检索结果-内蒙古大学图书馆

Power Engineering Conference (SAUPEC), Southern African Universities

作者： Joniff Wellen Senthil Krishnamurthy Mukovhe Ratshitanga Department of Electrical Electronics and Computer Engineering Centre for Substation Automation and Energy Management Systems (CSAEMS) Centre for Intelligent Systems and Emerging Technologies (CSIET) Cape Peninsula University of Technology Bellville South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

Voltage regulation of power transformers is critical to controlling and maintaining system voltage levels in the electrical transmission and distribution network system. It is vital to keep the transmission and distribution network's voltage levels within a specified range and to avoid voltage levels from fluctuating outside of the predetermined range of allowed tolerance levels. Two SEL-2414 IEDs were employed in this study to perform the voltage control function of the parallel power transformers using a master-follower control scheme. The automatic tap changers that execute this voltage regulating function incorporate the IEC61850 GOOSE message application as the communication protocol. The paper demonstrates the control function, a hardware-in-the-loop simulation in a closed-loop simulation using Real Time Digital Simulator (RTDS), and the two transformer tap changer IEDs SEL-2414 were utilized for lab-scale implementation and validating the simulation results.

关键词： Substations Protocols Hardware-in-the-loop simulation Reliability engineering Real-time systems Power transformers Tap changers Voltage control Protection Remote monitoring

来源：评论

学校读者我要写书评

暂无评论

Distance Protection Application for 33kV Traction Network

Distance Protection Application for 33kV Traction Network

引用

Power Engineering Conference (SAUPEC), Southern African Universities

作者： Luthando Nkalitshana Senthil Krishnamurthy Haltor Mataifa Department of Electrical Electronics and Computer Engineering Centre for Substation Automation and Energy Management Systems (CSAEMS) Center for Intelligent Systems and Emerging Technologies (CSIET) Cape Peninsula University of Technology Bellville South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The research investigates the laboratory-scale setup and functional testing of distance protection techniques for the Transnet traction system. The Transnet network, which powers South Africa's railway system, requires a strong protection mechanism to assure the safety and dependability of the electrical systems that power locomotives. Distance protection, essential in securing high-voltage transmission networks, is critical to detecting faults, isolating affected portions, and minimizing system interruptions. In this study, a simulation environment was created to simulate the features of the Transnet traction system, including traction substations, overhead power lines, and associated equipment. A lab-scale model was developed utilizing protection functional simulation to simulate real-time fault situations and evaluate the distance protection relay's response under different fault conditions. The performance and efficacy of the protection mechanism were assessed by varying key factors such as fault impedance, fault location, and fault types.

关键词： Rails Substations Simulation Reliability engineering Stability analysis Software Real-time systems Protection Load flow Testing

来源：评论

学校读者我要写书评

暂无评论

Experimental implementation of chaos-based video encryption using visible light communication technology

引用

Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Unmanned aerial vehicle based multi-person detection via deep neural network models

引用

Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge systems with Heterogeneous CPUs and GPUs

引用

IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

引用

Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Domain Adaptation With Synchronized Self-Training for Cross-Domain Motor Imagery Recognition

引用

IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3664-3677页

作者： Chen, Peiyin Liu, Xiaofeng Ma, Chao Wang, He Yang, Xiong Grebogi, Celso Gu, Xiao Gao, Zhongke Hohai University College of Artificial Intelligence and Automation Changzhou213200 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China National University of Singapore Institute for Health Innovation and Technology Department of Electrical and Computer Engineering 119077 Singapore University of Aberdeen Institute for Complex Systems and Mathematical Biology King's College AberdeenAB24 3UE United Kingdom University of Oxford Computational Health Informatics Laboratory Institute of Biomedical Engineering OxfordOX3 7DQ United Kingdom

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance. © 2013 IEEE.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

On Hyper-Stability Theory Based Multivariable Nonlinear Adaptive Control: Experimental Validation on Induction Motors

引用

IET Electric Power Applications 2025年第1期19卷

作者： Bekhiti, Belkacem Nail, Bachir Tibermacine, Imad Eddine Salim, Ramzi Blida Algeria Faculty of Sciences and Technology University of Ziane Achour Djelfa Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratoire d’Automatique et Analysis des Systems National Polytechnic School Oran Algeria

This paper presents a novel hyperstability-based adaptive control strategy for induction motors, distinguishing itself from conventional model reference adaptive control (MRAC) approaches by integrating enhanced robustness against parametric uncertainties and external disturbances. Unlike traditional adaptive controllers, the proposed Hyper-stable adaptive controller (H-MRAC) ensures improved transient performance and faster convergence rates, validated through both theoretical analysis and experimental verification. Key innovations include the integration of hyper-stability theory into adaptive control design and a comprehensive evaluation of parameter uncertainties, which significantly improves motor performance in variable conditions. Experimental results demonstrate a 42% reduction in integral squared error (ISE), a 37% improvement in integral absolute error (IAE), a 28.7% improvement in integral time absolute error (ITAE) and a 25% faster convergence compared to standard MRAC. A detailed comparison with passivity-based control strategies shows a 26.5% improvement in steady-state performance and 30% faster transient response. Despite these successes, the paper discusses limitations related to computational complexity, real-time implementation challenges, and the impact of sensor noise on control performance. The potential need for DSPs or FPGA-based solutions is also addressed. Finally, the generalisability of the proposed control method across different motor types and power ratings is considered with future directions for broader validation in diverse industrial scenarios. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

On robust learning of memory attractors with noisy deep associative memory networks

引用

Neural Networks 2025年 188卷 107474页

作者： Rao, Xuan Zhao, Bo Liu, Derong School of Systems Science Beijing Normal University Beijing100875 China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518000 China Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States

Developing the computational mechanism for memory systems is a long-standing focus in machine learning and neuroscience. Recent studies have shown that overparameterized autoencoders (OAEs) implement associative memory (AM) by encoding training data as attractors. However, the learning of memory attractors requires that the norms of all eigenvalues of the input–output Jacobian matrix are strictly less than one. Motivated by the observed strong negative correlation between the attractor robustness and the largest singular value of the Jacobian matrix, we develop the noisy overparameterized autoencoders (NOAEs) for learning robust attractors by injecting random noises into their inputs during the training procedure. Theoretical demonstrations show that the training objective of the NOAE approximately minimizes the upper bound of the weighted sum of the reconstruction error and the square of the largest singular value. Extensive experiments in terms of numerical and image-based datasets show that NOAEs not only increase the success rate of the training samples becoming attractors, but also improve the attractor robustness. Codes are available at https://***/RaoXuan-1998/neural-netowrk-journal-NOAE. © 2025

关键词： Associative storage

来源：评论

学校读者我要写书评

暂无评论

A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

引用

IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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：