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arXiv 2020年

作者： Liu, Huan Wang, Xiaobin Zhao, Changfeng Wang, Zehua Jian, Ge Dong, Haobin Liu, Zheng School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China School of Engineering University of British Columbia Okanagan Campus KelownaBCV1V 1V7 Canada

The low magnetic field measurement has been utilized since ancient times in order to find economic resources, to detect magnetic anomalies, etc. In this case, the vector magnetic survey can simultaneously obtain the modulus and direction information of the magnetic field, which can contribute to obtaining more precise information and characteristics of magnetic field resources. This paper is concerned with the potential to exploit the signals of vector magnetic field measurement with a magneto-inductive (MI) sensor. To evaluate the capability of the MI sensor, a test platform is set up and its performance including the noise floor, the resolution, the sensitivity, etc., are comprehensively characterized. Further, a comparative geomagnetic observation and magnetic anomaly detection among the proposed MI sensor, a high-precision Overhauser sensor, and a commonly used and accepted commercial MI sensor are conducted. The experimental results identify the capability of the proposed MI sensor in weak magnetic detection. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic fields

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Driver Action Recognition Based on Attention Mechanism

Driver Action Recognition Based on Attention Mechanism

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International Conference on systems and Informatics (ICSAI)

作者： Wenhao Wang Xiaobo Lu Pengguo Zhang Huibin Xie Wenbin Zeng Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation Southeast University Nanjing China Zhejiang Uniview Technologies Co. Ltd Hangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

According to the world health organization, millions of people are killed by traffic accidents worldwide every year, and more than 80 percent of accidents are caused by unsafe driving. This paper studies driver behavior recognition, aiming to standardize driver's driving behavior and reduce the probability of traffic accidents. However, the inter-class variance of drivers' different actions is small, making it difficult to identify. To improve fine-grained identification, an attention module is designed to be inserted into convolutional neural network, which consists of two parallel parts: channel level attention and space level attention. The introduction of attention mechanism can focus the weight of the network on the meaningful pixels and channels, promote the expression of effective features, and suppress the interference of noise. The experiments show that the recognition accuracy is improved after applying attention mechanism. The visualization results show that the introduction of attention mechanism can make the network focus on the prominent areas of the feature map.

关键词： Vehicles Convolutional neural networks Training Convolution Mathematical model Visualization Accidents

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Energy Analysis of a CPG-controlled Miniature Robotic Fish

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Journal of Bionic engineering 2018年第2期15卷 260-269页

作者： Junzhi Yu Shifeng Chen Zhengxing Wu Xingyu Chen Ming Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China School of lnformation and Electrical Engineering Shandong Jianzhu University Jinan 250101 China

Bionic robotic fish has a significant impact on design and control of innovative underwater robots capable of both rapid swimming and high maneuverability. This paper explores the relationship between Central Pattern Generator （CPG） based locomotion control and energy consumption of a miniature self-propelled robotic fish. To this end, a real-time energy measurement system compatible with the CPG-based locomotion control is firstly built on an embedded system. Then, tests are conducted on the untethered actual robot. The results indicate that different CPG feature parameters involving amplitude, frequency, and phase lag play distinct roles in energy consumption under different swimming gaits. Specifically, energy consumption is positively correlated with the changes in the amplitude and frequency of CPGs, whereas the phase lag of CPGs has little influence on the energy consumption. It may offer important inspiration for improving energy efficiency and locomotion performance of versatile swimming gaits.

关键词： bionic robotic fish energy analysis Central Pattern Generator （CPG） swimming motion control

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PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator

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IFAC-PapersOnLine 2018年第4期51卷 888-893页

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. © 2018

关键词： controllers Benchmarking Disturbance rejection Electric control equipment Proportional control systems Refrigeration Three term control systems Two term control systems Conditional integrators Feed forward compensation Model based method PID controllers Refrigeration system

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Primitives generation policy learning without catastrophic forgetting for robotic manipulation 19

Primitives generation policy learning without catastrophic f...

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19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

作者： Xiong, Fangzhou Liu, Zhiyong Huang, Kaizhu Yang, Xu Hussain, Amir State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science China China CAS Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Department of EEE Xi'an Jiaotong-Liverpool University China School of Computing Edinburgh Napier University United Kingdom

ISBN: (纸本)9781728146034

Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods. © 2019 IEEE.

关键词： Learning systems

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Efficient Dense Spatial Pyramid Network for Lane Detection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yinggang Li Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.

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Vehicle and Driver Detection on Highway Based on Cascade R-CNN

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yifei Geng Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.

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Towards End-to-End Face Recognition Method through Spatial Transformer

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yanfang Wu Xiaobo Lu Chen Qin School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Face recognition technology is widely applied in daily life, but in most methods, similarity or affine transformation is employed to align face images according to five facial landmarks. The face alignment module is implemented independently, thus it's difficult in end-to-end training. In this paper, the main purpose is to design a towards end-to-end trainable face recognition method based on indoor scenes. Due to that spatial transformer can implement any parametrizable transformation, we joint it with recognition network, making end-to-end training possible. Simultaneously, any prior knowledge on facial landmarks isn't required. The model jointly with spatial transformer can achieve 0.3% higher accuracy than similarity transformation. Most downsampling methods ignore the sampling theorem, making convolutional networks not shift-invariant. We replace max-pooling by MaxBlurPool in spatial transformer network, and the accuracy is improved by 0.25%.

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Distributed Cooperative Attitude Tracking for Multiple Flexible Spacecraft under a Directed Graph

Distributed Cooperative Attitude Tracking for Multiple Flexi...

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第三十八届中国控制会议

作者： Lei Wang Zhuoyue Song Housheng Su Yiqun Zou Xiafei Tang State Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology School of Automation Huazhong University of Science and Technology School of Automation Central South University School of Electrical and Information Engineering Changsha University of Science and Technology

This paper is concerned with the problem of cooperative attitude tracking and vibration reduction for multiple flexible spacecraft without modal variable measurement under a directed communication topology. Firstly, a distributed attitude synchronization control law for MRPs representation is proposed in the absence of external disturbances, where the modal information is obtained using a modal variable estimator. To deal with a more practical case that the spacecraft are affected by external disturbances, the control law is further modified by integrating a disturbance observer and a feedforward compensating scheme,in which the lumped disturbances including the external disturbances and errors of the estimated modal information are observed and compensated in finite time. Under this enhanced control scheme, the attitude tracking errors and vibration variables will converge to zero even in the presence of external disturbances, and the controller is continuous and chattering-free. Besides,compared with the existing results, the communication graph in this paper is assumed to be directed and contain a spanning tree with the virtual leader as the root node. A numerical example is illustrated to demonstrate the effectiveness of the proposed results.

关键词： Attitude tracking Multiple flexible spacecraft Directed graph External disturbance

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System and walking gait design for hexapod search and rescue robot

System and walking gait design for hexapod search and rescue...

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Chinese Intelligent systems Conference, CISC 2017

作者： Yang-Yang, Chen Yingying, Huang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Electronic Science and Engineering Southeast University Nanjing2017514 China

ISBN: (纸本)9789811064982

In order to adapt to the complex disaster environment, this paper considers the system design of hexapod search and rescue robot. Such hexapod robot is suitable to different kinds of roads and obstacle, which can avoid to the shortcomings of crawler robots. This hexapod search and rescue robot includes the six foot body, voice modular, obstacle avoidance and remote monitoring function. Based on the relationship between the center of gravity and the supporting polygon, the design of the hexagon robot’s walking gait is presented. The feasibility of the hexapod search and rescue robot is verified by the prototype experiment. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Machine design

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