检索结果-内蒙古大学图书馆

Erratum to “Time-Frequency Properties of the Short-Time Linear Canonical Transform and Its Application”

Mathematical Problems in Engineering 2022年第1期2022卷

作者： Lei Huang Qingying Sun Qian Xi Yuhu Liu Mengsheng An Zihan Zhou School of Computer Science and Technology Huaiyin Normal University Huai’an 223300 *** School of Automation Chongqing University Chongqing 400044 *** Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing University Chongqing 400044 ***

来源：评论

学校读者我要写书评

暂无评论

Robust model predictive control for linear systems with polytopic uncertainties under weighted mef-tod protocol

引用

IEEE Transactions on systems, Man, and Cybernetics: systems 2019年第7期49卷 1470-1481页

作者： Song, Yan Wang, Zidong Ding, Derui Wei, Guoliang Department of Control Science and Engineering Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science Brunel University London Uxbridge21589 United Kingdom School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia

This paper is concerned with the robust {-Grant-info-{2}/H-\infty } model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (... 详细信息

This paper is concerned with the robust {-Grant-info-control/H-\infty } model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (MEF-TOD) protocol. To prevent data from collision, the weighted MEF-TOD protocol is employed during the data transmission from sensors to controller, where only one sensor is allowed to transmit the measurement at every time instant. A switched linear system is established with respect to the switching signals generated by the adopted weighted MEF-TOD protocol. By taking the influence of the exogenous disturbance and the weighted MEF-TOD protocol into consideration, an online optimization problem with nonconvex conditions is formulated. Then, to deal with the couplings of unknown variables, singular value decomposition technique is utilized to transform the nonconvex conditions into solvable ones. Subsequently, a set of dynamic output-feedback controllers is designed to guarantee the stability of the closed-loop system with guaranteed robust { H-control/H-\infty } performance. Finally, a direct current motor example is used to illustrate the validity and effectiveness of the proposed methods. © 2013 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

A security enhanced certificateless aggregate signcryption scheme for VANETs

引用

Peer-to-Peer Networking and Applications 2025年第4期18卷

作者： Liu, Dong Tang, Wentao Wang, Liangliang Liu, Yang Liu, Zhiquan Zhang, Kai Li, Weiwei School of Computer Science and Technology Xidian University Xi’an710699 China the College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China Institute of Network Security Inspection and Evaluation China Industrial Control System Cyber Emergency Response Team Beijing100040 China Key Laboratory of Industrial Information Security Perception and Evaluation Technology Ministry of Industry and Information Technology Beijing100040 China the College of Cyber Security Jinan University Guangzhou510632 China

Unlike traditional Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) operate in open environments and transmit information through wireless communication technologies, making VANETs more susceptible to various attacks. To protect the security of drivers’ private information and improve the efficiency of the transportation system, various Certificateless Signcryption (CLSC) schemes have been proposed. Recently, Shao et al. designed a provably secure lightweight authentication scheme, which satisfied aggregate signcryption and batch unsigncryption. Unfortunately, we uncovered there are some vulnerabilities through security analysis in their scheme. In this paper, we first give an analysis method based on their security model with public key replacement to demonstrate the weakness. Second, we introduce a security enhanced CLSC scheme with a small index, demonstrating that the improved scheme is existential unforgeability under adaptive chosen message attacks (EUF-CMA), indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2), and our scheme can resist extra Type-III adversaries which can combine the invalid signcryptions to generate a valid signcryption by formal security proof and heuristic security analysis. Finally, we compare our scheme with recent CLSC schemes in security performance, computation overhead, and communication overhead. The computational overhead of our scheme is below 41 ms under computing resource constraints while still meeting extremely high levels of security. The results show that our scheme is more suitable for VANETs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Trajectory optimisation design of robot based on artificial intelligence algorithm

引用

International Journal of Wireless and Mobile Computing 2019年第1期16卷 35-40页

作者： Huang, Li Zhang, Kai Hu, Wei Li, Chengcheng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan430081 China

Artificial intelligence has attracted more and more attention and has been widely used in all walks of life, especially in the education industry;artificial intelligence has gradually become the core. Aiming at the problem of robot trajectory planning in artificial intelligence, this paper applies the project teaching method to the course of artificial intelligence, regards trajectory planning as a project, analyses and studies it, uses ant colony algorithm to find the optimal planning path. Through the teaching of the project, the students will understand the ant colony algorithm more deeply. The algorithm is programmed independently to achieve the final trajectory optimisation. Students become the main body of the classroom, give full play to the initiative and enthusiasm of the students, through the operation of the project to train the students' innovative ability and cooperation ability, and improve the overall quality of the college students. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Pinning boundary sampled-data synchronization of coupled reaction–diffusion neural networks

