检索结果-内蒙古大学图书馆

43rd Chinese control Conference, CCC 2024

作者： Chen, Kai Lv, Zheng Sun, Qiao Zhao, Jun Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology Ministry of Education Dalian116024 China Electric Operations Control Center State Grid Dalian Power Supply Company Dalian China

ISBN: (纸本)9789887581581

The monitoring of smoke emissions serves as a crucial reference for guiding operations in the steel industry. However, due to the scarcity of industrial smoke datasets, most deep learning methods encounter difficulties when addressing this problem. To address the issue of limited smoke data, this paper proposes a novel data augmentation method called the bidirectional recurrent generative network. This algorithm combines the concepts of recurrent generative networks and Laplacian pyramid networks to learn smoke generation from smoke-free blast furnaces and eliminate smoke from smoke blast furnaces. In order to overcome the limitation of recurrent generative networks in accurately depicting target contours, this paper introduces contour feature vectors, which provide precise characterization of the target contours through constraint-based feature vectors. Furthermore, to alleviate the difficulty of generation, the Laplacian pyramid model is introduced, which reduces parameters and learning time by learning from low-resolution images. To validate the effectiveness of the proposed algorithm, extensive ablation experiments and comparative trials are conducted on four different smoke image datasets from industrial scenarios with limited quantities. The results demonstrate significant improvements achieved by this paper's algorithm. Compared to other generation methods, the proposed algorithm exhibits superior performance in smoke generation tasks, offering an effective solution to address the challenges of scarce industrial smoke data and the training difficulties faced by related deep learning algorithms. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Laplace transforms

来源：评论

学校读者我要写书评

暂无评论

Review on electrochemical carbon dioxide capture and transformation with bipolar membranes

引用

Chinese Chemical Letters 2023年第8期34卷 114-129页

作者： Jinyun Xu Guoqiang Zhong Minjing Li Di Zhao Yu Sun Xudong Hu Jiefang Sun Xiaoyun Li Wenju Zhu Ming Li Ziqi Zhang Yu Zhang Liping Zhao Chunming Zheng Xiaohong Sun School of Chemical Engineering Tianjin Key Laboratory of Green Chemical Technology and Process EngineeringState Key Laboratory of Separation Membrane and Membrane ProcessesTiangong UniversityTianjin 300387China School of Materials Science and Engineering Key Laboratory of Advanced Ceramics and Machining TechnologyMinistry of EducationTianjin UniversityTianjin 300072China Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food poisoning Beijing Center for Disease Prevention and ControlBeijing 100013China Advanced Materials Research Laboratory CNOOC Tianjin Chemical Research and Design InstituteTianjin 300131China

Anthropogenic carbon dioxide(CO_(2))emission from the combustion of fossil fuels aggravates the global greenhouse *** implementation of CO_(2)capture and transformation technologies have recently received great attention for providing a pathway in dealing with global climate *** these technologies,electrochemical CO_(2)capture technology has attracted wide attention because of its environmental friendliness and flexible operating *** membranes(BPMs)are considered as one of the key components in electrochemical devices,especially for electrochemical CO_(2)reduction and electrodialysis *** create an alkaline environment for CO_(2)capture and a stable pH environment for electrocatalysis on a single *** key to CO_(2)capture in these devices is to understand the water dissociation mechanism occurring in BPMs,which could be used for optimizing the operating conditions for CO_(2)capture and *** this paper,the references and technologies of electrochemical CO_(2)capture based on BPMs are reviewed in detail,thus the challenges and opportunities are also discussed for the development of more efficient,sustainable and practical CO_(2)capture and transformation based on BPMs.

关键词： Bipolar membranes Carbon dioxide Electrochemical capture Transformation CO_(2)reduction Electrodialysis devices Water dissociation

来源：评论

学校读者我要写书评

暂无评论

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

引用

Computer Modeling in Engineering & Sciences 2023年第4期135卷 653-671页

作者： Xuhui Zhu Pingfan Xia Qizhi He Zhiwei Ni Liping Ni School of Management Hefei University of TechnologyHefei230009China School of Statistics Zhejiang Gongshang UniversityHangzhou310018China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of EducationHefei230009China Collaborative Innovation Center for Statistical Data Engineering Technology and Application ZhejiangGongshang UniversityHangzhou310018China

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational *** ensemble system aims to attain equivalent or better classification accuracy with fewer ***,current methods fail to identify precise solutions for constructing an ensemble *** this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational ***,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low *** addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of *** selected ELMs can then be used to forman ensemble *** on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

关键词： Ensemble classifier salp swarmalgorithm diversity measure multiple classifiers system extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

A Hybrid SVM Based Real-Time Attack Classification Method for Cyber Network 42

A Hybrid SVM Based Real-Time Attack Classification Method fo...

