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arXiv 2021年

作者： Wang, Rui Chen, Jian Yu, Gang Sun, Li Yu, Changqian Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Shanghai China East China Normal University Shanghai China

image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i.e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles. Specifically, we collaboratively manipulate the modulation style channels and feature maps in control units rather than individual ones to obtain the semantic and spatial disentangled controls. Furthermore, we propose a simple yet effective method to detect the attribute-specific control units. We move the modulation style along a specific sparse direction vector and replace the filter-wise styles used to compute the feature maps to manipulate these control units. We evaluate our proposed method in various face attribute manipulation tasks. Extensive qualitative and quantitative results demonstrate that our proposed method performs favorably against the state-of-the-art methods. The manipulation results of real images further show the effectiveness of our method. © 2021, CC BY.

关键词： Modulation

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Accurate Resilient Average Consensus via Detection and Compensation

Accurate Resilient Average Consensus via Detection and Compe...

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IEEE Conference on Decision and control

作者： Wenzhe Zheng Zhiyu He Jianping He Chengcheng Zhao Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665436601

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. Different from the widely investigated Mean-Subsequence-Reduced-based and detection-isolation-based approaches which guarantee consensus, in this paper, we address this problem by detecting misbehaviors, mitigating corresponding impact and achieving accurate average consensus. General types of misbehaviors are considered, including deception attacks and accidental faults. We characterize the disturbances of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where such information is intermittently available, a stochastic extension named S-DCC is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve average consensus exactly and in expectation, respectively. Finally, extensive simulations verify the effectiveness of the proposed algorithms.

关键词： Fault tolerance Adaptation models Conferences Fault tolerant systems Multi-robot systems Multi-agent systems Convergence

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Convergence analysis of nonconvex distributed stochastic zeroth-order coordinate method

arXiv

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arXiv 2021年

作者： Zhang, Shengjun Dong, Yunlong Xie, Dong Yao, Lisha Bailey, Colleen P. Fu, Shengli OSCAR Laboratory Department of Electrical Engineering University of North Texas DentonTX76207 United States School of Artificial Intelligence and Automation The MOE Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates with an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of O(√p/√T) for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Scale-free distributed control for the DC MG cluster under DoS attacks: An asynchronous switched strategy

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Automatica 2025年 179卷

作者： Fabin Cheng Jingang Lai School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan 430074 China

This paper studies the voltage restoration problem of the DC Microgrid (MG) cluster with a two-layer communication structure under Denial-of-Service (DoS) attacks. Based on the open multiagent framework, an asynchronous switching strategy is designed to suppress the impact of DoS attacks on the entire MG cluster’s voltage restoration. Meanwhile, we derived the relationship between attack model parameters and communication link switching mode selection through the general model of DoS attacks and asynchronous switching methods based on average dwell time. Moreover, the designed distributed control protocol is scale-free; that is, it does not rely on any information from the two-layer communication structure and the number of distributed generations (DGs), which makes it universal. Through rigorous theoretical proof, it has been proven that in the DC MG cluster with two-layer communication structures, voltage restoration can be achieved regardless of the number and structure of DGs being attacked. Finally, two simulation examples on the MATLAB/SimPowerSystem, including a DC MG cluster consisting of 8 DGs and an IEEE 33-node system are used to investigate the scalability and flexibility of the proposed distributed control algorithm.

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Continuous Cross-resolution Remote Sensing image Change Detection

arXiv

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arXiv 2023年

作者： Chen, Hao Zhang, Haotian Chen, keyan Zhou, Chenyao Chen, Song Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics Beihang University Beijing100191 China The Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Journalism and Communications Jeonuk National University Jeonju-Si54896 Korea Republic of The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Given training samples of a fixed bitemporal resolution difference (ratio) between the high-resolution (HR) image and the low-resolution (LR) one, current cross-resolution methods may fit a certain ratio but lack adaptation to other resolution differences. Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences. Concretely, we synthesize blurred versions of the HR image by random downsampled reconstructions to reduce the gap between HR and LR images. We introduce coordinate-based representations to decode per-pixel predictions by feeding the coordinate query and corresponding multi-level embedding features into an MLP that implicitly learns the shape of land cover changes, therefore benefiting recognizing blurred objects in the LR image. Moreover, considering that spatial resolution mainly affects the local textures, we apply local-window self-attention to align bitemporal features during the early stages of the encoder. Extensive experiments on two synthesized and one real-world different-resolution CD datasets verify the effectiveness of the proposed method. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on the three datasets both in in-distribution and out-of-distribution settings. The empirical results suggest that our method could yield relatively consistent HR change predictions regardless of varying bitemporal resolution ratios. Our code is available at https://***/justchenhao/SILI_CD. Copyright © 2023, The Authors. All rights reserved.

