检索结果-内蒙古大学图书馆

第41届中国控制会议

作者： Yunong Zhang Zhiwen Yuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Research Institute of Sun Yat-sen University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents three relatively effective numerical algorithms,i.e.,94 LVI algorithms of OIpU(one-iteration-perupdate) type,to handle time-dependent QP(quadratic programming) problems subject to two constraints respectively(i.e.,equality and inequality constraints,equality and bound constraints,or inequality and bound constraints).Specifically,two "bridges" play essential parts of the methods(i.e.,the first "bridge" makes a QP problem convert to an LVI [linear variational inequality],and the second "bridge" makes the LVI convert to a PLPE [piecewise-linear projection equation]).On the basis of the standard94 LVI algorithm,the methods only update once through the solution of the previous sampling time ***,we conduct a number of experiments to substantiate the three algorithms respectively,which use exponential functions,log functions,and trigonometric functions in corresponding coefficients of the experimental examples.

关键词： Quadratic programming Time-dependent Numerical algorithms Linear variational inequalities(LVI)

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal evolution of Beijing 2003 SARS epidemic

引用

Science China Earth Sciences 2010年第7期53卷 1017-1028页

作者： CAO ZhiDong ZENG DaJun ZHENG XiaoLong WANG QuanYi WANG FeiYue WANG JinFeng WANG XiaoLi Key Laboratory of Complex Systems and Intelligence Science Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Center for Disease Control and Prevention Beijing 100013China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

关键词： severe acute respiratory syndrome (SARS) Beijing morbidity rate spatial analysis spatio-temporal evolution control measures

来源：评论

学校读者我要写书评

暂无评论

Data-driven intelligent monitoring system for key variables in wastewater treatment process

引用

Chinese Journal of Chemical Engineering 2018年第10期26卷 2093-2101页

作者： Honggui Han Shuguang Zhu Junfei Qiao Min Guo College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

关键词： Data-driven Soft sensor Intelligent monitoring system Data distribution service Wastewater treatment process

来源：评论

学校读者我要写书评

暂无评论

A data-derived soft-sensor method for monitoring effluent total phosphorus

引用

Chinese Journal of Chemical Engineering 2017年第12期25卷 1791-1797页

作者： Shuguang Zhu Honggui Han Min Guo Junfei Qiao College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

关键词： Data-derived soft-sensor Effluent total phosphorus Wastewater treatment process Radial basis function neural network Partial least square method

来源：评论

学校读者我要写书评

暂无评论

Structure Design for RBF Neural Network Based on Improved K-means Algorithm

Structure Design for RBF Neural Network Based on Improved K-...

引用

第28届中国控制与决策会议

作者： Sun Yuqing Qiao Junfei Han Honggui College of Electronic Information & Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Aiming at the disadvantages of the traditional K-means clustering algorithm,a new algorithm based on density is proposed to remove the noises and outliers in this *** algorithm determines whether a point is a noise or not according to the density of the *** show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the *** the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm,meanwhile the number of cluster centers is *** improved K-means algorithm is taken to optimize the structure of RBF neural network,and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability.

关键词： K-means algorithm Density Subtractive clustering algorithm RBF neural network

来源：评论

学校读者我要写书评

暂无评论

An Improved NSGA2 Algorithm with the Adaptive Differential Mutation Operator

An Improved NSGA2 Algorithm with the Adaptive Differential M...

引用

第三十四届中国控制会议

作者： WEI Jing QIAO Junfei Meng Qinchao College of Electronic Information and Control Engineering Beijing University of TechnologyBeijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467374439

This paper proposes an improved non-dominated sorting genetic algorithm(NSGA2)-DNSGA2, with the aim of preserving diversity of obtained optimal solution and avoiding the original NSGA2 algorithm falling into local optimal. The proposed DNSGA2 algorithm which introduces a differential mutation operator to replace the original polynomial mutation because the method of differential local search is helpful to the uniformity of Pareto optimal solution set. The performance of the proposed DNSGA2, NSGA2 and W-LRCD-NSGA2(Based on left-right crowding distance non-dominated sorting genetic algorithm) are compared via four benchmark functions. Simulation results indicate that the diversity and uniformity of Pareto optimal solution obtained by DNSGA2 are better than the other two algorithms.

关键词： Multi-objective,NSGA2,Differential mutation,Pareto optimal solution

来源：评论

学校读者我要写书评

暂无评论

Structure design for RBF neural network based on improved K-means algorithm 28

Structure design for RBF neural network based on improved K-...

引用

28th Chinese control and Decision Conference, CCDC 2016

作者： Yuqing, Sun Junfei, Qiao Honggui, Han College of Electronic Information and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781467397148

Aiming at the disadvantages of the traditional K-means clustering algorithm, a new algorithm based on density is proposed to remove the noises and outliers in this paper. This algorithm determines whether a point is a noise or not according to the density of the point. Experiments show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the samples. Then the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm, meanwhile the number of cluster centers is gotten. The improved K-means algorithm is taken to optimize the structure of RBF neural network, and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability. © 2016 IEEE.

关键词： Density K-means algorithm RBF neural network Subtractive clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Informative Path Planning based on Global and Local Active Sensing for Environmental Field Mapping

Informative Path Planning based on Global and Local Active S...

引用

2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Jiahui Yang, Bingyu Yang, Kaibo Li, Teng School of Control Science and Engineering Shandong University Jinan250061 China School of Statistics Beijing Normal University Zhuhai519085 China Key Laboratory of Machine Intelligence and System Control Ministry of Education of the People's Republic of China Jinan250061 China

ISBN: (纸本)9781665481090

Mobile robots can reconstruct environmental fields through active sensing and autonomous navigation, so as to achieve online environmental monitoring. Active sensing enables determine the target sampling location by exploiting the critical information in the target monitored environments. The key information for active sensing is commonly determined by the objective function based on environmental field statistics. In this paper, a novel global and local informative path planning method based on Gaussian process (GP) and multivariate conditional mutual information (MCMI) is proposed, which can achieve effective and reliable execution of information-based active sensing and path planning. Experimental results demonstrate the performance of the proposed method for robotic mobile sensing and environmental field mapping. © 2024 IEEE.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Abnormal Test Data Diagnosis and Prognosis Based on Least Squares Support Vector machine

Abnormal Test Data Diagnosis and Prognosis Based on Least Sq...

引用

2020 IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2020

作者： Han, Huilian Cui, Zhaojing National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

To figure out the shortage of traditional fault diagnosis method, the paper carried out fault diagnosis and forecast by detecting abnormal data based on LS-SVM algorithm. SVM has the optimal solution and good generalization ability in small samples and can avoid falling into local minimum. The paper introduced the principle of SVM, made up the model of non-linear least squares support vector regression solve various breakdowns coexist of actual circumstance, then identified and prognosis abnormal data by using high- dimension feature vector space as the input of LS-SVM. Examples showed that abnormal data could be detected effectively. The results proved that the model of fault diagnosis and prediction effective and has higher rate of correct judgments. © 2020 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：