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Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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Chinese automation Congress (CAC)

作者： Jiang Li Xiaoping Wang Shanglin Lei Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology (HUST) Wuhan China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan China

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge.

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Shaping effect of phase-locked loop on impedance characteristics of PMSG wind turbines connected to weak-grid

Shaping effect of phase-locked loop on impedance characteris...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Wan, Feiya Li, Zhen Liu, Bin Li, Guanghui Dong, Xiaoliang Chen, Xi Yu, Samson S. Oo, Amanullah M. T. Shan, Zhenyu Liu, Xiangdong Beijing Institute of Technology School of Automation and Key Laboratory for Intelligent Control Decision on Complex Systems Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America United States Deakin University School of Engineering 75 Pigdon Rd Waurn Ponds Victoria Australia North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9781728155081

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL. © 2020 IEEE.

关键词： Phase locked loops

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Fully linear graph convolutional networks for semi-supervised learning and clustering

arXiv

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Cai, Zhihua Liu, Xiaobo Ding, Yao Ghamisi, Pedram School of Computer Science China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China Xi'an Research Institute of High Technology Xi'an710000 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria

This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closedform solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain, e.g., ridge regression and subspace clustering. Furthermore, we implement a semisupervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://***/AngryCai/FLGC. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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A Bioinspired retinal neural network for accurately extracting small-target motion information in cluttered backgrounds

arXiv

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arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities through adjusting the dendrite field size of spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly. Copyright © 2021, The Authors. All rights reserved.

关键词： Ophthalmology

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

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arXiv 2021年

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Xi Tang Qingge Li Yao Tang Lan Tian Yue Zheng Xiangxin Li Naifu Jiang Peng Shang Guanglin Li Liang Peng Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems & the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients’ neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience.

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Research on the Stability of Aircraft With Nose Wheel Autonomous Taxiing Configuration

Research on the Stability of Aircraft With Nose Wheel Autono...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Huang, Haixiang Zhang, Hao Liu, Xiaochao Shang, Yaoxing Beihang University School of Automation Science and Electrical Engineering Beijing China Ningbo Institute of Technology Beihang University Ningbo China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Research Institute for Frontier Science Beihang University Beijing China Key Laboratory of Advanced Aircraft Utility Systems Beihang University Ministry of Industry and Information Technology China Beijing Tianma Intelligent Control Technology Co. Ltd. Beijing China General Research Institute of Coal Science Beijing China

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

The traditional main engine-driven aircraft ground taxiing method has the problems of low efficiency, heavy pollution and high fuel consumption. However, today's aircraft is facing the urgent need for efficient ground maneuvering, it is urgent to further optimize the ground taxiing method of the aircraft. In response to this demand, a taxiing strategy of autonomously dragging the nose wheels of the aircraft is proposed. However, the ground dynamics research on the nose-wheel drive taxiing configuration aircraft is still insufficient. Therefore, in this paper, the ground dynamics modeling and simplification of the nose wheel autonomous drag configuration aircraft are carried out;the stability boundary of the system under different nose wheel rotation angles and rotation angle change rates during ground taxiing is analyzed and calculated;The traditional configuration aircraft with thrust to achieve the function of ground taxiing is compared and analyzed. It provides a theoretical basis for the efficient and autonomous taxiing stability control of aircraft on the ground. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Wheels

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Webly Supervised Knowledge Embedding Model for Visual Reasoning

Webly Supervised Knowledge Embedding Model for Visual Reason...

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Conference on Computer Vision and Pattern Recognition (CVPR)

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

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-available web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.

关键词： Visualization Cognition Knowledge based systems Task analysis Knowledge engineering Modulation Robustness

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Research on SVM and FLDA in classification with comparative experiments

Research on SVM and FLDA in classification with comparative ...

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IEEE International Conference on Networking, Sensing and control

作者： Yegan Qian Gang Xiong Yanjie Yao Anhui Radio TV Station Hefei Hanteng Information Tech Company Hefei Anhui China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

The paper discusses two important classification techniques, Fisher's linear discriminated analysis (FLDA) and Support Vector Machine (SVM). First, we propose a theoretical discussion, and then implement FLDA and SVM on several datasets of two classes and multiclass, a comparative experimental analysis among these two techniques aims at exploring and assessing the performance of FLDA and SVM classifiers. To sustain such analysis, the two classification techniques are compared with different training data sets and testing data sets. Different performance indicators have been used to support our experimental studies in a detailed and accurate way such as the classification accuracy. The results obtained on different datasets conclude that FLDA and SVM are valid and effective approaches for pattern classification and conclude their different performance and problems with different size datasets. Meanwhile, the paper employs a non-traditional method to get the training and testing data set, and concludes detailed pros and cons from the experiment results.

关键词： Support vector machines Training Testing Accuracy Vectors Head Magnetic heads

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