关键词： Change detection

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Thawing Methods Affect Quality Properties and Protein Denaturation of Frozen Beef

SSRN

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SSRN 2024年

作者： Zhu, Jiaying Zhu, Yingying Li, Hui Fu, Caili Yin, Weimin Li, Chunbao State Key Laboratory of Meat Quality Control and Cultured Meat MOST College of Food Science and Technology Nanjing Agricultural University Nanjing210095 China Key Laboratory of Meat Processing MOA College of Food Science and Technology Nanjing Agricultural University Nanjing210095 China Jiangsu Synergetic Innovation Center of Meat Processing and Quality Control College of Food Science and Technology Nanjing Agricultural University Nanjing210095 China Engineering Research Center of Magnetic Resonance Analysis Technology Department of Food Nutrition and Test Suzhou Vocational University Jiangsu Suzhou China Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources College Chemical Engineering Nanjing Forestry University Nanjing210037 China Shanghai Dotwil Intelligent Technology Co. Ltd. Jiangsu Suzhou China

Balancing efficiency and meat quality during the thawing of frozen meat remains a big challenge for meat industry. In this study, the effects of three thawing methods, including ultrasound-assisted thawing (UT), solid-state microwave thawing (SMT) and air thawing (CT) on the quality and protein physicochemical properties of beef in a pilot scale, were investigated. COMSOL Multiphysics simulation was employed to predict the thawing process, identifying an optimal endpoint of no more than -2 ºC. Results showed that optimized SMT efficiently avoided overheating and greatly balanced the thawing efficiency, meat quality and physicochemical properties of myofibrillar proteins, resulting in better water-holding capacity, lower levels of TBARS value and protein oxidation, while though UT kept better color and reduced protein denaturation, serious mechanical damage and aggravated protein oxidation limited its application. Thus, SMT could be a promising solution for industrial applications to thaw frozen meat. © 2024, The Authors. All rights reserved.

关键词： Beef

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Attacking End-to-End Visual Navigation Model: How Weak Existing Learning-Based Approaches Can Be?

Attacking End-to-End Visual Navigation Model: How Weak Exist...

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IEEE International Conference on Robotics and Biomimetics

作者： Hongye Wang Kefan Jin Hesheng Wang Shanghai Jiao Tong University China MOE Key Laboratory of Marine Intelligent Equipment and System State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665405362

Learning-based self-driving techniques and vehicle navigation approaches are the most hot topics in recent years. However, existing approaches typically assume the attack-free sensor data, the safety issue under large disturbance or external attack have not been well-solved. In this article, we build a simple FGSM-based attack method designed by minimizing the maximum value of the extracted visual features in order to greatly decrease the performance of the popular learning model for vehicle navigation and even make it totally fail. The proposed min-max operation based feature space attack method solves the problems of branch activation uncertainties and the lack of labels. Furthermore, we also provide a general adversarial training framework which can be used to overcome the proposed feature space attack. Simulational experiments in CARLA platform demonstrate the effectiveness and practical applicability of our attacking approach and defence strategy.

关键词： Training Space vehicles Visualization Uncertainty Navigation Feature extraction Robustness

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Lithium-ion battery state-of-charge estimation using a real-time moving horizon estimation algorithm

Lithium-ion battery state-of-charge estimation using a real-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Jiayu Yan Song Li Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China the 29th Research Institute of China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781665401166

For battery management systems, it is significant to reliably estimate state-of-charge (SOC) from limited measurements in real time. Based on a nonlinear SOC-dependent equivalent circuit model, we propose a real-time moving horizon estimation (MHE) algorithm for SOC estimation. For efficient computation, the sequential quadratic programming strategy is applied, and each quadratic programming (QP) iteration is solved via the structure-exploiting forward and backward recursions. To account for the limited computation time allowed within each sampling interval, the proposed algorithm does not fully solve the receding horizon optimization problem over each sliding window, but performs fixed number of QP iterations. The simulation results show that compared to the existing fully solved MHE applied to SOC estimation, the proposed real-time MHE algorithm significantly saves computation time at the cost of a slight loss of estimation performance.

关键词： Computational modeling Simulation Estimation Real-time systems Time measurement Computational efficiency state of charge

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Convergence Analysis of Nonconvex Distributed Stochastic Zeroth-order Coordinate Method

Convergence Analysis of Nonconvex Distributed Stochastic Zer...

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IEEE Conference on Decision and control

作者： Shengjun Zhang Yunlong Dong Dong Xie Lisha Yao Colleen P. Bailey Shengli Fu OS-CAR Laboratory University of North Texas Denton TX USA School of Artificial Intelligence and Automation the MOE Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China University of North Texas Denton TX USA

ISBN: (纸本)9781665436601

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates under an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of ${\mathcal{O}}(\sqrt p /\sqrt T )$ for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms.

关键词： Smoothing methods Conferences Benchmark testing Cost function Approximation algorithms Information exchange Convergence

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New Insights on the Stability of Sampled-Data-Based LFC for Power Systems: A Delay-Interval-Dependent LKF Approach

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IEEE Transactions on Smart Grid 2025年

作者： Wang, Wei-Min Wang, Yan-Wu Wan, Qian Zeng, Hong-Bing Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Hunan University of Technology School of Electrical and Information Engineering Zhuzhou412007 China

This paper analyzes the stability problem of load frequency control (LFC) for power systems under uncertain transmission delays. First, an argumented LFC system model accounting for uncertainties in transmission delays is proposed. Then, a novel delay-interval-dependent Lyapunov-Krasovskii functional (LKF) that takes into account both the upper and lower bounds of the transmission delay is proposed. Several less conservative stability criteria are derived using the proposed LKF. Based on the stability criterion obtained, the sampling interval and the upper and lower bounds of the transmission delay are taken as the conditions to guide the controller design. Finally, case studies are conducted based on a one-area power system and a three-area power system, both integrated with wind and solar power generation. The simulation comparison results demonstrate that the proposed LFC scheme exhibits strong robustness against uncertain transmission delays, load fluctuations, and the intermittent generation of wind and solar power. © 2010-2012 IEEE.

关键词： Stability criteria

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