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2018 Chinese Automation Congress, CAC 2018

作者： Han, Xiao Hu, Fang Xiong, Gang Liu, Xiwei Gong, Xiaoyan Niu, Xiaojie Shi, Wanruo Wang, Xinzhu Qingdao Institute of Education Sciences Qingdao China Qingdao No.2 Middle School of Shandong Province Qingdao China Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Faculty of Education Beijing Normal University Institute of Educational Technology Beijing China

ISBN: (纸本)9781728113128

We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things;therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality. © 2018 IEEE.

关键词： Curricula

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhao, Xiaoxuan Wang, Haoyu Wang, Xiujuan Lewlomphaisarl, Udom Li, Dong Hua, Jing Kang, Mengzhen 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

ISBN: (纸本)9781665465335

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. © 2022 IEEE.

关键词： Greenhouses

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Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning

Dual adaptive control of bimanual manipulation with online f...

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IEEE International Symposium on intelligent control (ISIC)

作者： Alex Smith Chenguang Yang Hongbin Ma Phil Culverhouse Angelo Cangelosi Etienne Burdet Center for Robotics and Neural Systems Plymouth University Devon UK Key Laboratory of Autonomous System and Network Control South China University of Technology Guangzhou P. R. China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China Department of Bioengineering Imperial College London UK

A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tuning gains which affect the control performance and control effort of the controller, which would conventionally be set to a static value based on expert knowledge of the controller; the aim of this being to avoid the use of arbitary values to set these values. A simulated experiment is carried out, where the Baxter robot is required to move an object through a trajectory while subjected to two different disturbance forces in four phases. The controller with fuzzy inferred control gains is compared against the same controller with fixed gains to gauge the effectiveness of the new method. Results show that fuzzy inference of control gains impart an improvement in both tracking error and control effort.

关键词： Trajectory Fuzzy logic Force Dynamics Mathematical model Manipulators

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SVD based scale transform invariant observable degree for LTI system

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Science China(Information Sciences) 2021年第3期64卷 246-248页

作者： Quanbo GE Peng ZHUO Hongli HE Zhentao HU Zhansheng DUAN Junzhi YU Shenzhen Institute of Guangdong Ocean University School of Automation Hangzhou Dianzi University Test Institute Chinese Flight Test Establishment College of Computer and Information Engineering Henan University The Center for Information Engineering Science Research Xi'an Jiaotong University State Key Laboratory of Complex System Management and Control Institute of AutomationChinese Academy of Sciences

Dear editor,The basic concept of observability is used to express the possibility to recover the state from measurements in modern control theory [1–3]. It is a qualitative parameter,which cannot reflect the observable degree(OD) ability [4,5].Therefore, the OD is presented to quantitatively obtain the function. The quantitative parameter is effective for indicating the exact degree of estimation ability.

关键词： observability matrix observable degree singular value decomposition scale transform invariance linear system linear relation

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Feature Aggregation Attention Network for Single Image Dehazing

Feature Aggregation Attention Network for Single Image Dehaz...

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IEEE International Conference on Image Processing

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

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures.

关键词： Convolution Atmospheric modeling Kernel Image color analysis Task analysis Training

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A Collision-Free Person-Following Approach Based on Path Planning

A Collision-Free Person-Following Approach Based on Path Pla...

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IEEE International Conference on Mechatronics and Automation

作者： Lei Pang Zhiqiang Cao Junzhi Yu Weimin Zhang Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence University of Chinese Academy of Sciences BIC-ESAT College of Engineering Peking University Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach.

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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IEEE International Conference on Networking, Sensing and control

作者： Ying Tang Shengtao Sun Ben Wu Rowan University Glassboro USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.

关键词： Training Surface reconstruction Three-dimensional displays Databases Knowledge based systems Feature extraction Strain

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LoRa on the Move: Performance Evaluation of LoRa in V2X Communications

LoRa on the Move: Performance Evaluation of LoRa in V2X Comm...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Li, Yuke Han, Shuangshuang Yang, Linyao Wang, Fei-Yue Zhang, Hui State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Qingdao Huituo Intelligent Machine Company Qingdao China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Computer Science Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Conviva Inc. Silicon ValleyCA94404 United States

ISBN: (纸本)9781538644522

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks. © 2018 IEEE.

关键词： Monte Carlo methods

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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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