检索结果-内蒙古大学图书馆

Frequency stabilization of a sophisticated multi-area interconnected hybrid power system considering non-inertia sources

学校读者我要写书评

暂无评论

International Journal of Electrical Power and Energy systems 2025年 169卷

作者： Gawad, Diaa M. Magdy, Gaber Ebrahim, M.A. Petlenkov, Eduard Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Cairo Egypt National Energy Control Center Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan81528 Egypt Faculty of Engineering King Salman International University El-Tor46511 Egypt Centre for Intelligent Systems Department of Computer Systems Tallinn University of Technology Tallinn19086 Estonia

This study introduces a novel interconnected power system model, which combines four real areas, integrating conventional power plants with renewable energy sources (RESs). This comprehensive system model aims to address the load frequency control (LFC) challenges that such systems face. Where the integration of RESs into power systems poses significant challenges, particularly in maintaining frequency stability. The LFC approach utilizes a proportional-integral-derivative (PID) controller, meticulously optimized through the Runge-Kutta (RUN) optimizer based on its mathematical principles. Dealing with the uncertainties of RESs and system nonlinearities poses a significant challenge for this controller. The efficacy of the proposed PID controller based on the RUN algorithm is examined by analyzing the frequency stability of the Egyptian power system (EPS), which stays interconnected with neighboring grids in Jordan, Libya, and Sudan. This study accounts for various loading scenarios and system nonlinearities to validate the algorithm's effectiveness. Furthermore, the superior performance of the RUN optimizer is verified by contrasting its results with other widely recognized optimization techniques, such as the Ant Lion optimizer (ALO) and Chernobyl disaster optimizer (CDO). Additionally, the PID controller based on the RUN optimizer is also evaluated against literature that employed a moth swarm algorithm (MSA) to design the PID controller-based LFC in the EPS. Simulation results in the MATLAB environment demonstrate the effectiveness and robustness of the proposed PID controller based on the RUN algorithm. The RUN-based PID controller outperforms other PID controllers optimized using algorithms from the literature (e.g., ALO, CDO, and MSA), achieving superior performance across various contingencies, including load variations, RES uncertainties, and system nonlinearities. The results highlight the effectiveness of the proposed controller compared to those in the l

关键词： Electric power system interconnection

Distributed Prescribed-Time Observer for Nonlinear systems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Bocus, Mohammud J. Häkkinen, Juha Fontes, Hélder Drzewiecki, Marcin Qiu, Senhui Eder, Kerstin Piechocki, Robert School of Computer Science University of Bristol Bristol United Kingdom Circuits and Systems Research Unit University of Oulu Oulu Finland MpicoSys Embedded Pico Systems Sp. z o. o. Gdynia Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk Poland INESC TEC Porto Portugal Faculty of Engineering University of Porto Porto Portugal

his paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations. © 2025, CC BY.

关键词： Digital storage

Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

学校读者我要写书评

暂无评论

IEEE Transactions on control of Network systems 2025年

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

Leader-Follower Formation Tracking control of Quadrotor UAVs Using Bearing Measurements

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Doodeman, Sander Tang, Zhiqi Jacinto, Marcelo Cunha, Rita Silvestre, Carlos Department of Mechanical Engineering TU/e Eindhoven University of Technology Eindhoven Netherlands Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Instituto Superior Técnico University of Lisbon Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

This work addresses the practical problem of distributed formation tracking control of a group of quadrotor vehicles in a relaxed sensing graph topology with a very limited sensor set, where only one leader vehicle can access the global position. Other vehicles in the formation are assumed to only have access to inter-agent bearing (direction) measurements and relative velocities with respect to their neighbor agents. A hierarchical control architecture is adopted for each quadrotor, combining a high-gain attitude inner-loop and an outer-loop bearing-based formation controller with collision avoidance augmentation. The proposed method enables a group of quadrotors to track arbitrary bearing persistently exciting desired formations, including time-varying shapes and rotational maneuvers, such that each quadrotor only requires relative measurements to at least one neighboring quadrotor. The effective performance of the control strategy is validated by numerical simulations in MATLAB and real-world experiments with three quadrotors. © 2025, CC BY-NC-ND.

关键词： Unmanned aerial vehicles (UAV)

UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

关键词：

Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

学校读者我要写书评

暂无评论

Alexandria Engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

学校读者我要写书评

暂无评论

Knowledge-Based systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search