报告题目1：System identification in Presence of Outliers

报告人：王庆国教授，南非国家科学院院士

报告时间：7月6日 14:30

报告地点：机电工程学院报告厅B110

主办单位：中国矿业大学机电工程学院

            矿山智能采掘装备省部共建协同创新中心

            矿山智能采掘装备学科创新引智基地

报告摘要：

    The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low rank and sparse matrices, and further recast as a semidefinite programming problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low-rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers, and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

报告题目2：Multi-period Optimization：Brief Introduction and New Results

报告人：王庆国教授，南非国家科学院院士

报告时间：7月7日9:00

报告地点：南湖机电学院楼B110

主办单位：中国矿业大学机电工程学院

                    矿山智能采掘装备省部共建协同创新中心

                    矿山智能采掘装备学科创新引智基地

报告摘要：

      Multi-period optimization problems are extensively existed in many engineering applications. This talk is concerned with a multi-period optimization problem over a finite horizon. Such a problem is technically challenging since nonsmooth property of variance causes difficulties in applying dynamic programming. A convex quadratic programming problem in terms of the decision variables, regardless of system’s dynamic nature (either linear or nonlinear cases) is established, and by solving the stationary equation directly, an optimal solution is developed for its original problem without using embedding method. The solution is simplified for a general linear model with high-order and coupled dynamics and shown to be implementable with historical data. Some engineering application cases are also exhibited in this academic report.

报告题目3：When Science Fails

报 告 人：王庆国教授，南非国家科学院院士

报告时间：7月7日 14:30

报告地点：南湖机电学院楼B110

主办单位：中国矿业大学机电工程学院

            矿山智能采掘装备省部共建协同创新中心

            矿山智能采掘装备学科创新引智基地

报告摘要：

This lecture introduces the history and characteristics of science and demonstrates its possibility of leading to natural or social disasters. The fact when and why science fails is firstly revealed. The limitations of science are then expounded from the aspects of system’s complexity and uncertainty, the applicability of deductive logic, the representativeness of facts, the evolution of living things over time and the difference of cognitive space. It is also pointed out that unscientific methods (qualitative analysis, induction, etc.) are widely used in non-science and engineering fields, such as economy, finance, management and other complex systems, and have become effective research methods.

报告人简介：

王庆国，南非约翰内斯堡大学智能系统研究院杰出教授. 南非国家A级科学家, 南非国家科学院院士。自2020年起，担任北师大-浸大联合国际学院讲座教授，北京师范大学人工智能与未来网络研究院教授。学术领域为自动化/人工智能,主要从事复杂系统的建模、估计、预测、优化, 控制等方面的研究. 应用领域包括工业与环境过程、能源系统、航空与国防工程、医疗工程, 金融市场,农业和渔业; 他的工作涵盖了工业4.0的核心。在国际杂志发表论文350余篇，由Springer出版7部学术专著, 累计论著引用近19000次，H-index 为75。荣获国际自控界权威学报《Automatica》2006-2010年最多引用论文奖, 在2013年名列Thomson Reuters list of highly cited researchers榜, 2014年荣获《控制理论与应用》创刊30周年最具影响力论文奖，在2020年名列斯坦福大学发布的全球前2%顶尖科学家“终身科学影响力”和“年度科学影响力”榜单。他也从事大量高技术研发及实际的工程应用，如造纸机、注塑机、间隙过程、飞机、无人机、风能、电厂、机器人、超净室、空调系统及医疗过程等的建模和控制，与许多国际控制大公司合作过，累计科研经费超亿人民币。荣获2017年度常州科教城“国家级人才奖”. 获美国等地专利6项(转让2项) 。曾任美国电气与电子工程师协会新加坡控制分会主席(4次), 亚洲控制会议及若干IEEE国际会议总主席, 国际自动控制联合会学报《过程控制》编委。现任《美国仪表学会会刊》执行副主编(Deputy Editor-in-Chief)，及多份国际学报编委。已培养博士及博士后各30余名。