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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Yu, Yingying Cao, Zhiqiang Liang, Shuang Liu, Zhicheng Yu, Junzhi Chen, Xuechao Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Intelligent Robotics Institute BIT Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781728163215

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot. © 2019 IEEE.

关键词： Image segmentation

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Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

Graph Attention Model Embedded With Multi-Modal Knowledge Fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi’an Jiaotong University Xi’an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.

关键词： Biomedical imaging Robustness Data models Machine learning Task analysis Knowledge engineering

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Design of Ultrasonic Ranging System Based on Cross-correlation Method

Design of Ultrasonic Ranging System Based on Cross-correlati...

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Chinese control Conference (CCC)

作者： Han Lu Zhihua Li Pei Gao School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In order to solve the problem that ultrasonic ranging is difficult to obtain the first wave, an ultrasonic ranging system based on cross-correlation method is designed. In this system, the signal generating unit is mainly responsible for transmitting high frequency sound waves, and the data acquisition unit is mainly responsible for collecting and storing the sound waves. Then, according to the time delay between the collected acoustic signals, the time difference between the signals received by the signal acquisition unit is obtained, so as to measure the distance.

关键词： Acoustics Correlation Ultrasonic variables measurement Delays Power supplies Distance measurement

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Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image

arXiv

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arXiv 2020年

作者： Cai, Yaoming Zhang, Zijia Cai, Zhihua Liu, Xiaobo Jiang, Xinwei Yan, Qin School of Computer Science China University of Geosciences Wuhan430074 China School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering has been proven to be powerful for exploiting the intrinsic relationship between data points. Despite the impressive performance in the HSI clustering, traditional subspace clustering methods often ignore the inherent structural information among data. In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering. Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain, which results in a more robust graph embedding dictionary. We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data. Basing on the framework, we further propose two novel subspace clustering models by using the Frobenius norm, namely Efficient GCSC (EGCSC) and Efficient Kernel GCSC (EKGCSC). Both models have a globally optimal closed-form solution, which makes them easier to implement, train, and apply in practice. Extensive experiments on three popular HSI datasets demonstrate that EGCSC and EKGCSC can achieve state-of-the-art clustering performance and dramatically outperforms many existing methods with significant margins. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectroscopy

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A Privilege Management Method of Centralized Management System in the Computer Room

A Privilege Management Method of Centralized Management Syst...

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Chinese Automation Congress (CAC)

作者： XiaoYu Dou Yan Yuan Weihua Cao Peilun Ouyang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The computer room management system is a management system for network monitoring, bottom control, information filtering and security monitoring of computer room. It has the characteristics of multi-user and multi module. Scientific and intelligent management and control is the premise of efficient and stable work in computer room. Aiming at the problems of power supply security and efficiency, user authority configuration and management security of traditional computer room, a centralized management system of computer room based on Role Based of Access control(RBAC) is designed and implemented. This paper first introduces the structure and function of the system, then focuses on the detailed design of the user management subsystem of the system, and finally gives the realization and actual operation of the system. At present, the system has been successfully applied to a power room equipment, which proves the security, stability and efficiency of the system.

关键词： Computer security Security Encryption Databases Real-time systems Personnel complexity theory

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A Comparison Study of Three Active Disturbance Rejection Methods

A Comparison Study of Three Active Disturbance Rejection Met...

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第三十九届中国控制会议

作者： Youwu Du Weihua Cao Jinhua She Mingxing Fang School of Automation China University of Geosciences College of Physics and Electronic Information Anhui Normal University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the performance of three active disturbance-rejection methods: disturbance-observer-based（DOB） control, active disturbance rejection control（ADRC）, and equivalent-input-disturbance-based（EID） control. Compared to the classic feedback control, these methods provide an additional degree of freedom to reject disturbances effectively. First, the disturbance-rejection mechanism of each of the methods is analyzed. For a proper design, the transfer function from disturbances to the output of a system approximates zero using these methods. This implies the effect of disturbances is blocked to the ***, the three methods are applied to the position control of a ball-and-beam system for comparison. The simulation results show that all these methods are insensitive to disturbances and robust to system uncertainties. And the error of the system output is smaller for the EID method than for the DOB and ADRC methods.

关键词： Active disturbance rejection control（ADRC） disturbance observer（DOB） disturbance estimation equivalent input disturbance（EID） robust control

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Graph Convolutional Extreme Learning Machine

Graph Convolutional Extreme Learning Machine

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International Joint Conference on Neural Networks (IJCNN)

作者： Zijia Zhang Yaoming Cai Wenyin Gong Xiaobo Liu Zhihua Cai School of Computer Science China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don't apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.

关键词： Convolution Robustness Closed-form solutions Neural networks Training Convolutional codes optimization

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Fluidspread: A New Method of Maximizing Positive Influence in Online Social Networks via Fluid Dynamics

Fluidspread: A New Method of Maximizing Positive Influence i...

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International Conference on control and Automation (ICCA)

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Influence maximization aims at detecting the top-k influential users in online social networks. Almost all previous models of influence spread cannot simultaneously incorporate users' attitudes, interactions between users, and dynamic influences. However, in this study, we established a new model of influence spread via fluid dynamics, which reveals the time-evolving process for influence spread. We modeled the spread of influence as the process of fluid update based on three dimensions: the difference of fluid height, the temperature of fluids, and the difference of temperature. Moreover, we formulated the problem of maximizing positive influence and devised a Fluid-spread greedy algorithm to solve it. We conducted extensive comparisons between our approach and several baselines, and experimental results illustrate the effectiveness and efficiency of the Fluidspread model and algorithm.

关键词： Containers Dynamical systems Valves Plugs Time complexity Temperature distribution Social networking (online)

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Compensation control for Tool Attitude in Directional Drilling systems

Compensation Control for Tool Attitude in Directional Drilli...

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Asian control Conference

作者： Zhen Cai Xuzhi Lai Min Wu Luefeng Chen Chengda Lu the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781728102634

This paper is concerned with tool attitude control of directional drilling systems, which plays a key role in guaranteeing desired drilling trajectory. The coupling of inclination and azimuth motions are taken into account in the motion model, where the azimuth one involves some time-varying parameters. To ensure satisfactory control precision, an output-feedback compensation control scheme is developed based on a motion model established from Taylor expansion. Stability analysis is sequentially made for the resulting inclination and azimuth closed-loop system. At last, some simulations show that the compensation controller can achieve higher control accuracy than a proportional integral controller.

关键词： Azimuth Tools Mathematical model Stability analysis control systems Attitude control Delays

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Emotion Regulation Based on Multi-objective Weighted Reinforcement Learning for Human-robot Interaction

Emotion Regulation Based on Multi-objective Weighted Reinfor...

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Asian control Conference

作者： Man Hao Weihua Cao Zhentao Liu Min Wu Yan Yuan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781728102634

Given emotion is important in maintaining mental and physical well-being. A multi-objective weighted reinforcement learning (MOW-RL) decision method is proposed to execute emotion regulation in human-robot interaction. The goal of emotion regulation is to minimize the cost of executing the service while eliminate user's negative emotions or maintain user's positive emotions. Considering the coordination problem of two objectives, fuzzy analytic hierarchy process (FAHP) is used to calculate the weight of each target reward and punishment function under different conditions. In addition, the influence factors of different personality on the difficulty level of emotion transfer are calculated by FAHP. Experiments are performed by 20 experimenters in the laboratory scenario, from which the results show that experimenters' satisfaction is 2.3, which is close to satisfaction.

关键词： Human-robot interaction External stimuli Service robots Reinforcement learning Robot kinematics Emotion recognition

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