检索结果-内蒙古大学图书馆

第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

来源：评论

学校读者我要写书评

暂无评论

A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-Scale Twin Range image 42

A Point Cloud Segmentation Method Based on Ground Point Clou...

引用

42nd Chinese control Conference, CCC 2023

作者： Zhou, Yicheng Peng, Gang Duan, Hangqi Wu, Zhangang Zhu, Xukang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Shantui Construction Machinery Co. Ltd. Jining272073 China

ISBN: (纸本)9789887581543

Aiming at the shortcomings of traditional point cloud segmentation algorithms, this paper proposes a 3D lidar point cloud segmentation method combining ground filtering and multi-scale twin range image construction. This method fully considers the geometric space distribution characteristics of the point cloud and the randomness of the positions between objects. Based on the assumption of multi plane model, the point cloud is divided into sectors. The ground points are removed through region ground plane fitting and growth, and then the multi-scale twin range image is constructed according to non-ground points. On the basis of the twin range image, the Breadth First Search algorithm is improved, the depth-breadth joint search strategy is proposed, all the points to be clustered are traversed, and the adaptive clustering threshold is used for point cloud clustering, which realizes a point cloud segmentation method with higher accuracy and robustness. Through the experimental analysis of KITTI dataset and collected campus outdoor scene dataset, compared with the traditional point cloud segmentation algorithm, the method proposed in this paper can effectively improve the point cloud segmentation accuracy and robustness of mobile robots in complex environments. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

引用

COMPLEXITY 2021年第1期2021卷

作者： Wang, Zhaojing Yang, Weidong Zhang, Hong Zheng, Ying Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).

关键词： Process monitoring

来源：评论

学校读者我要写书评

暂无评论

Prescribed-Time Cooperative Output Regulation of Heterogeneous Multi-Agent Systems

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期20卷 2432页

作者： Chen, Chongyang Han, Yiyan Zhu, Song Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial Intelligence Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China

This paper concentrates on the prescribed-time cooperative output regulation problem (CORP) of linear heterogeneous multi-agent systems (HMASs) under directed topology. Firstly, a novel distributed observer with prescribed-time convergence is designed to estimate the state of the exosystem. Then, two distributed prescribed-time control protocols, one based on the agent's state, the other based on the agent's output, are proposed using the observed exosystem's state. It is shown that the CORP of linear HMASs with any different orders is solved in a prescribed (any user-chosen as needed) time. Unlike the existing finite-time control strategies, the settling time is guaranteed by the designed first-order smooth control protocols, independent of the system's initial values and the control parameters. Lastly, a numerical simulation is presented to verify our theoretical results.

关键词： Cooperative output regulation prescribed-time consensus heterogeneous multi-agent systems directed topology

来源：评论

学校读者我要写书评

暂无评论

A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding

arXiv

引用

arXiv 2024年

作者： Chen, Xiaoqing Jia, Tianwang Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While previous studies have addressed one or two of these issues, simultaneous accommodation of all three challenges remains challenging and unexplored. This paper fills this gap, by proposing an Aligned and Augmented Adversarial Ensemble (A3E) algorithm and integrating it into three privacy protection scenarios (centralized source-free transfer, federated source-free transfer, and source data perturbation), achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on three public EEG datasets demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness in all three privacy-preserving scenarios, even outperforming state-of-the-art transfer learning approaches that do not consider privacy protection at all. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs. © 2024, CC BY.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Pose Estimation Based on Bidirectional Visual-Inertial Odometry with 3D LiDAR (BV-LIO)

引用

REMOTE SENSING 2024年第16期16卷 2970页

作者： Peng, Gang Gao, Qiang Xu, Yue Li, Jianfeng Deng, Zhang Li, Cong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Autonomous Intelligent Unmanned Syst Wuhan 430074 Peoples R China Beijing Railway Engn Electromech Technol Res Inst Beijing 100071 Peoples R China Wuhan Lisai Technol Co Ltd Wuhan 430074 Peoples R China

Due to the limitation of a single sensor such as only camera or only LiDAR, the Visual SLAM detects few effective features in the case of poor lighting or no texture. The LiDAR SLAM will also degrade in an unstructured environment and open spaces, which reduces the accuracy of pose estimation and the quality of mapping. In order to solve this problem, on account of the high efficiency of Visual odometry and the high accuracy of LiDAR odometry, this paper investigates the multi-sensor fusion of bidirectional visual-inertial odometry with 3D LiDAR for pose estimation. This method can couple the IMU with the bidirectional vision respectively, and the LiDAR odometry is obtained assisted by the bidirectional visual inertial. The factor graph optimization is constructed, which effectively improves the accuracy of pose estimation. The algorithm in this paper is compared with LIO-LOAM, LeGO-LOAM, VINS-Mono, and so on using challenging datasets such as KITTI and M2DGR. The results show that this method effectively improves the accuracy of pose estimation and has high application value for mobile robots.

