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GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

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Protection and control of Modern Power Systems 2024年第6期9卷 108-121页

作者： Shiwei Xia Chenhui Zhang Yahan Li Gengyin Li Linlin Ma Ning Zhou Ziqing Zhu Huan Ma the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China the State Grid Shandong Dispatch and Control Center Jinan 250003China the State Grid Shandong Electric Power Research Institute Jinan 250001China the Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China

Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series ***,a data-driven method is proposed in this study,combining graph convolution network and long short-term memory network(GCN-LSTM)to analyze the transient power angle sta-bility by exploring the spatiotemporal disturbance char-acteristics of future power systems with high penetration of renewable energy sources(wind and solar energy)and power *** key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score,whereas the network adjacency matrix is constructed according to the system network *** normalized feature quan-tities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features,reflecting changes in the network ***,the spa-tial features are inputted into the LSTM network to ob-tain the temporal features,reflecting dynamic changes in the transient power angle of the ***,the spatiotemporal features are fused through a fully con-nected network to analyze the transient power angle stability of future power systems,and the softmax activa-tion cross-entropy loss functions are used to predict the stability of the *** proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system,and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power sys-tems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algo-rithms.

关键词： Future power system spatiotemporal disturbance characteristics transient power angle stabil-ity graph convolutional network long short-term memory network.

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Cloud Architectural Design for Image-based Vehicle Positioning in Traffic Management

Cloud Architectural Design for Image-based Vehicle Positioni...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Iosif-Alin Beti Paul-Corneliu Herghelegiu Cristian-Mihai Amarandei Constantin-Florin Caruntu Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iaṣi Iasi Romania Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iaṣi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using image processing algorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.

关键词： Cloud computing Technological innovation 5G mobile communication Image processing Scalability computer architecture Reliability theory Real-time systems Servers Autonomous vehicles

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Joint Chance Constrained Optimal control via Linear Programming

arXiv

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arXiv 2024年

作者： Schmid, Niklas Fochesato, Marta Sutter, Tobias Lygeros, John Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland Department of Computer Science University of Konstanz Konstanz78457 Germany

We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may represent an invariance, reachability or reach-avoid specification that the trajectory must satisfy with a predefined probability. For finite state and action spaces, the solution is exact and our method computationally superior to approaches in the literature. For continuous state or action spaces, our linear programming formulation enables basis function approximations. © 2024, CC BY.

关键词： Linear programming

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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Joint State and Parameter Estimation Using the Partial Errors-in-Variables Principle

arXiv

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arXiv 2024年

作者： Liu, Peng Li, Kailai Hendeby, Gustaf Gustafsson, Fredrik Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands

This letter proposes a new method for joint state and parameter estimation in uncertain dynamical systems. We exploit the partial errors-in-variables (PEIV) principle and formulate a regression problem in the sense of weighted total least squares, where the uncertainty in the parameter prior is explicitly considered. Based thereon, the PEIV regression can be solved iteratively through the Kalman smoothing and the regularized least squares for estimating the state and the parameter, respectively. The simulations demonstrate improved accuracy of the proposed method compared to existing approaches, including the joint maximum a posterior-maximum likelihood, the expectation maximisation, and the augmented state extended Kalman smoother. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

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Trust Establishment for the Role-Based Collaborative Multi-Robot Systems

Trust Establishment for the Role-Based Collaborative Multi-R...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Akbari, Behzad Zhu, Haibin Pan, Ya-Jun Dalhousie University Advanced Control and Mechatronics Laboratory Department of Mechanical Engineering HalifaxNS Canada Nipissing University Collaborative Systems Laboratory Department of Computer Science and Mathematics North BayON Canada

ISBN: (纸本)9798350337020

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways. © 2023 IEEE.

关键词： Gaussian process factor graph Multi-Agent Systems (MASs) Role-based collaboration Trust management systems

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Fowler-Nordheim stress-induced degradation in n-channel MOSFETs 7

Fowler-Nordheim stress-induced degradation in n-channel MOSF...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Chukhraev, Igor V. Mastykash, Dmitry V. Drach, Vladimir E. Bauman Moscow State Technical University Computer Science and Control Department Moscow Russian Federation Sochi State University Department of Innovative Engineering and Digital Technologies Sochi Russian Federation

ISBN: (纸本)9798331531836

This study investigates the degradation mechanisms of gate oxide in n-channel MOSFETs subjected to Fowler-Nordheim constant current stress to introduce uniformly distributed defects. Different techniques were used to characterise the impact of stress-induced defects on the electrical properties of MOSFETs. The findings reveal distinct degradation behaviors, with early-stage negative shifts in threshold voltage attributed to hole trapping, transitioning to positive shifts due to interface state generation at higher electron fluence. The study also emphasises the factors that influence device performance. The results enhance our understanding of gate oxide reliability under high-field stress, as well as the mechanisms of defect creation and charge trapping in MOSFETs. © 2025 IEEE.

关键词： Fowler-Nordheim stress gate dielectric MOSFET threshold voltage

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning 3

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Tian, Jilun Zhang, Jiusi Jiang, Yuchen Luo, Hao Huang, Congsheng Chow, Mo-Yuen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350324754

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks. © 2023 IEEE.

关键词： Long short-term memory

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