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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Lina Tong Qianzhi Lin Chuanlei Qin Liang Peng School of Mechanical Electronic & Information Engineering China University of Mining and Technology-Beijng Beijing China State Key Laboratory of Management and Controlfor Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426565

The number and location configuration methods of wearable sensors for human activity recognition (HAR) are analytically discussed. Based on the publicly available Daily and Sports Activities data set, a convolutional neural network (CNN) was built to recognize 19 kinds of daily and sports activities, and then the model was optimized for better performance. The results of numerous comparative experiments show that deep learning-based HAR is better than machine learning-based HAR in terms of accuracy, and its improvement in accuracy is not directly related to the increase of sensor quantity. Due to its strong capability of feature extraction, deep learning extracts not only activity-related features but also individual differences, therefore, the location with less individual randomness should be selected according to practical engineering. Moreover, the results are also influenced by the limb symmetry in the data set. Finally, the feasibility of achieving higher accuracy with fewer sensors is proved.

关键词： Deep learning Interference Activity recognition Feature extraction Data models Convolutional neural networks Informatics

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

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Science China(Information Sciences) 2019年第9期62卷 144-157页

作者： Guoxing WEN C.L.Philip CHEN Hui DOU Hongli YANG Chunfang LIU College of Science Binzhou University Department of Computer and Information Science Faculty of Science and Technology University of Macau College of Navigation Dalian Maritime University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Mathematics and Systems Science College Shandong University of Science and Technology

This paper addresses the obstacle avoidance problem of formation control for the multi-agent systems modeled by double integrator dynamics under a directed interconnection topology. The control task is finished by a leader-follower formation scheme combined with an artificial potential field(APF) *** leader-follower scheme is carried out by taking the desired trajectory with the desired velocity as virtual leader, while the APF method is carried out by dealing with the obstacles as the high potential points. When the obstacle avoidance tasks are finished, the artificial potential forces degrade the formation performance, so their undesired effects are treated as disturbances, which is analyzed by the robust H∞performance. Based on Lyapunov stability theory, it is proved that the proposed formation approach can realize the control objective. The result is also extended to the switching multi-agent formation. The effectiveness of the proposed formation scheme is further confirmed by simulation studies.

关键词： formation control obstacle avoidance artificial potential field method H_∞ performance second-order multi-agent system directed topology

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Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

Residual Squeeze-and-Excitation Network with Multi-scale Spa...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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Micro-displacement Amplifying Mechanism of a Piezoelectric Single Crystal Actuator and Its Motion Characterization

Micro-displacement Amplifying Mechanism of a Piezoelectric S...

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IEEE International Conference on Mechatronics and automation

作者： Tianlu Zhang Zhangming Du Chao Zhou Zhiqiang Cao Shuo Wang Long Cheng State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Piezoelectric single crystal actuator is useful in the micro- and nano-scaled. But the range of the operation is limited. The paper presented the amplifying mechanism of the single crystal actuator in order to amplify the output of the piezoelectric actuator in micro- and nano-applications. Due to the single crystal's expansion when the applied signals decrease, the paper developed the adjustable spring that added to the amplifying mechanism. This paper tested the characteristics of the amplifying mechanism by designed a set of experiment device. The input and output displacement of the amplifying mechanism driven by kinds of signals were measured. It can concluded that the amplifying mechanism achieve the positive and negative displacement response and show its accuracy and stable. These excellent properties of the amplifying mechanism amplify the micro-displacement and expand the operating range of single crystal actuator in micro- and nano-positioning applications.

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Nonparametric Different-Feature Selection Using Wasserstein Distance

Nonparametric Different-Feature Selection Using Wasserstein ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Wenbo Zheng Fei-Yue Wang Chao Gou 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

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

In this paper, we propose a feature selection method that characterizes the difference between two kinds of probability distributions. The key idea is to view the feature selection problem as a sparsest k-subgraph problem that considers Wasserstein distance between the studied two probability distributions. Our method does not presume any specific parametric models on the data distribution and is non-parametric. It outperforms existing Kullback-Leibler divergence based approaches, since we do not require two distributions to overlap. This relaxation makes our method work in many problems in which Kullback-Leibler divergence based methods fail. We also design a fast calculation algorithm using dynamic programming. Our experimental results show that our method outperforms the current method in both computation accuracy and speed.

