/L(S/N=3).对曲克芦丁注射... 详细信息

基于曲克芦丁对Belousov-Zhabotinskii振荡体系的扰动,建立了一种测定曲克芦丁的新方法.在优化条件下,振幅改变量ΔE与曲克芦丁的浓度在0.808~40.4μmol/L范围内呈现良好的线性关系,检出限为2.14×10^(-7)mol/L(S/N=3).对曲克芦丁注射液中的曲克芦丁进行了分析测定,回收率在98.8%~101%之间,测定结果与紫外法无显著性差异.采用循环伏安法和紫外分光光度法对可能的扰动机理进行了探讨.

关键词： Belousov-Zhabotinskii振荡曲克芦丁化学振荡

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玻璃基板码垛机械手位置的迭代学习跟踪控制

电子测量与仪器学报 2013年第9期27卷 882-888页

作者：岳伟超余发山尚殿琦河南理工大学电气工程与自动化学院焦作454003

码垛机械手在玻璃基板生产过程中发挥着至关重要的作用。针对玻璃基板码垛机械手码垛过程具有较强重复性的特点,提出迭代学习的码垛机械手位置跟踪控制方法。考虑到运行过程中出现的非重复性干扰设计了鲁棒迭代学习控制器。同时采用Lyap... 详细信息

码垛机械手在玻璃基板生产过程中发挥着至关重要的作用。针对玻璃基板码垛机械手码垛过程具有较强重复性的特点,提出迭代学习的码垛机械手位置跟踪控制方法。考虑到运行过程中出现的非重复性干扰设计了鲁棒迭代学习控制器。同时采用Lyapunov函数分析了码垛机械手的收敛性。理论结果表明,鲁棒迭代学习控制算法可以保证码垛机械手的位置跟踪误差收敛到0。仿真结果表明,所提出的鲁棒迭代学习控制算法,较好地抑制了非重复扰动的影响,有效提高码垛机械手的位置跟踪性能,经过20次的迭代,控制精度能达到0.01 mm。在实验测试中,相邻两个玻璃基板间的码垛误差最大不超过0.1 mm,取得了良好的控制效果。

关键词：玻璃基板码垛机械手迭代学习控制鲁棒算法位置跟踪

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桥式起重机位置跟踪及防摆的迭代学习控制

电子测量与仪器学报 2014年第2期28卷 184-191页

作者：栗三一余发山卜旭辉岳伟超河南理工大学电气工程与自动化学院焦作454000

桥式起重机运行时,小车移动引起的货物摆动影响了运输效率,容易引发事故,桥式起重机的定位及防摆问题引起了众多专家的关注。针对桥式起重机重复运动的情况,采用迭代学习控制策略来实现位置跟踪及角度控制。根据拉格朗日方程建立了桥式... 详细信息

桥式起重机运行时,小车移动引起的货物摆动影响了运输效率,容易引发事故,桥式起重机的定位及防摆问题引起了众多专家的关注。针对桥式起重机重复运动的情况,采用迭代学习控制策略来实现位置跟踪及角度控制。根据拉格朗日方程建立了桥式起重机的三维数学模型。设计了桥式起重机角度控制的迭代学习控制算法,并证明了算法的收敛性。研究表明,迭代学习控制可有效利用桥式起重机运行的重复信息,经过一定次数的迭代学习后,位置跟踪误差为0,摆角抑制在7°以内。仿真示例验证了方法的有效性。

关键词：桥式起重机迭代学习控制位置跟踪防摆控制

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基于迭代学习的矿井提升系统跟踪控制

电子测量与仪器学报 2014年第2期28卷 138-145页

作者：余发山岳伟超张瑞庄卜旭辉河南理工大学电气工程与自动化学院焦作454000 河南理工大学研究生处焦作454000

矿井提升机在煤矿开采和生产过程中发挥着至关重要的作用。针对矿料提升过程具有较强重复性的特点,提出迭代学习的提升机速度和位置跟踪控制方法。设计了提升机的D型迭代学习控制器,同时考虑运行过程中出现的非重复性干扰设计了带有滤... 详细信息

矿井提升机在煤矿开采和生产过程中发挥着至关重要的作用。针对矿料提升过程具有较强重复性的特点,提出迭代学习的提升机速度和位置跟踪控制方法。设计了提升机的D型迭代学习控制器,同时考虑运行过程中出现的非重复性干扰设计了带有滤波器型迭代学习控制器。并采用λ范数证明了系统的收敛性,理论结果表明矿井提升机位置与速度跟踪误差可以收敛到0。同时仿真结果表明,经过30次运行后,跟踪误差几乎收敛到0,迭代学习控制算法可利用矿井作业的重复运行特性可以有效提高提升机的跟踪性能,带有滤波器的迭代学习控制算法可较好地抑制了非重复扰动的影响。

关键词：矿井提升系统永磁同步直线电机滤波器迭代学习控制

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井下胶带接头图像采集系统设计

工矿自动化 2014年第11期40卷 10-13页

作者：黄鹤松高翔岳维超廖玉波李平山东科技大学电气与自动化工程学院山东青岛266590 山东省机器人与智能技术重点实验室山东青岛266590 淄博矿业集团有限责任公司许厂煤矿山东济宁272173

针对现有胶带接头隐患检测方法存在漏检、误报等问题,设计了一种井下运输胶带接头的图像采集系统。该系统采用电感式接近开关实时检测金属胶带接头,并控制数字摄像机自动抓拍运输胶带接头处的图像,利用井下环网将图像传输到地面监控室,... 详细信息

针对现有胶带接头隐患检测方法存在漏检、误报等问题,设计了一种井下运输胶带接头的图像采集系统。该系统采用电感式接近开关实时检测金属胶带接头,并控制数字摄像机自动抓拍运输胶带接头处的图像,利用井下环网将图像传输到地面监控室,实现胶带接头图像的实时采集与处理,及时排除胶带接头可能出现的撕裂、脱扣等隐患。

关键词：胶带接头图像采集图像传输速度传感器接近开关