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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Chala, Olena Bronnikov, Artem Igor, Nevliudov Mospan, Denis Kharkiv National University of Radio Electronic Department of Computer-Integrated Technologies Automation and Mechatronic Kharkiv Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk Ukraine

ISBN: (纸本)9798350346831

This work is devoted to the actual topic of neural networks using for the technological objects recognition tasks in computer-integrated manufacturing. In the work, a study for the transport technological objects recognition using a neural network was carried out. This study contains a neural network without and with the convolutional layers usage in architecture, as well as experiments on neural networks training depends on the number of iterations. The practical implementation of this work is the possibility of the developed software usage on portable mobile devices, as well as training on remote servers, which makes it possible to use this software even on devices with minimal technical requirements. © 2022 IEEE.

关键词： computer vision

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AgentMixer: Multi-Agent Correlated Policy Factorization 39

AgentMixer: Multi-Agent Correlated Policy Factorization

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zhiyuan Zhao, Wenshuai Wu, Lijun Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)157735897X

In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an asymmetric learning failure due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a strategy modification called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce Individual-Global-Consistency to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an ϵ-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Controller Synthesis by a Polynomial Method in Order to Achieve Minimal Overshoot for Systems with a Delay

Controller Synthesis by a Polynomial Method in Order to Achi...

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2022 Conference of Russian Young Researchers in Electrical and Electronic engineering, ElConRus 2022

作者： Voevoda, Aleksandr A. Shipagin, Victor I. Troshina, Galina V. Novosibirsk State Technical University Nstu Department of Automation Novosibirsk Russia Novosibirsk State Technical University Nstu Department of Computer Engineering Novosibirsk Russia

ISBN: (纸本)9781665409933

The single-channel technical system containing a delay channel is considered. To synthesize the automatic control system, the method of polynomial decomposition of the system is used. In this case, the delay block is approximated by the Pade series. To eliminate the negative influence of this link on the system, the poles of the transfer function of the delay link are added to the desired characteristic polynomial of the closed system. The resulting system is static, but the level of system overshoot can be significant. In a real technical system, such a quality of management may be economically unprofitable or even unacceptable. This article suggests methods and techniques for improving the quality of system management and, in particular, reducing excessive controlling. © 2022 IEEE.

关键词： Polynomial approximation

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Ultimately Bounded State Estimation for Discrete-Time Singularly Perturbed Complex Networks Under Bit Rate Constraints 28

Ultimately Bounded State Estimation for Discrete-Time Singul...

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28th International Conference on automation and Computing, ICAC 2023

作者： Guo, Yuru Wang, Zidong Wang, Yezheng Xu, Yong School of Automation Guangdong University of Technology Guangzhou510006 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350335859

In this paper, the ultimately bounded state estimation problem is investigated for discrete-time singularly perturbed complex networks (SPCNs) under bit rate constraints. The measurement outputs of the considered SPCNs are transmitted to the remote estimator through a bandwidth-limited wireless network where a coding-decoding mechanism is employed. A state estimator is developed by fully considering the effects of the constrained bit rate to achieve the desired estimation performance index. The boundedness of the estimation error dynamics is analyzed with the help of the constructed Lyapunov-like functional candidate. Finally, a simulation example is used to verify the correctness of the obtained theoretical results. © 2023 IEEE.

关键词： Complex networks

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PLUG-AND-PLAY: AN EFFICIENT POST-TRAINING PRUNING METHOD FOR LARGE LANGUAGE MODELS 12

PLUG-AND-PLAY: AN EFFICIENT POST-TRAINING PRUNING METHOD FOR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Yingtao Bai, Haoli Lin, Haokun Zhao, Jialin Hou, Lu Cannistraci, Carlo Vittorio Center for Complex Network Intelligence Tsinghua Laboratory of Brain and Intelligence China Department of Computer Science Tsinghua University China Department of Biomedical Engineering Tsinghua University China Huawei Noah's Ark Lab Canada Institute of Automation Chinese Academy of Sciences China

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs;and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speedup on specific hardware. Our code is available at: https://***/biomedicalcybernetics/Relative-importance-and-activation-pruning. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Chemical activation

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Towards Atomic Defect-Aware Physical Design of Silicon Dangling Bond Logic on the H -Si (100)-2× 1 Surface

Towards Atomic Defect-Aware Physical Design of Silicon Dangl...

