检索结果-内蒙古大学图书馆

42nd Chinese Control Conference, CCC 2023

作者： Liu, Feng Wang, Dongping Lyu, Kedong Ding, Xiaojun School of Electrical Information Engineering North Minzu University Yinchuan750030 China College of Computer and Information Engineering Tianjin Agricultural University Tianjin300392 China Tianjin Electronic Information Technician College Department of Automation Engineering Tianjin300350 China

ISBN: (纸本)9789887581543

The adaptive-skew of the single gimbal control moment gyroscopes (ASGCMG) has been proposed. The effectiveness of the singularity avoidance with varying variable skew angle in ASGCMG is not clear. In this paper, the singularity of ASGCMG is analyzed based on Binet-Cauchy identity, and the singular gimbal angles are given, so the characteristics of the ASGCMG system can be used for designing logic. The mechanism of the singularity avoidance with assisting of the varying skew angle is revealed. Based on convex optimization theory, the new steering law with null motion is designed. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Optimal Control for Continuous-Time Quantized Systems via Adaptive Dynamic Programming 22

Reinforcement Learning-Based Optimal Control for Continuous-...

引用

22nd IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2025

作者： Qasem, Omar Gao, Weinan Daoud, Omar R. School of Engineering and Computing American International University Electrical and Computer Engineering Department Al-Jahra Kuwait Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Lianning Shenyang 110819 China

ISBN: (纸本)9798331542726

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems. © 2025 IEEE.

关键词： Adaptive dynamic programming Data-Driven control Dynamic Pro-gramming Quantized Systems Reinforcement Learning

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of HDB3 Clock Recovery Module with Large Frequency Range 2

Design and Implementation of HDB3 Clock Recovery Module with...

引用

2nd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2023

作者： Li, Haoxiang Lu, Shengbo Zhao, Zixiang Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China College of Oceanography and Space Informatics Qingdao266580 China

ISBN: (纸本)9781665462532

With the development of the communication and information industry, the demand for high-speed data transmission is growing. One of the essential tasks of the receiver is to extract synchronization information from the received data polluted by noise and recover the data and clock accurately. Therefore, clock data recovery (CDR) is crucial in optical transmission systems and other digital transmission fields. However, the traditional bit synchronous clock recovery can only recover signals of a specific frequency. If a front-end frequency meter is added at the beginning of signal input to measure the input signal frequency, and the real-time control bit synchronization extraction circuit uses the input signal frequency as its working frequency, the operating range of the bit synchronization clock recovery circuit can be significantly improved. This will be the perfect combination of clock recovery performance and time cost. This paper studies the basic principles of bit synchronous clock recovery circuit and dynamic frequency meter. The HDB3 dynamic clock in a wide frequency range is extracted in FPGA using an all-digital design method. Through simulation and verification, the bit synchronous CDR with a pre-dynamic frequency meter has a more comprehensive recovery range, reaching 64Kb/s to 32.768Mb/s. © 2023 IEEE.

关键词： Real time control

来源：评论

学校读者我要写书评

暂无评论

A NOVEL REAL-TIME DEVS SIMULATION ARCHITECTURE WITH HARDWARE-IN-THE-LOOP CAPABILITIES

A NOVEL REAL-TIME DEVS SIMULATION ARCHITECTURE WITH HARDWARE...

引用

2024 Annual Modeling and Simulation Conference, ANNSIM 2024

作者： Fernández-Sebastián, Óscar Cárdenas, Román Arroba, Patricia Risco-Martín, José L. Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain Department of Electronic Engineering Universidad Politécnica de Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Spain

ISBN: (纸本)9781713899310

Advanced Modelling and Simulation (M&S) techniques, including Digital Twins (DTs) and Hardware-In-the-Loop (HIL) simulation, are essential for the design and operation of today’s complex systems. Despite these advances, there remains a gap between the simulation methods used in design and those used in implementation. This paper introduces a Real-Time (RT) simulation framework that connects models using the Discrete-Event System Specification (DEVS) with external systems, improving implementation and operation. The Python xDEVS tool has been enhanced to introduce a new suite of real-time simulation components and a DEVS-based communication interface, facilitating adaptable communication protocols. The framework demonstrates its capability to integrate both DEVS and non-DEVS systems and establish effective communication through various experiments, ranging from RT simulation of DEVS models to complex multi-component systems. This tool has significant potential in supporting real and incremental system implementations through HIL techniques, making a valuable contribution to complex system design and operation. © 2024 Society for Modeling & Simulation International (SCS).