引用

42nd Chinese control Conference, CCC 2023

作者： Qin, Xin Jin, Feng Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789887581543

The cyber network attacks on the Internet have the characteristics of diversity, uncertainty and real-time. This paper proposes a hybrid support vector machine (HSVM)-based classification method for real-time cyber network attack detection. In the supervised environment, a one-class classifier is built to divide the unknown attack traffic into 'other classes', and a new extension module is established for the identified unknown attacks, so as to improve the classification accuracy of unknown attacks. Then, an online training algorithm is used to enable the classifier to quickly distinguish cyber network attacks and improve the timeliness of the intrusion detection process. To verify the effectiveness of the proposed method is employed, three classifiers, i.e., the random forest, the support vector machine and the multi-layer perceptron are employed for comparative experiments based on the public data set KDDCup99. Results show that the proposed method has the advantages of higher classification accuracy and less time consumption. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

ESO-Based Data Driven Set-Point Learning control for Nonlinear batch processes with PD-Type Feedback control Structure Subject to Nonrepetitive Uncertainties* 13

ESO-Based Data Driven Set-Point Learning Control for Nonline...

引用

13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Ahmad, Naseem Hao, Shoulin Liu, Tao Gong, Yihui Zhang, Jiyan Wang, Haixia Zhu, Yong Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China School of Biomedical Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example. © 2024 IEEE.

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

A control Method for Coal Slime Water Thickening process in Coal Preparation Plants Based on BP-TSFNN

A Control Method for Coal Slime Water Thickening Process in ...

引用

2024 China Automation Congress, CAC 2024

作者： Fu, Shangluo Zhou, Fan Zhao, Jun Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education China School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Engineering Dalian Polytechnic University Dalian China

ISBN: (纸本)9798350368604

Aiming at the complex mechanism of coal slime water thickening process in coal preparation plant, which has the characteristics of multivariable and strong nonlinearity leading to the difficulty of realizing the intelligent optimization control, a model predictive control method based on back propagation neural network (BPNN) and Takagi-Sugeno fuzzy neural network (TSFNN) cascade (BP-TSFNN-MPC) is proposed. Further, in order to realize the intelligent optimization control of the thickening process, the BP-TSFNN-MPC controller is constructed, and the gradient descent optimization algorithm is used to solve the optimization problem. In the experimental part, the effectiveness of the model and control method is verified by combining the production data of the slime water concentration process and the simulation model of the thickener. The results show that the BP-TSFNN model has high prediction accuracy for the concentration process and BP-TSFNN-MPC has good control performance. © 2024 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Regularization based reweighted estimation algorithm for nonlinear systems in presence of outliers 43

Regularization based reweighted estimation algorithm for non...

引用

43rd Chinese control Conference, CCC 2024

作者： Yawen, Mao Chen, Xu Jiangnan University School of science Wuxi214122 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China

ISBN: (纸本)9789887581581

Outliers usually occur during data collection process and can lead to poor system identification performance. This paper considers the parameter estimation algorithms for Hammerstein systems whose output measurements are contaminated with additive outliers. By incorporating a lasso-type penalty on the outlier vector, a regularization based least squares identification algorithm that can interactively estimate the system parameters and outliers is proposed based on the auxiliary model identification idea. To further reduce the impact of outliers on the identification effect, each observation is assigned an individual weight, which decreases as the number of iterations increases once the corresponding data is determined to be an outlier. Finally, the outliers can be excluded from the contaminated data without any prior knowledge and the estimation bias is reduced. A numerical example is given to confirm the effectiveness of the proposed algorithm. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Deterministic Event-Based Physical Watermarks Against Replay Attacks in Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

引用

IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 61-69页

作者： Zhao, Xudong Xing, Wei Zhang, Junfeng Wang, Xinyu Zong, Guangdeng Zhao, Ning Hainan University School of Information and Communication Engineering Haikou570288 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116622 China Tiangong University School of Control Science and Engineering Tianjin300387 China Bohai University College of Control Science and Engineering Jinzhou121013 China

