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43rd Chinese control Conference, CCC 2024

作者： Li, Kecheng Yang, Genyuan Dong, Shanling Liu, Meiqin Zhejiang University College of Control Science and Engineering Hangzhou China Xi'an Jiaotong University The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an China Zhejiang University College of Electrical Engineering Hangzhou China

ISBN: (纸本)9789887581581

The increasing integration of smart grid technologies in multiregional power systems improves efficiency but also exposes them to cyber security threats, especially false data injection attacks. In this paper, we address this challenge by proposing a distributed detection framework tailored for multi-regional power systems. Traditional centralized approaches are insufficient to cope with the dynamic and decentralized nature of these systems. The framework aims to simultaneously monitor and analyze multiple regions in real-time, enhancing the system's ability to withstand false data injection attacks. This study outlines the proposed framework and provides simulation results for validation, emphasizing the need for proactive defense mechanisms in safeguarding the data integrity of power systems. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： distributed detection framework false data injection attacks multi-area power systems

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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A Technique for Altering Face Attributes Based on Differential Structure 22

A Technique for Altering Face Attributes Based on Differenti...

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6th International Conference on Big Data and Internet of Things, BDIOT 2022

作者： Li, Yue He, Xiping He, Dan Yuan, Rui Chongqing Key Laboratory of Intelligent Sensing and Blockchain Technology School of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China Chongqing Key Laboratory of Intelligent Sensing and Blockchain Technology Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing400067 China

ISBN: (纸本)9781450390361

The task of face attribute editing refers to the implementation of specified attribute editing by integrating encoder-decoder and generative adversarial networks. The majority of current codec architectures carry out attribute editing by providing the generator with both the source face attribute vector and the destination face attribute vector. Nevertheless, using the entire target attribute vector as input more often hurts the editing outcomes, leading to modifications in unnecessary areas and blurring of image features. This paper suggests an attribute difference method that uses the difference between the target attribute and the source attribute vector as the encoder-decoder input to improve the flexible editing ability of attributes, taking into account that a particular editing task is only related to the attribute to be changed. Constrained picture reconstruction quality, detailed information preservation, and effective attribute editing are achieved simultaneously by combining the reconstruction loss, adversarial loss, and attribute classification loss as the algorithm's loss expression. Experiments demonstrate that the suggested approach successfully address the issue of blurring the details in the generated image as well as precisely alter the face attributes. © 2022 ACM.

关键词： Generative adversarial networks

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Restoring Chinese documents images based on text boundary lines

Restoring Chinese documents images based on text boundary li...

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IEEE International Conference on systems, Man and Cybernetics

作者： Hong Liu Runwei Ding Key Laboratory of Machine Perception and Intelligence Key Laboratory of Integrated Micro-system Shenzhen Graduate School Shenzhen Graduate School Peking University China Key Laboratory of Integrated Microsystem Key Laboratory of Machine Perception and Intelligence Shenzhen Graduate School Peking University China

Distortion always appears in document images while scanning thick bound volumes. There are two kinds of distortion for the scanned grayscale images, shadow appears at the volumes' spine area, and warping of the words occurs in the shadow. In this paper, a novel text boundary lines based method for efficient restoration of warped scanning Chinese document images is presented. We first detect on which side of an image the shadow lays by row grayscale analysis method. Then the shadow is removed by a modified Niblack's algorithm. In order to detect the warped feature, a text boundary lines' detection method is proposed. Finally, an adjustment method based on the text boundary lines is carried to restore the warped words. Experiments on 400 various scanning Chinese document images are implemented. The improvement on average character recall is 11.92% to 14.89%. Experiments show that the proposed restoration method is efficient for Chinese documents with both text and non-text regions.

关键词： Image restoration Surface reconstruction Laboratories Image reconstruction Image processing Books machine intelligence Gray-scale Computer vision Shape

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2020

作者： Qi, Xianzhi Fu, Wenxing An, Pei Wu, Bingli Ma, Jie National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100071 China

ISBN: (纸本)9781728152240

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy. © 2020 IEEE.

关键词： Wakes

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Design and simulation of a hip exoskeleton for lateral walking

Design and simulation of a hip exoskeleton for lateral walki...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Zhewen Cao, Wujing Chen, Chunjie Yu, Hongliu Wu, Xinyu Meng, Qiaoling Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology

ISBN: (纸本)9781665436786

In this article, a rehabilitation exoskeleton assisted by lateral walking is designed to help patients with lower limb movement disorders to solve the problem of lateral walking assistance, so as to achieve the effect of rehabilitation training. Based on the characteristics of lateral movement of human body, the exoskeleton designed in this paper uses linear push rod motor and thigh binding to assist hip abduction and adduction respectively. Based on the research of human transverse movement gait, a reasonable linkage mechanism was designed and kinematics simulation was carried out. The dynamics model of human lower limbs was established and calculated with MATLAB simulation software. the moment of hip joint lateral movement is obtained. Finally, the structural parameters of the driving mechanism were analyzed by dynamic simulation, and the simulation results were compared with the target motion function to verify the rationality of the design. © 2021 IEEE.

关键词： MATLAB

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What is that in your hand? Recognizing grasped objects via forearm electromyography sensing

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Fan, Junjun Fan, Xiangmin Tian, Feng Li, Yang Liu, Zitao Sun, Wei Wang, Hongan State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Google Research and Machine Intelligence Mountain ViewCA United States TAL AI Lab Beijing China

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking. © 2018 Association for Computing machinery.

关键词： Object detection

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WaterMask: Instance Segmentation for Underwater Imagery

WaterMask: Instance Segmentation for Underwater Imagery

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International Conference on Computer Vision (ICCV)

作者： Shijie Lian Hua Li Runmin Cong Suqi Li Wei Zhang Sam Kwong Hainan University Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong University City University of Hong Kong Lingnan University Hong Kong

Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding. However, the paucity of general multiclass instance segmentation datasets has impeded the development of instance segmentation studies for underwater images. In this paper, we propose the first underwater image instance segmentation dataset (UIIS), which provides 4628 images for 7 categories with pixel-level annotations. Meanwhile, we also design WaterMask for underwater image instance segmentation for the first time. In Water-Mask, we first devise Difference Similarity Graph Attention Module (DSGAT) to recover lost detailed information due to image quality degradation and downsampling to help the network prediction. Then, we propose Multi-level Feature Refinement Module (MFRM) to predict foreground masks and boundary masks separately by features at different scales, and guide the network through Boundary Mask Strategy (BMS) with boundary learning loss to provide finer prediction results. Extensive experimental results demonstrates that WaterMask can achieve significant gains of 2.9, 3.8 mAP over Mask R-CNN when using ResNet-50 and ResNet-101. Code and Dataset are available at https://***/LiamLian0727/WaterMask.

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Bionic design of a self-reconfigurable modular robot for search and rescue

Bionic design of a self-reconfigurable modular robot for sea...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Gao, Guangju Liu, Jingshuai He, Yong Sun, Jianquan Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out. © 2021 IEEE.

关键词： Reliability analysis

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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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