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面向知识自动化的磨矿系统操作员脑认知特征与控制效果的相关分析

化成城 王宏 卢绍文 王宏

化成城, 王宏, 卢绍文, 王宏. 面向知识自动化的磨矿系统操作员脑认知特征与控制效果的相关分析. 自动化学报, 2017, 43(11): 1898-1907. doi: 10.16383/j.aas.2017.c160328
引用本文: 化成城, 王宏, 卢绍文, 王宏. 面向知识自动化的磨矿系统操作员脑认知特征与控制效果的相关分析. 自动化学报, 2017, 43(11): 1898-1907. doi: 10.16383/j.aas.2017.c160328
HUA Cheng-Cheng, WANG Hong, LU Shao-Wen, WANG Hong. Knowledge Automation-oriented Brain Cognitive Feature and Control Effect Analysis of Operator in Mineral Grinding Process. ACTA AUTOMATICA SINICA, 2017, 43(11): 1898-1907. doi: 10.16383/j.aas.2017.c160328
Citation: HUA Cheng-Cheng, WANG Hong, LU Shao-Wen, WANG Hong. Knowledge Automation-oriented Brain Cognitive Feature and Control Effect Analysis of Operator in Mineral Grinding Process. ACTA AUTOMATICA SINICA, 2017, 43(11): 1898-1907. doi: 10.16383/j.aas.2017.c160328

面向知识自动化的磨矿系统操作员脑认知特征与控制效果的相关分析

doi: 10.16383/j.aas.2017.c160328
基金项目: 

国家自然科学基金 51505069

国家自然科学基金 61621004

流程工业综合自动化国家重点实验室开放基金 PAL-N201304

辽宁省高等学校创新团队项 LT2014006

详细信息
    作者简介:

    化成城 东北大学机械工程与自动化学院博士研究生.主要研究方向为基于脑电信号的专家系统与模式识别.E-mail:131006411@stu.neu.edu.cn

    卢绍文 东北大学流程工业综合自动化国家重点实验室副教授.2006年获得伦敦大学皇后玛丽学院电子工程学博士学位.主要研究方向为工业过程建模与仿真, 多尺度随机建模方法, 可视化方法.E-mail:lusw@mail.neu.edu.cn

    王宏 曼彻斯特大学教授, 同时在东北大学、华中科技大学和中国科学院自动化研究所从事研究工作.主要研究方向为随机分布控制, 故障检测和诊断, 非线性控制, 基于数据的复杂系统的建模.E-mail:Hong.wang@manchester.ac.uk

    通讯作者:

    王宏 东北大学机械工程与自动化学院教授.主要研究方向为生物机械电子工程, 人机交互与融合, 生物电信号分析与利用, 机器学习.本文通信作者.E-mail:hongwang@mail.neu.edu.cn

Knowledge Automation-oriented Brain Cognitive Feature and Control Effect Analysis of Operator in Mineral Grinding Process

Funds: 

National Natural Science Foundation of China 51505069

National Natural Science Foundation of China 61621004

the State Key Laboratory of Process Industry Automation of China PAL-N201304

the University Innovation Team of Liaoning Province LT2014006

More Information
    Author Bio:

    Ph. D. candidate at the School of Mechanical Engineering and Automation, Northeastern University. His research interest covers expert system based on EEG and pattern recognition

    Associate professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his Ph. D. degree in electronic engineering from the Queen Mary University of London, UK. His research interest covers industrial process modeling and simulation, multi-scale modeling, stochastic simulation, and visualization methods

    Professor at the University of Manchester, UK. He also holds a research position at Northeastern University, China, Huazhong University of Science and Technology, and the Institute of Automation, Chinese Academy of Sciences, China. His research interest covers stochastic distribution control, fault detection and diagnosis, nonlinear control, and data-based modeling for complex systems

    Corresponding author: WANG Hong Professor at the School of Mechanical Engineering and Automation, Northeastern University. Her research interest covers biomechatronics engineering, human-machine interaction and fusion, electrophysiology, and machine learning. Corresponding author of this paper
  • 摘要: 面向知识型工作自动化,研究了流程工业生产过程中操作人员的脑认知特征与操作控制水平之间的关键,建立了一种基于操作员脑网络特征的操作熟练程度隐性知识的显性化模型.采用关注信号瞬时相位、基于希尔伯特变换的相位锁方法,构建了脑功能网络(Functional brain network,FBN).基于磨矿系统操作员脑功能网络的图论参数与社区连接强度,建立了特征空间,采用支持向量机与神经网络进行特征分类.结果表明,在高频区,熟练操作员(熟手)的脑功能网络连接强度明显高于不熟练操作员(生手):在低频部分则生手的脑功能网络连接强度略高,其特征分类准确率为87.24%.磨矿系统操作过程中形成的溢流粒度(Grinding particle size,GPS)曲线可以初略地反映操作人员的熟练程度,本文在深入分析了其溢流粒度曲线与操作员脑网络特征的基础上,发现相对于溢流粒度曲线操作员的脑网络特征可以更全面地描述操作控制水平(特别在操作开始时间段),采用脑网络特征识别操作控制水平在时间上超前于溢流粒度曲线识别方法.本研究对于将知识工作者的认知特征引入到流程工业控制中,具有一定的借鉴意义.
    1)  本文责任编委 赵千川
  • 图  1  实验过程示意图

