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Chinese control Conference (CCC)

作者： Jun Cheng Min Wu Luefeng Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

A control method is proposed to suppress stick-slip vibration of drillstring system based on tracking control with zero steady-state error and state observer in this paper. A multiple degree-of-freedom (DOF) model of drillstring dynamic is presented first, which considers high-order modal of stick-slip vibration. Then, state observer is constructed to estimate the states of drillstring system, whose states are usually difficult to measure in the top of drillstring system. Finally, combing state feedback control and internal model principle, a tracking control with zero steady-state error is proposed to ensure the speed of rotary table and bit are consistent. The proposal only need top measurement, can eliminate multiple torsional modes, and has a strong robustness. Simulations show the effective of the proposal.

关键词： Vibrations Mathematical model Observers Torque Computational modeling State feedback

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Training and Testing Object Detectors With Virtual Images

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 539-546页

作者： Yonglin Tian Xuan Li Kunfeng Wang Fei-Yue Wang Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijirtg 100190 China School of Automation Beijing Institute of Technology Beijing 100081. China Qingdao Academy of Intelligent Industries Qingdao 266000 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha 410073 China IEEE

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.

关键词： Index Terms--Deep learning object detection parallel vision virtual dataset

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The transient electromagnetic inversion based on the simplex-simulated annealing algorithm

The transient electromagnetic inversion based on the simplex...

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Chinese control Conference (CCC)

作者： Guangjun Wang Caifeng Xu Gang Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

In order to avoid the linear inversion method falling into local minima and slow convergence speed of the global optimization inversion method, the article proposed the simplex-simulated annealing algorithm for transient electromagnetic inversion research which combines advantages of the simplex method and the simulated annealing algorithm. The simplex method is used to obtain local minimum value which is relatively close to the actual value, then the simulated annealing algorithm is used to obtain the global optimal solution which can better reflect structural characteristics of the real stratigraphic model. Through the comparison of the noise inversion results and noise free inversion results about K-type, H-type, KH-type and HKH-type stratigraphic models, it can be proved that the simplex-simulated annealing algorithm can suppress some noise. The comparison of the simulated annealing method and the simplex-simulated annealing algorithm shows that the simplex-simulated annealing algorithm has the characteristics of global searching ability and fast convergence speed.

关键词： Simulated annealing Transient analysis Linear programming Geology Data models Convergence

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Research on Handwritten Alphabet Recognition System Based on Extreme Learning Machine

Research on Handwritten Alphabet Recognition System Based on...

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Chinese control Conference

作者： Junlei Song Qi Liu Shengnan Tian Yi Wei Fang Jin Wenqin Mo Kaifeng Dong Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Aiming at the problems of slow recognition, low efficiency and degree of automation in handwritten letter recognition system at present, a handwritten letter recognition system based on extreme learning machine is designed in this paper. The system is implemented by mixed programming with MATLAB and visual studio, it can reads, normalize, binarize and extract the handwritten letter images. The real-time interactive recognition of handwritten letters can be realized on the basis of training the simple pictures by using the identification model of the extreme learning machine algorithm. The experimental results show that the handwriting recognition system based on extreme learning machine designed in this paper can recognize 98.82% of handwritten letters and greatly reduce learning and testing time. Compared with BP neural network and other recognition algorithms, its training times have been reduced by hundreds or even thousands of times. At the same time, there is no manual intervention in the entire learning and testing process, which improves the automation of handwriting recognition.

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A Two-level Adaptive Target Recognition and Tracking Method Based on Vision for Multi-robot System

A Two-level Adaptive Target Recognition and Tracking Method ...

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IEEE International Conference on Robotics and Biomimetics

作者： Liang Ren Zhiqiang Cao Min Tan Peng Zhao Xuechao Chen State Key Laboratory of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China The 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems BIT Beijing China

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance.

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Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Adaptive boosting

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Path Planning System for Smart Cars Used in Education

Path Planning System for Smart Cars Used in Education

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Guangyu Xiong Lulu Niu Yanfu Tian Jiehan Zhou Babak Mohajeri Timo R. Nyberg Silk Road Business School Sanya University(USY) China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Articial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Oulu Oulu Finland Aalto University Espoo Finland

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.

关键词： Education Path planning Automobiles Task analysis optimization Programming profession Matlab

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RBF Neural Network Based-PID control for Weight on Bit During Drilling Operation

RBF Neural Network Based-PID Control for Weight on Bit Durin...

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Chinese control Conference

作者： Sike Ma Min Wu Luefeng Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China

Deep drilling is a costly project and efficiency is of paramount importance. The weight on bit is one of the main operating parameters that influences the drilling efficiency and it was controlled by manual before. But after people saw the giant potential of an auto-drilling system in increasing the drilling efficiency, more and more studies on the feed back control of weight on bit have emerged. This paper mainly studied weight on bit dynamic under the variational formation based on a lumped parameter model and a self-tuning PID controller for weight on bit control. The parameters of the PID controller are tuned by using gradient descent method and RBF neural network identification.

关键词： Neural networks DC motors Feeds Suspensions Lifting equipment Torque Angular velocity

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The Distributed System of Smart Wearable Energy Harvesting Based on Human Body

The Distributed System of Smart Wearable Energy Harvesting B...

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第37届中国控制会议

作者： Yeyun Cai Fang Deng Jiachen Zhao Huangbin Qiu Xinyu Fan Zelang Liang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

Wearable devices have appeared in every aspect of our lives, from small items such as wristbands and eyeglasses to finished garments as shoes and clothing. With the promoting of scientific development, intelligent technology is also combined with wearable devices to form a unique network that depends on the human body. The increasing demand for equipment has made smart wearable devices a research hotspot. Such equipment is usually small, light-weight, good flexibility, and can be adapted to the complex use of the environment. But these advantages just become the limitations of power supply for ***, the fixed charger can not be used to charge the device during use, and the volume of such a special battery can not be increased in order to increase the storage capacity. Therefore, how to solve the problem of power suppling for the wearable device effectively is a big challenge. Based on this, the paper proposes a distributed system of smart wearable energy harvesting, which considers the human body as the core and uses a variety of ubiquitous energy sources such as solar energy, thermal energy and mechanical energy. The system makes a combination of wearable and distributed design to form a wireless body area network,and provides a stable power output for smart wearable devices.

关键词： wearable energy harvesting distributed system human body

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Off-Policy Q-Learning for Infinite Horizon LQR Problem with Unknown Dynamics

Off-Policy Q-Learning for Infinite Horizon LQR Problem with ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Xinxing Li Zhihong Peng Li Liang Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

In this paper, a novel online Q-Iearning approach is proposed to solve the Infinite Horizon Linear Quadratic Regulator (IHLQR) problem for continuous-time (CT) linear time-invariant (LMI) systems. The proposed Q-Iearning algorithm employing off-policy reinforcement learning (RL) technology improves the exploration ability of Q-Iearning to the state space. During the learning process, the Q-Iearning algorithm can be implemented just using the data sets which just contains the information of the behavior policy and the corresponding system state, thus is data- driven. Moreover, the data sets can be used repeatedly, which is computationally efficient. A mild condition on probing noise is established to ensure the converge of the proposed Q-Iearning algorithm. Simulation results demonstrate the effectiveness of the developed algorithm.

关键词： Silicon Optimal control Convergence Approximation algorithms Manganese Regulators Adaptation models

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