Cyber-physical systems (CPSs) are of great importance in various applications and have garnered significant attention. Ensuring their security is a crucial concern, while this paper focuses on the security of CPSs against replay attacks during the transmission of sensor measurements over a wireless network. It is widely recognized that incorporating physical watermark signals is an effective approach to mitigate replay attacks. However, traditional physical watermarks are time-based and result in a notable degradation in the performance of Linear Quadratic Gaussian systems. With this in mind, a novel event-based physical watermarking technique is devised to address the performance degradation and detect replay attacks. The approach involves a deterministic physical watermark schedule that utilizes the innovation sequence to determine whether to incorporate physical watermarks or not. We provide evidence that the probability of successfully embedding physical watermarks increases when the system is targeted by malicious attacks. Additionally, we demonstrate that the deterministic event-based watermarking approach is more effective than the traditional time-based watermarking method. Finally, simulation examples are included to validate and illustrate the theoretical results. © 2023 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

New Adaptive ESO Based Data-Driven Anti-Disturbance control for Nonlinear Systems with Convergence Guarantee ⁎

引用

IFAC-PapersOnLine 2024年第14期58卷 397-402页

作者： Shoulin Hao Yihui Gong Naseem Ahmad Shuhao Yue Tao Liu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Institute for Advanced Control Technology Dalian University of Technology Dalian 116024 China

In this paper, a new adaptive extended state observer based data-driven anti-disturbance control (AESO-DDADC) design is proposed for industrial nonlinear systems with unknown dynamics subject to external disturbances. By reformulating such system description into a compact-form dynamic linearization model with a residual term, a new AESO is firstly constructed to estimate the residual term using the partial derivative (PD) estimation from the previous time step, such that the residual term could be proactively counteracted by the feedback control law, in contrast to the existing data-driven ESO where the residual term in the PD estimation is absolutely neglected to facilitate the convergence analysis. Then, the bounded convergence of PD estimation and AESO is jointly analyzed by the Gerschgorin disk theorem, followed by robust convergence analysis of the established closed-loop system. Moreover, another AESO-DDADC scheme is developed using a partial-form dynamic linearization model of the system, along with rigorous robust convergence analysis. Finally, an illustrative example is shown to confirm the efficacy and advantages of the proposed designs.

关键词： Nonlinear systems data driven adaptive extended state observer anti-disturbance control convergence analysis

来源：评论

学校读者我要写书评

暂无评论

GPP estimation of a grassland ecosystem based on photosynthesis-hydrology coordination optimization

GPP estimation of a grassland ecosystem based on photosynthe...

引用

2024 China Automation Congress, CAC 2024

作者： Fu, Lijiang Tan, Jinglu Jiang, Yongnian Allakhverdiev, Suleyman I. Xu, Zhenyu Guo, Ya Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Department of Chemical & Biomedical Engineering University of Missouri ColumbiaMO United States Jiangsu Zhongnong IoT Technology Co. LTD Yixing China Timiryazev Institute of Plant Physiology RAS Moscow Russia Longcom Internet of Things Co. Ltd Hefei China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China

ISBN: (纸本)9798350368604

Accurate estimation of gross primary production (GPP, ecosystem level photosynthesis) is critical for monitoring terrestrial ecosystems. However, most of the current models used for GPP estimation are with many unknown parameters and rarely include the effects of soil water stress due to the covariation of soil moisture and air humidity. In addition, grasslands, as the most widely distributed ecosystems in the world, have simple ecosystem structure and are easily affected by soil drought. Therefore, in this paper, we applied photosynthesis-hydrology coordination optimization theory and soil water potential based on soil moisture effect to develop a combined model (PH model) for GPP estimation of grassland ecosystems and used the US-Var grassland ecosystem, one of the FLUXNET sites from January to July 2004 (the driest period in recent years) as an example to validate the model. The results show that the model can effectively track the measured GPP during soil drought period and is better than the result from another commonly used photosynthesis coordination model (P-model) which does not consider the effect of soil drought. Further, we can infer the vegetation photosynthetic capacity parameters including the maximum carboxylation capacity (Vcmax) and the maximum electron-transport capacity (Jmax) under the effect of soil drought. The result also shows that if soil drought is not considered, the photosynthetic capacity parameters of vegetation will be overestimated. These results provide evidence for the application of photosynthesis-hydrology coordination theory in the assessment of ecosystem carbon cycle under the intensified climate changes. © 2024 IEEE.

关键词： coordination optimization theory GPP estimation soil drought

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：