关键词： Hardware-in-the-loop simulation

来源：评论

学校读者我要写书评

暂无评论

Terrain-Adaptive Cooperative Braking Control for Heavy-Haul Trains 7th

Terrain-Adaptive Cooperative Braking Control for Heavy-Haul...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Shu, Boyu Huang, Zhiwu Yu, Xiaoquan Ren, Wanwan Rong, Jieqi Li, Heng Yang, Yingze School of Automation Central South University Changsha China School of Traffic and Transportation Engineering Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China School of Electronic Information Central South University Changsha China

ISBN: (纸本)9789819733279

In order to ensure the safe operation of multi-carriage heavy-haul trains, braking control is very important. However, the decentralized braking control method causes the relative speed between the cars to be non-zero and makes the coupling force between the carriages exceeded, especially when the railway line has a gradient, which poses a threat to the operational safety of heavy-haul trains. In order to solve the above problems, an all-terrain adaptive train cooperative braking control strategy is proposed in this paper. First, a cyber-physics system including a multi-particle model is established to describe the heavy-haul trains. Second, a cooperative braking control strategy is proposed to keep the speeds of the carriages consistent and the relative displacement between adjacent carriages to be stable within a safe range. Third, extensive simulation experiments are performed to evaluate different braking methods for trains in complex terrain. The cooperative braking control strategy in this paper has potential advantages in improving the safety and stability of heavy-haul trains and has been fully verified by simulation experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive control systems

来源：评论

学校读者我要写书评

暂无评论

A Case-based Channel Selection Method for EEG Emotion Recognition Using Interpretable Transformer Networks 23

A Case-based Channel Selection Method for EEG Emotion Recogn...

引用

2023 International Conference on computer, Vision and Intelligent Technology, ICCVIT 2023

作者： Du, Yang Guan, Zijing Huang, Weichen Zhang, Xichun Huang, Qiyun School of Automation Science and Engineering South China University of Technology China Center for Brain-Computer Interface Pazhou Lab China Research Center for Brain-Computer Interface Pazhou Lab China South China University of Technology China

ISBN: (纸本)9798400708701

Optimizing the selection of features and channels with satisfactory emotion classification accuracy is important for electroencephalograph (EEG)-based emotion recognition. Here, we propose a novel case-based channel selection method based on interpretable transformer networks for emotion recognition. Gradients and relevance of the prediction of the transformer model were used for backpropagation to produce the attention relevance map. Critical channels were selected by ranking the attention relevance value. We test the effectiveness of the proposed method on the multi-channel EEG emotional datasets SEED. The results demonstrated that the method can pick out the critical channels effectively with relatively high classification accuracy. The locations of selected channels converged on the frontal and temporal areas which were consistent with neuroscience findings. Thus, the proposed method has a potential to reduce computation time and assist source localization in cognitive neuroscience studies. © 2023 ACM.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level 61

HAHE: Hierarchical Attention for Hyper-Relational Knowledge ...

引用

61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Luo, Haoran Haihong, E. Yang, Yuhao Guo, Yikai Sun, Mingzhi Yao, Tianyu Tang, Zichen Wan, Kaiyang Song, Meina Lin, Wei School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China

ISBN: (纸本)9781959429722

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information. The internal structure of HKG can be represented as a hypergraph-based representation globally and a semantic sequence-based representation locally. However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs' representation. To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets. In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task. Our code is publicly available. © 2023 Association for Computational Linguistics.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

AGSQTL: Adaptive Green Space Quality Transfer Learning for Urban Environmental Monitoring 8

AGSQTL: Adaptive Green Space Quality Transfer Learning for U...

引用

8th IET Smart Cities Symposium, SCS 2024

作者： Chundru, Swathi Balantrapu, Siva Subrahmanyam Maroju, Praveen Kumar Alam, Naved Dutta, Pushan Kumar Whig, Pawan Motivity labs INC QA Automation Engineer United States United States TX United States Department of computer science and engineering Jamia hamdard New Delhi India School of Engineering and Technology Amity India Threws VIPS-TC New Delhi India

ISBN: (纸本)9781837242672

This study presents a novel machine learning approach, Adaptive Green Space Quality Transfer Learning (AGSQTL), for assessing urban green space quality. Unlike traditional transfer learning methods, AGSQTL incorporates domain-specific adaptations and dynamic feature analysis tailored to green space monitoring. The algorithm utilizes a pre-trained source model and adapts it to the target domain of green space images, categorizing them as Healthy, Dried, or Contaminated. AGSQTL introduces several innovative components: domain-specific layers for capturing unique green space features, an adaptive domain discrepancy adjustment mechanism, dynamic feature pruning based on Grad-CAM importance analysis, and a self-attention mechanism for spatial context understanding. The model is trained on a dataset of 986 green space images, augmented to balance class distribution. Performance evaluation includes accuracy, precision, recall, F1-score, Cohen's Kappa, and ROC-AUC, as well as practical considerations such as model file size and inference time. The algorithm demonstrates superior adaptability to green space features compared to standard transfer learning approaches, achieving 99.2% accuracy on the test set. Grad-CAM visualizations confirm the model's ability to identify relevant areas for classification. AGSQTL was deployed as an interactive web application using Streamlit, allowing real-time green space quality assessment. This research contributes a more adaptive and efficient methodology for urban green space monitoring, with potential applications in environmental management and urban planning. © The Institution of engineering & Technology 2024.

关键词： Adaptive algorithms

来源：评论

学校读者我要写书评

暂无评论

Convolution-Transformer for Image Feature Extraction

引用

computer Modeling in engineering & Sciences 2024年第10期141卷 87-106页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

来源：评论

学校读者我要写书评

暂无评论

Multi-View Dynamic Kernelized Evidential Clustering

引用

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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：