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Chinese Automation Congress (CAC)

作者： Kaiwen Zhao Yu Xia Qi Su Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Network resilience generally refers to the ability of a network to maintain normal operation in the face of various failures and disturbances, which has many applications in social, financial, communication, and transportation systems. There is a large amount of research on designing strategies to disrupt the network from the perspective of attack and to protect the network from the defense standpoint. However, attacks and defenses in the real world often occur simultaneously and repeatedly. The study of network resilience should not be limited to the structural characteristics but should consider the functional characteristics of the network. We develop a general game-theoretic framework to study attack-defense repeated interactions on complex net-works and examine the possibility of achieving precise control over the payoffs of attackers and defenders. Our findings reveal that neither the attacker nor the defender can unilaterally control their own payoff, the opponent's payoff, or any linear combination thereof. This work highlights the limitations of unilateral payoff control in real-world interaction scenarios, a consideration often overlooked in numerous theoretical studies.

关键词： Presses Interactive systems Stability criteria Transportation Games Power system stability Protection Game theory Faces Resilience

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LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

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Annual Conference of Industrial Electronics Society

作者： Yu Wu Bo Yang Dafeng Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy.

关键词： Vibrations Industrial electronics Accuracy Computational modeling Mechanism design Predictive models Logic gates Real-time systems Data communication Industrial Internet of Things

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention

Gland Segmentation Via Dual Encoders and Boundary-Enhanced A...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huadeng Wang Jiejiang Yu Bingbing Li Xipeng Pan Zhenbing Liu Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods.

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Lianghao Li Cheng He Jianqing Lin Linqiang Pan Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems.

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Consensus of Linear Multi-agent Systems with Pure Relative Output Through Fully Distributed Reduced-Order Protocols

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IFAC-PapersOnLine 2023年第2期56卷 10162-10167页

作者： Yuwen Ma Xianwei Li Shaoyuan Li Department of Automation Shanghai JiaoTong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

This paper considers fully distributed adaptive control for linear multi-agent systems with pure relative output information only. Two reduced-order protocols, namely, an edge-based protocol and a node-based protocol, are derived. For the edge-based protocol, each edge is adapted by a coupling weight which depends only on relative output information of the associated two agents, while the coupling weight in the node-based protocol is based on the relative output information of all neighboring agents. Sufficient conditions for the solvability of the consensus problem under the two protocols are derived. Compared with most of the existing related protocols, the main merits of the protocols are that only relative output information is needed, which helps reduce the communication burdens and protect the multi-agent systems from network attacks, and that the protocols are fully distributed. A simulation example is finally presented to illustrate the effectiveness of the proposed protocols.

关键词： Multi-agent systems consensus pure relative output fully distributed design

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Adversarial Refinement Network for Human Motion Prediction 15th

Adversarial Refinement Network for Human Motion Prediction

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

ISBN: (纸本)9783030695316

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2021, Springer Nature Switzerland AG.

关键词： Errors

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Hierarchical finite-time optimization regulation of multiple autonomous surface vehicles for time-varying target cooperative tracking

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Kongzhi yu Juece/control and Decision 2025年第1期40卷 279-284页

作者： Ge, Ming-Feng Li, Yi-Fan Liu, Zhi-Wei Gu, Zhi-Wei Chen, Qian School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Imaging Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

We propose a novel hierarchical finite-time optimization regulation algorithm for multiple autonomous surface vehicles (ASVs) to achieve cooperative tracking of a time-varying target. An objective function reflecting the energy consumption of the multiple ASVs system in the tracking process is designed, and a finite-time optimal estimator (FTOE) is developed to minimize the objective function in the case that ASVs form a specified formation while following the desired trajectory. Meanwhile, the target trajectory can be variable instead of constant. In the finite-time local controller (FTLC), the sailing states of the multiple ASVs system can be stabilized to the vicinity of the multiple various predefined time-varying trajectories in finite time in a way that minimizes the objective function by tracking the FTOE. The proof of the finite-time stability is provided and the settling time is calculated. Finally, simulation results demonstrate the effectiveness of the proposed hierarchical algorithm. © 2025 Northeast University. All rights reserved.

关键词： Target tracking

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Research on the detection method of alignment beacon based on the spatial structure feature of train bogies

Research on the detection method of alignment beacon based o...

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2022 Chinese Automation Congress, CAC 2022

作者： Cao, Chong Wan, Shaowei Peng, Gang He, Dingxin Zhong, Sheng Deng, Zhang Guo, Wentao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing100071 China Wuhan Lisai Technology Co. Ltd. Wuhan430073 China

ISBN: (纸本)9781665465335

The high-speed train inspection robot needs to perform alignment parking and image acquisition at the position of the train bogie, and reliable alignment identification marks are the basis for the inspection robot to achieve highprecision alignment parking. The existing alignment method is mainly based on the way of artificial beacons, in the inspection workshop through manual laying of additional identification marks such as QR codes, magnetic stripes, RFID electronic tags, auxiliary robot alignment parking, but this type of method in beacon maintenance, laying costs and other aspects have certain limitations. Aiming at the problem, this paper proposes a vision-based bogie beacon detection method, which combines the target detection technology with the unique bogie spatial structure feature of the train, and provides reliable identification marks for the alignment and parking of the inspection robot. This method improves the alignment accuracy, reduces the cost of laying and maintaining artificial beacons, and has high application value. © 2022 IEEE.

关键词： Railroads

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