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第36届中国控制会议

作者： Sheng Du Min Wu Xin Chen Weihua Cao Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

ISBN: (纸本)9781538629185

Sinter ore is the raw material of the iron and steel production.A sintering production process is with high energy consumption and large CO *** is important to achieve the green manufacturing of the sinter *** this paper,an optimization and control system for the carbon efficiency(OCSCE) in the green manufacturing of the sinter ore is designed from the point of view of the maximum utilization of the carbon and the best protection of environment in the sintering *** OCSCE has three parts:the optimization for the carbon efficiency,the coordinated optimization and control for the production phases and the comprehensive performance evaluation for the carbon *** optimization for the carbon efficiency is used to optimize the state *** coordinated optimization and control for the production phases is applied to optimize and control the operation *** comprehensive performance evaluation for the carbon efficiency assesses whether the carbon efficiency of the whole sintering process is consistent with green ***,an implementation scheme of the OCSCE is put forward for the industrial *** results of preliminary tests show that the OCSCE is in line with the needs of the industrial site,and it will have a significant performance after the OCSCE is put into the industrial site.

关键词： Carbon Efficiency Sintering Process Optimization Evaluation Hierarchical Structure

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Forest Representation Learning with Multiscale Contour Feature Learning for Leaf Cultivar Classification

Forest Representation Learning with Multiscale Contour Featu...

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IEEE International Conference on Bioinformatics and Biomedicine

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

ISBN: (纸本)9781728118680

Automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. However, deep neural networks based leaf cultivar classification model can not deeply mine the contour features of leaf images. Besides, the deep neural networks are very data-hungry due to the large complexity of the models, which means that the model's performance can decrease significantly when the size of the training data decreases. In this paper, we propose the leaf cultivar classification model based on forest representation learning and multiscale contour feature learning. Firstly, we build the multiscale contour feature learning to extract the contour features of leaf images. Then, the original input features are transformed by the data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs the data of dimensionality reduction on the features. Finally, we use the improved deep forest algorithm and autoencoder by forest to build forest representation learning for classification. The experimental results show that the proposed algorithm has higher performance than the original deep forest (gcForest) algorithm and other existing start-of-art algorithms, and has higher performance and efficiency than other deep learning algorithms.

关键词： Leaf Cultivar Classification Deep Learning Deep Forest AutoEncoder by Forest Data Augmentation contour Forest Learning plant species foliage Botanists Taxonomy dimensionality reduction algorithms cultivars HIGH-PERFORMANCE

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From AR to AI: Augmentation Technology for intelligent Surgery and Medical Treatments

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IFAC-PapersOnLine 2020年第5期53卷 792-796页

作者： Mei Zhang Zhicheng Zhang Xiao Wang Hui Yu Yifan Xia Kanran Tan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Qingdao Academy of Intelligent Industries Qingdao 266109 China The Department of the 7th Medical Center of PLA General Hospital Beijing 100700 China The Department of Computer Science Purdue University West Lafayette Indiana 47906 USA The School of Creative Technologies University of Portsmouth UK The Department of Computer Science Johns Hopkins University Baltimore Maryland USA

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more information beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly determine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly deformable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency.

关键词： Potential impact of automation open problems Augmented reality Medical treatment Parallel theory 3D technology

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ADP-Based Model Reference Adaptive control Design for Unknown Discrete-Time Nonlinear systems

ADP-Based Model Reference Adaptive Control Design for Unknow...

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第37届中国控制会议

作者： Wei Wang Xin Chen Fang Wang Hao Fu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Electrical and Information Engineering Zhengzhou University of Light Industry

In engineering applications, it is very useful to make a nonlinear system behave like a linear stable system. However,how to design a model reference adaptive control（MRAC） as the nonlinear dynamics is unknown is the key problem. In this study,an MRAC with adaptive dynamic programming（ADP） algorithm is presented for discrete-time nonlinear unknown dynamic systems, in which a multi-layer neural network（NN） model is utilized to describe the nonlinear system’s dynamics. Then based on this approximate model, a feedforward neuro-controller is developed as the desired control corresponding to the reference input. Third, the iterative ADP algorithm, in the form of actor-critic framework, employs two NNs to estimate the action value function, and generates the feedback control which, together with the feedforward neuro-controller, makes the state of the system track the reference model. Finally the feasibility of the new approach is verified by two numerical experiments.

关键词： Model reference adaptive control Adaptive dynamic programming Nonlinear system Q-function

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An Initiative Service Method with Regard to Degree of Sleepiness for Drinking Service Robot

An Initiative Service Method with Regard to Degree of Sleepi...

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Chinese control Conference

作者： Zhen-Tao Liu Si-Han Li Wei-Hua Cao Man Hao Lin-Bo Xian Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Wuhan WXYZ Technologies Co. Ltd. Wuhan China

To ensure the efficiency of people's work while working long hours, an initiative service method with regard to degree of sleepiness for drinking service robot is proposed. It can recognize the degree of sleepiness and provide initiative service to increase the efficiency of people's work. Degree of sleepiness is defined as three levels by observer rating, which is supposed to be associated with human actions, environmental factors, and individual factors. In addition, a model of degree of sleepiness based on Finite State Machines (FSM) is introduced. The relationship between human demands and degree of sleepiness is established through demand analysis model by which the initiative service with regard to degree of sleepiness for drinking service robot is achieved. Experiments on real initiative drinking service are performed in a multimodal emotional communication based on human-robot interaction (MEC-HRI) system, from which the experimental results show that the time of initiative service is 8 minutes earlier than that without initiative service. In prospect, the proposal could be applied to other kinds of services such as health care, safety driving assistance, and other safety work assistance.

