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9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Wei Bao, Chunhui Chen, Tianxiang Zhuang, Yi Jiangsu Automation Research Institute Embedded Computer System Architecture Lianyungang China Nanjing University of Aeronautics and Astronautics Faculty of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350317251

In response to the challenge of comprehensively security assessment and analysis in existing mobile networks from both upper-layer services and network transmission levels, A SDN-based security assessment model for transmission and service security in mobile networks (SAMTS) is proposed. This model introduces security assessment algorithms, in which static configuration information and dynamic abnormal information are considered comprehensively to judge the trend and the risk degree of attack when evaluating the security of network nodes. Meanwhile, the node importance of service orientation and transmission is integrated for security evaluation. The effectiveness of the proposed method is verified by simulation and comparison experiments, demonstrating its higher evaluation accuracy compared with similar algorithms. © 2023 IEEE.

关键词： Wireless networks

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Node-Decomposition Privacy-Preserving for Finite-Time Secondary Control of DC Microgrids

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IEEE Transactions on Smart Grid 2025年第4期16卷 3295-3304页

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Multi-objective Optimization of the Interior Permanent Magnet Synchronous Motor for Electric Vehicles 6

Multi-objective Optimization of the Interior Permanent Magne...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems 13

Benchmarking Derivative-Free Global Optimization Methods on ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Küdela, Jakub Juříček, Martin Parǎk, Roman Tzanetos, Alexandros Matoušek, Radomil Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Jönköping University Department of Computing Jönköping Sweden

ISBN: (纸本)9798350308365

Several real-world applications introduce derivativefree optimization problems, called variable dimension problems, where the problem's dimension is not known in advance. Despite their importance, no unified framework for developing, comparing, and benchmarking variable dimension problems exists. The robot arm controlling problem is a variable dimension problem where the number of joints to optimize defines the problem's dimension. For a holistic study of global optimization methods, we studied 14 representative methods from 4 different categories, i.e., (i) local search optimization techniques with random restarts, (ii) state-of-the-art DIRECT-type methods, (iii) established Evolutionary Computation approaches, and (iv) state-of-the-art Evolutionary Computation approaches. To investigate the effect of the problem's dimensionality on the solution we generated 20 instances of various combinations among the number of predefined and open decision variables, and we performed experiments for various computational budgets. The results attest that the robot arm controlling problem provides a proper benchmark for variable dimensions. Furthermore, methods in-corporating local search techniques have dominant performance for higher dimensionalities of the problem, while state-of-the-art EC methods dominate in the lower dimensionalities. © 2024 IEEE.

关键词： Budget control

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Model Predictive Control for Three-Intersection Traffic Control Including Bus-Only Lanes

Model Predictive Control for Three-Intersection Traffic Cont...

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2023 China automation Congress, CAC 2023

作者： Huang, Zhentao Dai, Li Lin, Shu Sun, Zhongqi Zhan, Yufeng Xia, Yuanqing School of Automation Beijing Institute of Technology Beijing China Jiaxing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350303759

In this paper, we propose a model predictive control (MPC) method based on an enhanced urban traffic model. The aim is to enhance intersection access efficiency and alleviate congestion. The model incorporates two main improvements: 1) the separate modeling of ordinary vehicles and buses on urban roads, taking into account dedicated lanes and stops for buses, and 2) the inclusion of three intersections in the traffic network to capture the interactive influence of two road sections. Building upon this improved model, we design an MPC controller to regulate the traffic light status at the intersections and effectively control traffic flow. We provide simulation results of the controller implemented in the traffic network and compare with other scheme to validate the effectiveness of the model and control strategy. © 2023 IEEE.

关键词： Model predictive control

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Unifying Generation and Prediction on Graphs with Latent Graph Diffusion 38

Unifying Generation and Prediction on Graphs with Latent Gra...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Cai Wang, Xiyuan Zhang, Muhan Department of Automation Tsinghua University China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute for Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China

In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks including regression and classification into a generic (conditional) generation framework, which enables diffusion models to perform deterministic tasks with provable guarantees. We then propose Latent Graph Diffusion (LGD), a generative model that can generate node, edge, and graph-level features of all categories simultaneously. We achieve this goal by embedding the graph structures and features into a latent space leveraging a powerful encoder and decoder, then training a diffusion model in the latent space. LGD is also capable of conditional generation through a specifically designed cross-attention mechanism. Leveraging LGD and the "all tasks as generation" formulation, our framework is capable of solving graph tasks of various levels and types. We verify the effectiveness of our framework with extensive experiments, where our models achieve state-of-the-art or highly competitive results across a wide range of generation and regression tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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Scene text recognition via dual character counting-aware visual and semantic modeling network

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Science China(Information Sciences) 2024年第3期67卷 313-314页

作者： Ke XIAO Anna ZHU Brian Kenji IWANA Cheng-Lin LIU School of Computer Science and Artificial Intelligence Wuhan University of Technology Human Interface Laboratory Kyushu University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Scene text recognition(STR) is drawing increasing attention nowadays due to its wide application in real life. Character counting information, as auxiliary information, has been shown to be effective in boosting text ... 详细信息

关键词： Decoding

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年第4期16卷 3282-3294页

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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HGGAN: Visible to Thermal Translation Generative Adversarial Network Guided by Heatmap

HGGAN: Visible to Thermal Translation Generative Adversarial...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Liu, Tong Liu, Yufeng Xu, Wenda Pu, Yuandong Hao, Yuqi Zuo, Wei School of Automation Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The realization of multi-modal image fusion requires sufficient cross domain data. Translating the visible images is an effective method to obtain thermal-visible paired images from the visible image domain to the thermal image domain. The current translation methods have some disadvantages, such as unreasonable distribution of thermal radiation intensity, blurred edges, spatial distortion and feature loss. So they are not friendly to downstream tasks. Based on the generation and reconstruction strategy of CycleGAN, we propose an image to image translation network guided by heatmap which is called HGGAN. We use the heatmap of object that detected by network to encode the heatmap code, and combine the image edge code to improve the image generation performance. We test the image standard of the generated image, and use the object detection network to verify. © 2022 IEEE.

关键词： Generative adversarial networks

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