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2024 Design, automation and Test in Europe Conference and Exhibition, DATE 2024

作者： Walter, Marcel Croshaw, Jeremiah Hang Ng, Samuel Sze Walus, Konrad Wolkow, Robert Wille, Robert Technical University Of Munich Design Automation Germany University Of Alberta Department Of Physics Edmonton Canada Quantum Silicon Inc. Edmonton Canada University Of British Columbia Department Of Electrical And Computer Engineering Vancouver Canada Software Competence Center Hagenberg GmbH Austria

ISBN: (纸本)9798350348590

Recent advancements in Silicon Dangling Bond (SiDB) fabrication have transitioned from manual to automated processes. However, sub-nanometer substrate defects remain a significant challenge, thus preventing the fabrication of functional logic. Current design automation techniques lack defect-aware strategies. This paper introduces an idea for a surface defect model based on experimentally verified defects, which can be applied to enhance the robustness of established gate libraries. Additionally, a prototypical automatic placement and routing algorithm is presented, utilizing STM data from physical experiments to obtain dot- accurate circuitry resilient to atomic surface defects. Initial evaluations on surfaces with varying defect rates demonstrate their critical impact, suggesting that fabrication processes must achieve defect rates of around 0.1 % to further advance this circuit technology. © 2024 EDAA.

关键词： Surface defects

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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SR-Mamba: Data-Driven EV Charging Load-Altering Attack Detection and Localization Using Selective State based Model

SR-Mamba: Data-Driven EV Charging Load-Altering Attack Detec...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Yi Li Umara Hanif Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore Department of Automation Central South University Changsha China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Driven by the imperative to reduce emissions from transportation, Electric Vehicles (EVs) are proliferating rapidly. However, the widespread adoption of EV charging facilities poses a potential threat to the stability of the grid. Manipulating the demand for charging facilities can disrupt the supply/demand balance of the grid, potentially leading to blackouts. Moreover, current load alternation attack detection relies on static linearized grid modeling, which struggles to accurately represent the dynamic nature of the grid, thus making it difficult to detect such attacks effectively. In this paper, we propose a data-driven model using a State Space Model (Mamba-based structure) to capture temporal correlation among buses. We introduce spectral domain representation to enhance detection and utilize hidden grid representations for identifying load alternative attacks and their location.

关键词： Robust control Reactive power Power system dynamics Transportation Power system stability Electric vehicle charging Stability analysis Vehicle dynamics Spectral analysis Load modeling

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Convolution-Transformer for Image Feature Extraction

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computer Modeling in engineering & Sciences 2024年第10期141卷 87-106页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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Assessing the Suitability of Software Tools for System-Theoretic Process Analysis of Nuclear Instrumentation and Control Systems

Assessing the Suitability of Software Tools for System-Theor...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Akira King Polina Ovsiannikova Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Modernization of currently operational nuclear power plants is becoming increasingly important to maintain their performance and safety. Ensuring the safety of newer Instrumentation and Control (I&C) systems used in modernization efforts requires hazard analysis techniques suitable for the analysis of complex and software-heavy systems. System-Theoretic Process Analysis (STPA) has proven to be a suitable hazard analysis method for these complex I&C systems, however, its practical use is still often limited by its labor-intensive and time-consuming nature, partially due to the limitations of the tools used to perform the analysis: common Office tools such as Microsoft Excel or Visio. Conducting an STPA analysis could be simpler and more attractive with software tools specific to the method. This work introduces the requirements for these software tools and lays the foundation for further work, in which software tools will be evaluated against these requirements.

关键词： Instruments Process control Control systems Hazards Spreadsheet programs Software tools Power generation Manufacturing automation

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