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Breakdown in interdependent directed networks under targeted attacks

EPL 2021年第6期133卷 68004-68004页

作者： Xu, W. Pan, L. Liu, X. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Inst Artificial IntelligenceEduc Minist China Wuhan 430074 Peoples R China

Cascading failures take place between networks after initial failures occur in interdependent networks. Most previous studies are limited to undirected networks, while many real-world networks are directed. For interdependent directed networks, earlier studies focused on random initial failures. How the interdependent directed networks respond to targeted attacks has not been addressed. In this work, we develop a general theoretical framework for analyzing the robustness in interdependent directed networks under targeted attack on nodes with high or low degrees (in-degrees or out-degrees). We find that for degree heterogeneous networks, targeted attacks perform more efficiently in breaking down the system than random failures, with the degree-based attacks being the most efficient way. Interestingly, we find that system robustness first increases then decreases as the degree heterogeneity increases, showing a peak, which helps understand system robustness and enables the better design of robust infrastructure systems. Copyright (C) 2021 EPLA

关键词： Networks and genealogical trees Structures and organization in complex systems Percolation

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A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-scale Twin Range image

A Point Cloud Segmentation Method Based on Ground Point Clou...

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第42届中国控制会议

作者： Yicheng Zhou Gang Peng Hangqi Duan Zhangang Wu Xukang Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Shantui Construction Machinery Co. Ltd.

Aiming at the shortcomings of traditional point cloud segmentation algorithms, this paper proposes a 3D lidar point cloud segmentation method combining ground filtering and multi-scale twin range image construction. This method fully considers the geometric space distribution characteristics of the point cloud and the randomness of the positions between *** on the assumption of multi plane model, the point cloud is divided into sectors. The ground points are removed through region ground plane fitting and growth, and then the multi-scale twin range image is constructed according to non-ground *** the basis of the twin range image, the Breadth First Search algorithm is improved, the depth-breadth joint search strategy is proposed, all the points to be clustered are traversed, and the adaptive clustering threshold is used for point cloud clustering,which realizes a point cloud segmentation method with higher accuracy and robustness. Through the experimental analysis of KITTI dataset and collected campus outdoor scene dataset, compared with the traditional point cloud segmentation algorithm,the method proposed in this paper can effectively improve the point cloud segmentation accuracy and robustness of mobile robots in complex environments.

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HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Twitter United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match query and support videos. However, they still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. In addition, we explicitly exploit the temporal coherence in videos to regularize the matching process. In this way, HyRSM++ facilitates informative correlation exchanged among videos and enables flexible predictions under the data-limited scenario. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmark

关键词： Benchmarking

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A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints

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SCIENTIFIC REPORTS 2022年第1期12卷 1-9页

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Southeast Univ Sch Automat Nanjing 214135 Peoples R China

In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the accuracy of grasping are both low when facing the scene of stacking and occlusion for the reason that there is information missing by single-view 2D camera-based methods while acquiring scene information, and the methods of grasping only can't change the difficult-to-grasp scene which is stack and occluded. Regarding the issue above, a pushing-grasping collaborative method based on the deep Q-network in dual viewpoints is proposed in this paper. This method in this paper adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects' RGB images and point clouds from two viewpoints, which solved the problem of lack of information missing. What's more, it combines the pushing and grasping actions with the deep Q-network, which make it have the ability of active exploration, so that the trained manipulator can make the scenes less stacking and occlusion, and with the help of that, it can perform well in more complicated grasping scenes. In addition, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before.

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Modeling of Solid Oxide Electrolysis Cell with Oxygen Pressure at the Oxygen electrode/Electrolyte Interface

Modeling of Solid Oxide Electrolysis Cell with Oxygen Pressu...

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第34届中国控制与决策会议

作者： Zhiping Xia Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science & Technology

As one of the key degradation mechanisms of Solid oxide electrolysis cell(SOEC),oxygen electrode delamination directly affects the durability of *** order to improve the service life of SOEC stack,optimizing the system regulation range is an effective *** this study,according to the delamination mechanism of SOEC oxygen electrode,based on the existing thermal and electrical characteristics of the SOEC stack,a new gas characteristic of oxygen electrode/electrolyte interface oxygen pressure is added,and a cross-flow SOEC 2-D stack model with the oxygen electrode/electrolyte interface oxygen pressure was ***,the influence of the oxygen electrode/electrolyte interface oxygen pressure characteristics on the safe operating range of the stack is *** simulation results show that the safe operating range of the system was optimized by adding it.

