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arXiv 2025年

作者： Wang, Xuezao Xu, Yijun Gu, Wei Liu, Kai Lu, Shuai Korkali, Mert Mili, Lamine School of Electrical Engineering Southeast University Jiangsu Nanjing210096 China Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States Bradley Department of Electrical and Computer Engineering Virginia Tech Northern Virginia Center Falls ChurchVA22043 United States

The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs). This penetration threatens the dynamic stability of the modern power system. To assess the latter, we resort to the critical clearing time (CCT) as a stability index, which is typically computed through a large number of time-domain simulations. This is especially true for CIG-embedded power systems, where the complexity of the model is further increased. To alleviate the computing burden, we developed a trajectory sensitivity-based method for assessing the CCT in power systems with synchronous and asynchronous generators. This allows us to obtain the CCT cost-effectively. The simulation results reveal the excellent performance of the proposed method. © 2025, CC BY-NC-ND.

关键词： Asynchronous generators

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Fast Multi-Class Vehicle Cooperative Path Optimization in Complex Urban V2X Transportation: A Novel Parallel Multi-Agent Reinforcement Learning Approach

Fast Multi-Class Vehicle Cooperative Path Optimization in Co...

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IEEE Symposium on Intelligent Vehicle

作者： Shitao Chen Shuyang Cai Ziheng Tang Donghe Li Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Urban road traffic systems are advancing into sophisticated networks, underscoring the importance of real-time collaborative decision-making. This study tackles the intricate challenge of cooperative path planning under complex urban conditions, taking into account a variety of vehicle types and their respective priorities. While conventional path planning techniques struggle with such intricate coordination, reinforcement learning, though theoretically capable, is hindered by its limited model reusability and protracted training times. To address these issues, we present a novel parallel multi-agent reinforcement learning strategy for path planning that is adaptable to various vehicle types. The problem is initially cast as a multi-agent Markov Decision Process (MDP), followed by the introduction of a parallel training approach within the Q-learning framework. This approach leverages tensor computation to transform the Q-table, state, and reward, thereby markedly accelerating the training process. Empirical simulations demonstrate the approach’s efficacy, achieving a 0.84% reduction in training time (from approximately 771.611 seconds to 0.654 seconds), achieving a 93.94% lower probability of path overlap though the total distance increased by 7.69%.

关键词： Training Adaptation models Roads Decision making Transportation Transforms Path planning

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One-Hot Coding Similarity of Vehicle Routing Particle Swarm Optimizer 8th

One-Hot Coding Similarity of Vehicle Routing Particle Swarm ...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： He, CHao Li, Zhi Su, Shoubao Xu, Liukai Wang, Chishe School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China Provincial Engineering Research Center of Digital Technology for Industrial Economic Jinling Institute of Technology Nanjing211169 China School of Computer Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9783031067662

Aiming at the real-time problem of vehicle routing planning, an improved particle swarm optimization method, called AVSPSO, is proposed in this paper. In the method, the path similarity of vehicle routing is calculated by adopting based on the One-Hot coding vector and by improving the constraint processing through the combination of coding mode and fixed penalty, in which dynamically adjust the inertia weight through the evolution state is to balance the ability of local mining and global development of particles. Experimental results on two practical cases of vehicle routing optimization problems and comparisons with other methods have shown that the proposed method is feasible and stable with better performances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

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Tailoring selectivity and efficiency: pyrazolyl-1H-1,2,4-triazole MCM-41 and silica hybrid materials for efficient cadmium(II) removal from water

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Environmental science and Pollution Research 2025年第17期32卷 10984-11003页

作者： Draoui, Youssef Radi, Smaail El Mahdaoui, Amal El Massaoudi, Mohamed Rotaru, Aurelian Garcia, Yann do Amparo F. Faustino, Maria da Graça P. M. S. Neves, Maria Moura, Nuno M. M. LCAE Department of Chemistry Faculty of Science University Mohamed I P.O. Box 524 Oujda60 000 Morocco Department of Electrical Engineering and Computer Science & Research Center Mansid "Stefan Cel Mare" University University Street No. 13 Suceava720229 Romania Université Catholique de Louvain Place L. Pasteur 1 Louvain-La-Neuve1348 Belgium LAQV-REQUIMTE Department of Chemistry University of Aveiro Aveiro3810-193 Portugal Higher School of Education and Training University Mohamed I P.O. Box 410 Oujda60000 Morocco

Two novel silica-based hybrid materials, M1 and M2, based on silica gel and MCM-41 with a ditopic triazole-pyrazole ligand grafted onto their surfaces, respectively, were successfully synthesized and fully characterized. The adsorption capacity of these organic–inorganic hybrid materials was evaluated for copper(II), cadmium(II), and lead(II) ions. Experimental parameters including solution pH, contact time, temperature, and adsorbate concentration were systematically investigated. The obtained experimental data were analyzed using diverse adsorption isotherms and models to assess and interpret the behavior of the adsorbents. Notably, the newly synthesized materials exhibited exceptional selectivity, ultra-rapid adsorption rates within the first few minutes, and high removal efficiencies of 81.40 and 121.26 mg/g for M1 and M2, respectively, towards Cd(II). Moreover, reusability assessments demonstrated excellent consistency, with only marginal decreases in adsorption capacities of less than 8% observed over five consecutive cycles. Interestingly, the application of M1 and M2 for the extraction of transition metals from real contaminated river water, from Nador city in Morocco, showcased their effectiveness in removing heavy metal ions even at low concentrations, with M2 achieving up to 67% cadmium removal. These findings highlight the potential of both materials as reliable systems for heavy metal removal in practical environmental remediation applications, with material M2 demonstrating superior performance over M1. © The Author(s) 2025.

关键词： Silica gel

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DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration

DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral M...