关键词： Heuristic algorithms Conferences Tools Feature extraction Probability distribution Dynamic programming Parametric statistics

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Short Term Traffic Flow Forecast Based on CM-GRU Networks

Short Term Traffic Flow Forecast Based on CM-GRU Networks

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Guo, Zhongzheng Zhong, Weifeng Zhu, Fenghua Li, Xiaoshuang Wang, Fei-Yue Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728128535

Intelligent transportation systems (ITS) have developed for a long time. The rise of deep learning has brought new vitality of the ITS. However, traffic flow data is usually time-correlated and highly randomized. The data distribution will also change dynamically. To actualize the forecasting of traffic flow accurately, we use the historical traffic information to predict the messages of the traffic flow at any time interval. This paper proposes an efficient traffic flow forecast architecture based on deep learning. The method combines the gated recurrent unit (GRU, a type of recurrent neural network) layers and one-dimension convolution layers. Since the performance of these models has a strong dependence on hyper-parameters, this paper conducts a large-scale search of the hyper-parameter space. At the same time, experiments on flow data show that the method proposed in this paper can achieve a better prediction accuracy. Experiments also show lower test errors compared with the existing approaches. © 2019 IEEE.

关键词： Recurrent neural networks

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Sliding Mode control for Flexible-link Manipulators Based on Adaptive Neural Networks

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International Journal of automation and computing 2018年第2期15卷 239-248页

作者： Hong-Jun Yang Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function （RBF） neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation （PDE） model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov＇s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.

关键词： Sliding mode control adaptive control neural network flexible manipulator partial differential equation （PDE）.

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Grouping Methods for Facilitating Emergency Evacuations

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IFAC-PapersOnLine 2020年第5期53卷 845-850页

作者： Linyao Yang Ke Tan Xin Liu Xiao Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Shenzhen China

It is widely believed that the motion of crowds can be described by the “social force” model, and small group behaviors have great impacts on evacuation efficiency as a common phenomenon in pedestrian evacuations. Some previous works have modeled group behaviors in social force models by depicting group members’ interaction forces. However, the impacts of different grouping schemes have not been considered and evaluated. This paper proposes four different grouping methods to explore the evacuation dynamics of different group structured pedestrians. Results show that group evacuations based on the spatial partitioning outperform those clustering-based schemes with the larger group number. More importantly, simulations indicate that the number of groups also highly affects the evacuation efficiency, and the larger the number of groups, the more efficient the evacuation.

关键词： Evacuation Simulation Social force Clustering methods Agent based modeling

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Social Manufacturing Pattern in the Intelligent Reform of the Shoes and Clothing Industry

Social Manufacturing Pattern in the Intelligent Reform of th...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yansong Cao Yutong Wang Juwu Dai Xihao Zhu Fei-Yue Wang Macau Institute of Systems Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Ningbo Sanbody Intelligent Technology co. LTD Ningbo China Faculty of Science and Engineering University of Nottingham Ningbo China

Along with economic globalization, the shoes and clothing market is undergoing huge changes and is facing increasing individualized demands. To realize personalized customization, we propose a social manufacturing pattern for the shoes and clothing industry. On the customer side, we collect costumer information with the help of 3D scanning and virtual fitting. On the production side, we replace human labor with intelligent production equipment. On the enterprise management side, we construct a cloud platform to realize source maximization. Through this social manufacturing pattern, we expect to accelerate the intelligent reform of the shoes and clothing industry.

关键词： Product customization Cloud computing Three-dimensional displays Social networking (online) Production equipment Footwear Clothing industry

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