关键词： Sleep Service robots Analytical models Environmental factors Temperature Brain modeling

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Convolutional ordinal regression forest for image ordinal estimation

arXiv

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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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Current controller Based on Recurrent Neural Network for Grid-Connected Converter

Current Controller Based on Recurrent Neural Network for Gri...

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第37届中国控制会议

作者： Xuefen Wang Hongbin Wang Qingyi Wang Min Ding Quan Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Corporation of China School of Automation Huazhong University of science and technology

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q vector control mechanism. To mitigate these limitations, a RNN vector controller trained with Levenberg-Marquardt and FATT（Forward accumulation through time） algorithm is designed. The simulation is researched by using MATLAB software, and the results show that training neuralnetwork algorithm is effective and the system using RNN vector control method outperforms the system using conventional PI control method under low sampling rate conditions.

关键词： Grid-connected converter RNN controller LM plus FATT algorithm Simulation

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Analysis and Maximum Efficiency Design for Wireless Power Transfer systems

Analysis and Maximum Efficiency Design for Wireless Power Tr...

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第37届中国控制会议

作者： Bing Cao Hongbin Wang Qingyi Wang Min Ding Quan Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Corporation of China School of Automation Huazhong University of science and technology

How to stabilize the output voltage while ensuring the maximum charging efficiency of the system is a problem faced by the development of wireless power transfer(WPT). Especially in the dynamic environment, the change of system coupling coefficient or load resistance will have a huge impact on the system. In this paper, a two-stage DC-DC converter control mode is used to control the system with the goal of dynamic charging demand. Among them, the system achieves impedance matching through the receiver DC-DC converter to ensure the transmission efficiency of the system, and in order to match the impedance,the voltage and current information in the coil is used to calculate the coupling coefficient, then, the coupling coefficient can be used to achieve maximum efficiency. The system output voltage is stabilized by using the transmitter DC-DC converter. The simulation experiment shows that the control mode can stabilize the output voltage well, and is more efficient than the single receiver closed loop control(SRCLP) system.

关键词： Wireless Power Transfer Magnetic Resonance Efficiency Maximization

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A Wearable Finger Exoskeleton Based on the Crank-Rocker Mechanism for Rehabilitation 29

A Wearable Finger Exoskeleton Based on the Crank-Rocker Mech...

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29th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2018

作者： Ke, Ang Huang, Jian Chen, Luyao He, Jiping Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan Hubei China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology 5 South Zhongguancun Street Beijing China

ISBN: (纸本)9781538667927

Rehabilitation of the grasp function of hands is critical for improving the quality of living for those who suffer from lost or weakened hand abilities due to neuromuscular disease. Current finger rehabilitation devices usually need a complex mechanical structure or a larger actuation system, which reduce the portability of the devices for daily living rehabilitation. In this work, we introduced the developing an ultra-light integrated modular finger exoskeleton that driven by a crank-rocker mechanism. The actuator for each joint is directly assembled at each joint by simplifying the structure design, which makes the device portable for patients. All of the parts are manufactured by 3D printing thus to reduce the initial moment of the whole device, the actuator we used is also the ultra-light server motor. The weight of the whole device is just about 60g (including 3 actuators). We analysised the kinematic of the exoskeleton, then a real-Time embedded position tracking control algorithm is implemented on an Arduino board, which is used as the controller. Experimental results show that our device is capable of both positive and active training for finger habilitation. © 2018 IEEE.

关键词： Actuators

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Differential-Evolution-Based Generative Adversarial Networks for Edge Detection

Differential-Evolution-Based Generative Adversarial Networks...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Wenbo Zheng Chao Gou Lan Yan 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 Intelligent Systems Engineering Sun Yat-sen University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Since objects in natural scenarios possess various scales and aspect ratios, learning the rich edge information is very critical for vision-based tasks. Conventional generative adversarial networks (GANs) based methods for edge detection don't perform so well due to model collapse. In order to capture rich edge information and avoid model collapse as much as possible, we consider the learning of GANs as an evolutionary optimization and propose a novel method termed as differential-evolution-based generative adversarial networks (DEGAN) for richer edge detection. In particular, built upon GANs structure, we introduce an improved differential evolutionary algorithm to refine the input of generator, with fitness function evaluated by the discriminator. Experimental results on the well-known BSDS500 and NYUD benchmarks indicate our proposed DEGAN can achieve state-of-the-art performance while retaining a fast speed and validate its simplicity, effectiveness, and efficiency. The high quality of our results on edge detection with proposed DEGAN may promise to make other vision-based tasks work better.

关键词： Image edge detection Generators Gallium nitride Training Generative adversarial networks Evolutionary computation Evolution (biology)

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