    Fig.  1  The diagram of experiment process

    图  3  样本散点图((a)原始特征散点图; (b)主成分分析后相互独立的成分散点图)

    Fig.  3  The scatters of samples ((a) The scatters of raw features; (b) The scatters of individual PCA components)

    图  2  脑电电极位置及脑区划分图

    Fig.  2  The location of electrodes and brain communities

    图  4  熟手与生手操作形成的溢流粒度曲线对比

    Fig.  4  The difference in GPS curves between old hands and new learners

    图  5  各频段熟手与生手脑网络对比

    Fig.  5  The comparison of brain network between old hands and new learners in different frequency band

    图  6  操作员的脑功能网络图论参数随着时间变化(α频段、β频段)

    Fig.  6  The changes of graphic theoretic parameters of functional brain networks of operators with time (α and β bands)

    图  7  实验开始50 s内各频段熟手与生手脑网络参数对比

    Fig.  7  The comparison of brain network parameters between old hands and new learners in different frequency band during the 50 s of experiment0s beginning

    图  8  实验开始50 s内熟手与生手各频段三个脑区内连接与脑区间连接强度对比

    Fig.  8  The comparison of the internal-and inter-connection strength of three brain regions between old hands and new learners during the 50 s of experiment0s beginning

    图  9  次优搜索过程中特征子集的平均分类准确率

    Fig.  9  The average accuracy of feature vectors in searching for suboptimal feature vector

    表  1  主成分分析得到的各个成分的相关参数与可分性判据

    Table  1  The properties and the sort separability criterion of the PCA components

    成分 贡献(方差) 相关性 t检验显著性 AUC SVM交叉验证平均准确率
    成分1 28.3801 −0.6670** 1.005E−50 0.8820 0.7865
    成分2 20.0231 0.1570** 0.0021 0.4036 0.6302
    成分3 10.7873 -0.0844 0.0987 0.5406 0.5182
    成分4 10.3419 0.0729 0.1540 0.4637 0.5234
    成分5 4.9439 -0.0538 0.2926 0.5214 0.4870
    成分6 3.3562 −0.2060** 4.881E−05 0.6108 0.5990
    成分7 2.5598 −0.1020* 0.0467 0.5497 0.5443
    成分8 2.3139 0.0863 0.0915 0.4414 0.5391
    下载: 导出CSV

    表  2  不同分类方式的性能评价

    Table  2  The performance assessment of different classifiers and feature vectors

    分类方式/评价 准确率(%) 精确度(%) 召回率(%) F测度(%) AUC(%)
    SVM分类器成分(1, 2, 6, 7) 84.90 ± 6.77 85.57 ± 8.41 84.90 ± 10.13 84.82 ± 7.08 92.74 ± 5.64
    SVM分类器成分(1, 2, 6, 7, 8, 4, 3, 5) 85.42 ± 4.28 86.19 ± 5.00 84.90 ± 4.97 85.16 ± 4.97 92.45 ± 3.85
    带高斯核函数的SVM分类器成分(1, 2, 6, 7) 86.72 ± 5.51 85.76 ± 7.84 89.06 ± 8.48 87.01 ± 5.40 93.42 ± 5.63
    带高斯核函数的SVM分类器成分(1, 2, 6, 7, 8, 4, 3, 5) 87.24 ± 6.31 83.01 ± 7.01 94.27 ± 7.28 88.11 ± 5.94 93.82 ± 5.24
    神经网络分类器成分(1, 2, 6, 7) 82.03 ± 6.81 81.38 ± 11.33 85.94 ± 8.05 82.88 ± 5.73 82.03 ± 6.81
    神经网络分类器成分(1, 2, 6, 7, 8, 4, 3, 5) 86.46 ± 7.34 87.10 ± 9.21 86.46 ± 8.77 86.47 ± 7.27 86.46 ± 7.34
    下载: 导出CSV
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  • 收稿日期:  2016-04-13
  • 录用日期:  2016-08-02
  • 刊出日期:  2017-11-20

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