关键词： Solid oxide electrolysis cell Durability Delamination mechanism 2-D model Advantage analysis

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Research on Solid Oxide Fuel Cell System Model Building and 3D Testing Based on the Nodal Idea

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ATMOSPHERE 2023年第8期14卷 1261-1261页

作者： Li, Mingfei Zhu, Kanglin Rao, Mumin Chen, Zhengpeng Xiong, Kai Hou, Longtong Wang, Xiabin Chen, Chuangting Li, Shujun Li, Xi Guangdong Energy Grp Sci & Technol Res Inst Co Lt Guangzhou 511466 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Guangdong Energy Grp Co Ltd Guangzhou 510630 Peoples R China Guangdong Huizhou Lng Power Co Ltd Huizhou 516081 Peoples R China

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATlab/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor's inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system's response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system's efficiency and performance.

关键词： SOFC nodal idea model building three-dimensional testing

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Joint Moment Estimation for Hip Exoskeleton control: A Generalized Moment Feature Generation Method

arXiv

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arXiv 2024年

作者： Zhang, Yuanwen Xiong, Jingfeng Xian, Haolan Chen, Chuheng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China The Key Laboratory of Image Processing and Intelligent Control The Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (Robotics)

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Active Compliance Variable Impedance control of Lower Supernumerary Robotic Limb*

Active Compliance Variable Impedance Control of Lower Supern...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jun Huo Xinyi Wang Bo Yang Qingyang Yan Zhaofan Yuan Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Guangdong China China Ship Development and Design Centre Wuhan China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In human-robot systems, ensuring user safety amidst external disturbances remains paramount. An efficient variable impedance control (VIC) technique is designed to enhance human-robot interaction, particularly in scenarios with external force disturbances. By dynamically adapting impedance parameters in response to detected changes in contact force, VIC achieves heightened system flexibility. The robust stability of the VIC system is rigorously established using the Lyapunov approach, with a parameter update law tailored for a lower limb supernumerary robotic limb robot. Through simulations and experiments, the efficacy of the system in maintaining a specific safety margin is demonstrated. Moreover, the technique offers a pragmatic solution to accommodate individual variations in walking gait and cross-user disparities in interaction. The proposed method contributes to advancing the safety and adaptability of cross-user human-robot systems.

关键词： Legged locomotion Adaptation models Mechatronics Robust stability Force Human-robot interaction Safety Impedance Robots Pragmatics

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Cascading Failures in Interdependent Directed Networks Under Localized Attacks

SSRN

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SSRN 2023年

作者： Lv, Mengyu Pan, Linqiang Liu, Xueming Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Many real-world systems interact with one another through dependency links, which reduces the system robustness. Most previous studies on the robustness of interdependent networks focus on undirected networks, and the related studies on directed networks are limited to random or targeted attacks. However, some failure scenarios can not be described by these two kinds of attacks, such as earthquakes, floods, and epidemics, where systems are attacked in a local range. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. We find that for degree homogeneous networks, network robustness under localized attacks is similar to that under random attacks. There are four phase transitions in the phase diagram of the network, and a four-phase transition point and a two-phase transition point are found. For degree heterogeneous networks, localized attacks are more likely to lead to collapse than random attacks. As the coupling strength between networks increases, the interdependent networks first show a second order phase transition, and then a hybrid phase transition, and a first order phase transition at last. Furthermore, as the degree heterogeneity decreases, the robustness of networks first increases and then decreases, showing a local robustness maximum. The findings could help understand network robustness and enable better design of robust interdependent systems. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第8期69卷 8554-8565页

作者： Lu, Renzhi Bai, Ruichang Luo, Zhe Jiang, Junhui Sun, Mingyang Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Hanyang Univ Dept Elect Syst Engn Ansan 15588 South Korea Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining a satisfaction index. Specifically, to accommodate the characteristics of the decision-making problem, long short-term memory (LSTM) units are adopted to extract discriminative features from past electricity price sequences and fed into fully connected multi-layer perceptrons (MLPs) with the measured energy and time information, then a deep Q-network is developed to approximate the optimal policy. After that, an experimental setup is constructed to investigate the effectiveness of the proposed DRL-based demand response algorithm to bridge the gap between theoretical studies and practical implementations. Numerical results demonstrate that the proposed algorithm can handle energy management well for multiple smart facilities. Moreover, the proposed algorithm outperforms the model predictive control (MPC) strategy and uncontrolled solution and is close to the theoretical optimal control method.

关键词： Energy management Load management Feature extraction Approximation algorithms Predictive models Prediction algorithms Power system stability Deep reinforcement learning (DRL) demand response energy management experimental validation smart facility

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