关键词： multi-sensor fusion pose estimation 3D LiDAR visual-inertial odometry

来源：评论

学校读者我要写书评

暂无评论

Competition on robust deep learning

引用

National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

来源：评论

学校读者我要写书评

暂无评论

A Memristive Circuit Implementation of Eyes State Detection in Fatigue Driving Based on Biological Long Short-Term Memory Rule

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2218-2229页

作者： Wang, Zilu Hong, Qinghui Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

关键词： Memristors Integrated circuit modeling image processing Fatigue Threshold voltage Software algorithms Computer architecture Memristive circuit design image binarization eyes state detection biological long short-term memory (B-LSTM)

来源：评论

学校读者我要写书评

暂无评论

A Novel Pairwise Domain-Adaptation-Assisted Dual-Task Learning Approach to Coprediction of Robotic Machining Efficiency and Quality in New Parameter Spaces

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Ma, Guijun Wang, Zidong Yang, Zeyuan Chen, Ruijuan Liu, Weibo Zhang, Yong Yan, Sijie Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Wuhan Text Univ Res Ctr Nonlinear Sci Sch Math & Phys Sci Wuhan 430200 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430081 Peoples R China

Accurate prediction of material removal depth and averaged surface roughness is crucial for evaluating the performance of robotic belt grinding (RBG). Nevertheless, the machining parameters of RBG across different spaces exhibit various data distributions, which often results in prediction shifts on unseen machining parameters when using conventional approaches. In this article, we introduce a pairwise domain adaptation-assisted dual-task learning (PW-DA-DTL) method for copredicting material removal depth and averaged surface roughness with regard to new RBG machining parameter spaces. The multigate mixture-of-experts method is employed as the foundational framework for dual-task learning, effectively capturing and modeling the relationships between material removal depth and average surface roughness by leveraging their inherent task interdependencies. The pairwise domain adaptation strategy is put forward to simultaneously enhance sample diversity and mitigate cross-domain data distribution discrepancy between the existing and new RBG machining parameter spaces. Comparative experiments are presented to demonstrate the effectiveness and superiority of the proposed PW-DA-DTL method.

关键词： Machining Training Surface roughness Rough surfaces Supervised learning Electronic mail Transfer learning Manufacturing processes Correlation Belts Averaged surface roughness domain adaptation (DA) dual-task learning material removal depth (MRD) robotic belt grinding (RBG)

来源：评论

学校读者我要写书评

暂无评论

Full-Circuit Implementation of Transformer Network Based on Memristor

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第4期69卷 1395-1407页

作者： Yang, Chao Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Elect Elect & Comp Sci Liuzhou 545006 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

As an emerging in-memory element, memristor has been widely used in various neural network circuits to represent the weights and accelerate the calculation. However, the Transformer Network (TN), one of the most important models for machine vision and natural language processing in recent years, has not yet been full-circuit implemented using memristors due to the complex calculation process and data storage. In order to carry out the computation of the TN more efficiently, this work proposes a memristor-based full-circuit implementation of the TN capable of: 1) a memristor crossbar module to preserve the weights of the TN and perform the vector-matrix multiplications;2) an analog signal memory module to store the analog signal directly in near-memory mode;3) function circuit modules to achieve five transformations, namely Softmax, Layer Normalization, ReLU, Multiply-add and Residual;4) a timing signal generation module to schedule operations of the circuit. The proposed TN circuit can complete all calculations directly based on the analog signal without using any analog-digital converter (ADC), digital-analog converter (DAC) and digital memory. In addition, character image recognition experiments are carried out in PSPICE to verify the functional correctness of the designed circuit. The corresponding signal retention rates of the analog memory, the performance of the whole circuit, and the non-idealities of the memristors are also analyzed. The results indicate that the circuit has advantages in terms of area overhead, energy efficiency and anti-noise.

关键词： Memristors Transformers Neural networks Data models Task analysis image recognition Computational modeling Memristor transformer network analog signal memory circuit implementation

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：