检索结果-内蒙古大学图书馆

Optimal control of linear Gaussian quantum systems via quantum learning control

Physical Review A 2024年第6期109卷 063508-063508页

作者： Yu-Hong Liu Yexiong Zeng Qing-Shou Tan Daoyi Dong Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Quantum Computing Center Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics School of Engineering and Information Technology Department of Physics Institute of Interdisciplinary Studies

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems.

关键词： Optomechanics Quantum optics

来源：评论

学校读者我要写书评

暂无评论

Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

引用

China CDC weekly 2023年第4期5卷 71-75,I0001-I0003页

作者： Zhanwei Du Xiao Zhang Lin Wang Sidan Yao Yuan Bai Qi Tan Xiaoke Xu Sen Pei Jingyi Xiao Tim K.Tsang Qiuyan Liao Eric H.YLau Peng Wu Chao Gao Benjamin J.Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkNew TerritoriesHong Kong Special Administrative RegionChina Department of Genetics University of CambridgeCambridgeCB23EHUK Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore College of Information and Communication Engineering Dalian Minzu UniversityDalian CityLiaoning ProvinceChina Department of Environmental Health Sciences Mailman School of Public HealthColumbia UniversityNew York CityNYUSA School of Artificial Intelligence Opticsand Electronics(iOpen)Northwestern Polytechnical UniversityXi’an CityShaanxi ProvinceChina

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and... 详细信息

关键词： collective likely extreme

来源：评论

学校读者我要写书评

暂无评论

Application of Extreme Learning Machine for Shunt Faults Detection and Classification in Three-phase Transmission Line Systems

Application of Extreme Learning Machine for Shunt Faults Det...

引用

Signal, control and Communication (SCC), International Conference on

作者： Khaoula Assadi Jihane Ben Slimane Salah Salhi University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis Tunisia Department of Computer Sciences Faculty of Computing and InformationTechnology Northern Border University Rafha Saudi Arabia

ISBN: (数字)9798350326390

ISBN: (纸本)9798350326406

The electrical power transmission lines play a crucial role in maintaining a continuous electricity supply. However, the exposed environment of these lines increases the risks of faults occurrence, necessitating prompt detection, and classification, to eliminate them within a specified time. In light of this, the present study introduces a methodology for the detection and classification of faults in a three-phase transmission line system employing the Extreme Learning Machine (ELM). A 220 kV, 300 km three-phase transmission line system was simulated in MATLAB/Simulink software, where the current and voltage signals were generated under different fault conditions including fault types, fault inception time, and fault resistance. The fundamental components of these signals were used as inputs in the ELM model. Different shunt fault types, including line-to-ground, line-to-line, double-line-to-ground, triple-line, and triple-line-to-ground were applied for fault detection and classification framework. Furthermore, the optimum number of hidden neurons in the ELM model was investigated in this study. In terms of the Mean Squared Error (MSE) and correlation coefficient, four ELM variants based on Sine, Sigmoid, Radial Basis, and Triangular Basis transfer functions were assessed for each module. The outcome of these metrics indicates that the ELM with the sigmoid model demonstrated superior performance compared to the other ELM models. Based on statistical metrics MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), and also the computational time, the efficiency of ELM-Sigmoid and state-of-the-art methods of Multi-Layer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) were compared and analyzed during training and testing phases. The results obtained revealed that the ELM-Sigmoid model outperforms the other models in the detection and classification of shunt faults with significant accuracy and fast computational speed.

关键词： Measurement Training Power transmission lines Extreme learning machines Computational modeling Fault detection Neurons

来源：评论

学校读者我要写书评

暂无评论

Improved Fireworks Optimization Algorithm for Multi-product Human-robot Collaborative Hybrid Disassembly Line Balancing

Improved Fireworks Optimization Algorithm for Multi-product ...

引用

IEEE International Conference on Human-Machine Systems

作者： Feng Guo Xiwang Guo Jiacun Wang Weitian Wang Jinrui Cao Imane Bigaume Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA School of Computing Montclair State University Montclair USA Computer Science Department New Jersey City University Jersey City USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model’s validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem.

关键词： Accuracy Human-machine systems Collaboration Programming Stability analysis Whale optimization algorithms Recycling

来源：评论

学校读者我要写书评

暂无评论

Intellectual lidar-based object classification for V2V communication technology implementation 22

Intellectual lidar-based object classification for V2V commu...

引用

Proceedings of the 6th International Conference on Future Networks & Distributed Systems

作者： Ansaf Abdulnagimov Ekaterina Lopukhova Gleb Alektorov Nail Klyavlin Department of Automated Control Systems Ufa University of Science and Technology Russian Federation Department of Telecommunication Systems Ufa University of Science and Technology Russian Federation Department of Computational Mathematics and Engineering Cybernetics Ufa University of Science and Technology Russian Federation Department of High Performance Computing Systems and Technology Ufa University of Science and Technology Russian Federation

ISBN: (纸本)9781450399050

The deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. The robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. The features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. The best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.

关键词： PointNet

来源：评论

学校读者我要写书评

暂无评论

A Modified P&O-MPPT Technique Using Fuzzy Logic controller for PV Systems

A Modified P&O-MPPT Technique Using Fuzzy Logic Controller f...

引用

Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Rafah Ibraheem Jabbar Karam Khairullah Mohammed Saad Mekhilef Marizan Mubin Mehdi Seyedmahmoudian Alex Stojcevski Ben Horan Mohamed Becherif Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia Mechatronics Engineering Department Universiti Teknologi Malaysia Control Johor Bahru Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia School of Engineering Deakin University Geelong VIC Australia FEMTO-ST Institute Univ. Bourgogne Franche- Comté UTBM CNRS Rue Ernest Thierry Mieg Belfort France

The perturb and observe (P&O) algorithm is the most popular of the numerous maximum power point tracking (MPPT) algorithms due to its simplicity and ease of implementation. However, the method has limitations regarding convergence speed, steady-state oscillation, and drift problem under sudden irradiance variations. This paper presents an improved P&O-MPPT algorithm to enhance the tracking convergence speed using adaptive duty cycle perturbation step fuzzy logic (FL-∆D) size. Moreover, a new strategy is added to the algorithm to stop the steady-state oscillation once MPP is tracked. The proposed algorithm was verified using MATLAB/Simulink under constant and varied irradiance levels. The results showed that the proposed algorithm with an adaptive FL-∆D size could efficiently track an accurate MPP with zero-state oscillation at different irradiance levels. Moreover, the algorithm achieves fast tracking under a rapid change in irradiance. The proposed algorithm was compared with the traditional P&O algorithm and the modified P&O algorithms that have recently been published. The comparison results validated the effectiveness of the proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv

引用

arXiv 2023年

作者： Xiao, Jiaren Dai, Quanyu Shen, Xiao Xie, Xiaochen Dai, Jing Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Hainan University Haikou China Department of Automation Harbin Institute of Technology Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph Semi-supervised domain adaptation with Graph Contrastive Learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph’s local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. © 2023, CC BY-NC-ND.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Defect Detection Algorithm for Solar Panels

Deep Learning based Defect Detection Algorithm for Solar Pan...

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiaqi Li Hongxu Li Yifan Wu Hailiang Zhou Luigi Manfredi Peng Li Hong Zhang Department of Key Lab of Industrial Computer Control Engineering Hebei Province Yanshan University Qinhuangdao China Department of Computing Science University of Alberta Edmonton AB Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. Department of Institute for Medical Science and Technology (IMSaT) University of Dundee United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

引用

SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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：