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International Conference on computer Vision (ICCV)

作者： Yuchun Miao Lefei Zhang Liangpei Zhang Dacheng Tao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Sydney AI Centre School of Computer Science The University of Sydney

Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (DDS2M), which works by inferring the parameters of the proposed Variational Spatio-Spectral Module (VS2M) during the reverse diffusion process, solely using the degraded HSI without any extra training data. In VS2M, a variational inference-based loss function is customized to enable the untrained spatial and spectral networks to learn the posterior distribution, which serves as the transitions of the sampling chain to help reverse the diffusion process. Benefiting from its self-supervised nature and the diffusion process, DDS2M enjoys stronger generalization ability to various HSIs compared to existing diffusion-based methods and superior robustness to noise compared to existing HSI restoration methods. Extensive experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate DDS2M’s superiority over the existing task-specific state-of-the-arts. Code is available at: https://***/miaoyuchun/DDS2M.

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E-Binar application design: A digital-based essay evaluation tool in Indonesian language 7

E-Binar application design: A digital-based essay evaluation...

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7th International Conference on Education and Technology, ICET 2021

作者： Annisa, Robita Ika Febrianti, Lifia Yola Ningsih, Rahmi Yulia Language Center Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Binus Nusantara University Language Center Industrial Engineering !!!Faculty of Engineering Jakarta Indonesia Bina Nusantara University Language Center Primary Teacher Education !!!Faculty of Humanity Jakarta Indonesia

ISBN: (纸本)9781665427814

This study aims to develop an E-Binar stands for 'Elektronik-Bahasa Indonesia yang Benar'. E-Binar is an application designed as an evaluation tool for writing essays in Indonesian for college students. The research method used is the Research and Development (RD) with the ADDIE model. However, the stages in this research have only reached the design stage. The results of this study explain that the design of the E-Binar application as a digital-based Indonesian essay evaluation tool is adapted from the Project Essay GraderTM (PEG), Intelligent Essay AssessorTM (IEA), Bayesian Essay Test Scoring System™ (BETSY) model, and E-Rater. The features that can be adapted from the four models are used in essay assessment with ten assessment criteria: (a) express ideas clearly and effectively;(b) compose original essays;(c) develop an introduction, content, and conclusion based on the topic;(d) ideas with a logical line of reasoning;(e) present factual data;(f) compose essays with coherent paragraphs;(g) compose essays with effective sentences;(h) compose essays with appropriate vocabulary, diction, and terms;and (i) compose essays with good and correct Indonesian spelling;(j) compose an essay in 300 - 600 words. © 2021 IEEE.

关键词： Students

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiong Wang Yuxin Chen Yuqing Li Xiaofei Liao Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients’ selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal ${{\mathcal{O}}}\left({{T^{ - 2/3}}}\right)$ convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques.

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GeoPet: Interactive Prompt engineering for Enhancing Tool Calling of Large Language Models in Geospatial Tasks

GeoPet: Interactive Prompt Engineering for Enhancing Tool Ca...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Xuan Guo Chenchen Gao Weifan Niu Xinzong Wei Chengqi Hua Junnan Liu Mingliang Xu School of Computer and Arifcial intelligence Zhengzhou University Zhengzhou China Engineering Research Center of intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Earth Science and Technology Zhengzhou University Zhengzhou China

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Geospatial tasks often require the coordination of various spatial algorithms and operations, which are usually performed through tool calling guided by natural language prompts. Crafting effective prompts is challenging due to the inherent complexity and ambiguity of natural language. In this paper, we present GeoPet, a visual analytics system designed to simplify the process of prompt engineering to improve the performance of geospatial tool callings on large language models (LLMs). At its core, GeoPet is a sophisticated tool recommendation method that accepts geospatial tasks as input, decomposes them into atomic tasks, identifies relevant tools, and extracts the most relevant tool descriptions for these atomic tasks. The system is designed to support interactive prompt engineering of geospatial tool invocations, enabling users to explore the connections between geospatial tasks and established tools and to evaluate their performance. This enables crafting and refining prompts that coordinate LLMs with human expertise. GeoPet's satisfaction, practicality, usability, and visual design are validated through two case studies and a user study. These demonstrate that the system significantly eases the burden of rapid engineering and skillfully guides LLMs in geospatial tool calling capabilities. By providing a visual and interactive system for prompt engineering, GeoPet helps users navigate complex geospatial tasks and improves the overall efficiency and accuracy of tool calling for LLMs.

关键词： Navigation Large language models Visual analytics Interactive systems Natural languages Refining Geospatial analysis Complexity theory Prompt engineering Usability

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CRFs for Digital Signature and NIZK Proof System in Web Services 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Kang, Burong Zhang, Lei Yang, Yafang Meng, Xinyu Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan610041 China Research Institute of China Telecom Corporation Limited Shanghai200062 China School of Computer Science Fudan University Shanghai200433 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Web services are service-oriented computing technology which allows computers running different operating domains to access and share each other’s databases. Each web service is an application (like online business) which may require the private information of users. Thus, it will be important to preserve these web users’ individual privacy. The traditional approaches to achieve this goal in web security is to use the cryptographic technologies, such as digital signature, NIZK proof system. Whereas, some recent research results indicate that these cryptographic technologies may suffer from the algorithm substitution attack (ASA). ASA means that the cryptographic technology would be embedded some backdoor in the process of its implementation by the attacker, and with the backdoor information the attacker can steal the user’s private information. To address this problem, the concept of cryptographic reverse firewall (CRF) has been introduced, which could sanitize the messages inputting and outputting the user’s computer. In this paper, we construct the CRFs for the efficient Pointcheval-Sanders (PS) signature as well as the NIZK proof system. © 2023, Springer Nature Switzerland AG.

关键词： Web services

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