引用

Neurocomputing 2025年 648卷

作者： Zi-Peng Wang Bo-Ming Chen Junfei Qiao Biao Luo Huai-Ning Wu Tingwen Huang Guangwei Chen School of Information Science and Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System and Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing 100124 China School of Automation Central South University Changsha 410083 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China

In this paper, the pinning boundary sampled-data (SD) synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) with SD-based couplings is investigated under boundary and distributed SD measurements. First, a pinning boundary SD controller is designed to effectively address the synchronization problem of CRDNNs, utilizing boundary and distributed SD measurements and requiring only a small number of sensors and actuators. Then, linear matrix inequalities provide adequate conditions that ensure exponential stability of the synchronization error system by using the designed pinning boundary SD controller, derived through inequality techniques and Lyapunov functionals. Finally, we present a numerical example to showcase the practical applicability and effectiveness of the proposed pinning boundary SD synchronization method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

arXiv

引用

arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Distributed Coordinated Emergency Frequency control Strategy for Renewables-Rich Power systems Based on Multi-Agent Reinforcement Learning

SSRN

引用

SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Topology inference for multi-agent cooperation under unmeasurable latent input

arXiv

引用

arXiv 2020年

作者： Jiao, Qing Li, Yushan He, Jianping Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable latent input refers to intrinsic system signal and extrinsic environment interference. Considering the time-invariant/varying nature of the input, we propose two-layer optimization-based and iterative estimation-based topology inference algorithms (TO-TIA and IE-TIA), respectively. TO-TIA allows us to capture the separability of global agent state and eliminates the unknown influence of time-invariant input on system dynamics. IE-TIA further exploits the identifiability and estimability of more general time-varying input and provides an asymptotic solution with desired convergence properties, with higher computation cost compared with TO-TIA. Our novel algorithms relax the dependence of observation scale and leverage the empirical risk reformulation to improve the inference accuracy in terms of the topology structure and edge weight. Comprehensive theoretical analysis and simulations for various topologies are provided to illustrate the inference feasibility and the performance of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Inter-area coordination approach for metro train regulation under localized bi-directional power supply shortage during peak periods

引用

computers and Industrial Engineering 2025年 207卷

作者： Wang, Xuekai D'Ariano, Andrea Su, Shuai Tang, Tao Su, Boyi Yan, Maode School of Electronics and Control Engineering Chang'an University Xi'an700054 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome00154 Italy

Faults in the traction power supply system of metro lines frequently cause Localized Bi-directional Power Supply Shortage (LBPSS) during peak periods, significantly restricting the traction power available to trains in the under-supplied area, and have knock-on impacts on train operations in other regions. To mitigate the impacts of LBPSS on the entire line, this paper proposes an inter-area coordination approach for the train regulation problem, dividing the global problem into three sequential sub-problems. In the first stage, a cooperative control approach optimizes the control strategies in the under-supplied area, restoring the balance between power supply and demand within the power supply capacity constraints. Building on these results, the second stage reschedules the train timetable in the well-supplied areas using multiple dispatching measures. Since synchronization of entry and exit times between different regions may not be fully achieved in the first two stages, the third stage fine tures hard-to-synchronize entry/exit times, and adjusts control strategies in the under-supplied area via feedback adjustment. To address the unique challenges of each sub-problem, this paper adopts the specific algorithms: Independent Deep Q-Networks (IDQN) for cooperative control, the GUROBI solver for timetable rescheduling, and Adaptive Large-scale Neighborhood Search (ALNS) for fine-tuning synchronization. Notably, for complex constraints associated with the feedback adjustment problem, an improved ALNS algorithm is introduced, which incorporating a constraint operator to enhance convergence speed and solution quality through heuristic rules. Finally, numerical experiments based on the Beijing Metro Yizhuang Line validate the effectiveness of the proposed approach. Results indicate that the solution quality improves by up to 20.0% and 2.8% compared to two previous train regulation approaches: the conservative approach based on maximum traction power and the two-stage ap

关键词： Subways

来源：评论

学校读者我要写书评

暂无评论

NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

引用

2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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：