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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Observer-Based Robust Containment control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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Chinese control Conference (CCC)

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Nanjing PR China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry ofChina Huazhong University of Science and Technology Wuhan PR China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Multi-agent systems Robustness Observers Electronic mail State feedback Additives

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Pining control Algorithm for Complex Networks

Pining Control Algorithm for Complex Networks

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第三十八届中国控制会议

作者： Bingjun Wang Hui Liu Jiangnqiao Xu Jiaqi Liu School of Artificial Intelligence and Automation & Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology

In this paper, we quote the concept of resistance distance for Pinning control, and discuss the relationship between the upper bound of the minimum eigenvalue of the grounded Laplacian Matrix and the resistance distance of the Pinning control for one single node. Our simulation results in various networks like BA network, ER network, NW network etc. show that the ranking of the minimum eigenvalue of the grounded Laplacian Matrix is highly consistent with the ranking of resistance distance,much better than the degree ranking, which provides us another approach to estimate the node for Pinning control. In the case of controlling multiple(l) nodes, we use the iterative algorithm to calculate the grounded Laplacian Matrix for better combination of the controlled nodes, through which the computation of NP-hard problem of controlling l nodes can be greatly reduced, and the effect of the iterative algorithm are presented by simulation calculations in different networks,and the smaller l the better effect.

关键词： Complex dynamical network grounded Laplacian matrix pinning control spectral properties

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Trajectory optimisation design of robot based on artificial intelligence algorithm

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International Journal of Wireless and Mobile Computing 2019年第1期16卷 35-40页

作者： Huang, Li Zhang, Kai Hu, Wei Li, Chengcheng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan430081 China

Artificial intelligence has attracted more and more attention and has been widely used in all walks of life, especially in the education industry;artificial intelligence has gradually become the core. Aiming at the problem of robot trajectory planning in artificial intelligence, this paper applies the project teaching method to the course of artificial intelligence, regards trajectory planning as a project, analyses and studies it, uses ant colony algorithm to find the optimal planning path. Through the teaching of the project, the students will understand the ant colony algorithm more deeply. The algorithm is programmed independently to achieve the final trajectory optimisation. Students become the main body of the classroom, give full play to the initiative and enthusiasm of the students, through the operation of the project to train the students' innovative ability and cooperation ability, and improve the overall quality of the college students. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Students

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Three-dimensional spin Hall effect of light in tight focusing

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Physical Review A 2020年第2期101卷 023819-023819页

作者： Weixing Shu Chuyang Lin Jun Wu Shizhen Chen Xiaohui Ling Xinxing Zhou Hailu Luo Shuangchun Wen Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and Electronics Hunan University Changsha 410082 China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha 410081 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Physics and Electronic Engineering Hengyang Normal University Hengyang 421002 China

We theoretically propose the realization of a three-dimensional (3D) spin Hall effect (SHE) of light by tightly focusing a linearly polarized light. Owing to the spin-orbital interaction in focusing, the left and right circularly polarized components of light are partly converted into each other, acquiring opposite vortex phases. As the superposition of the vortex and the initial vortex-free light, the two polarized components exhibit spiral intensity patterns which rotate oppositely around the propagation axis. Consequently, the two spin components are distributed alternatively in both the azimuthal and the longitudinal directions, leading to a 3D array of split spin components, and the transverse intensity is also spit asynchronously. Moreover, that array is rotatable by the incident polarization, and the longitudinal spacing in the array can be controlled by the numerical aperture of the lens. The 3D SHE of light finds an additional degree of freedom available to control the spin state of the photon, which may be valuable in optical manipulation and quantum information.

关键词： Classical optics

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Protection of Large Sparse Network Through Optimal Cropped Depth-First Traversal Tree

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IEEE Transactions on Network Science and Engineering 2025年

作者： Wei, Wei Hu, Qiuyuan Zhang, Qinghui Li, Peng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Zhengzhou450001 China Henan University of Technology Henan Grain Big Data Analysis and Application Engineering Research Center Zhengzhou450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing100086 China

The robustness of the network is its ability to withstand unexpected failures. There is an urgent need to enhance the robustness of sparse networks as they are prone to breakage. Among the various ways to enhance network robustness measured by the edge connectivity of the entire network, shielding candidate edges is a popular method when redundant edges are not affordable. However, existing tree-cutting favors dense graphs with exponential cropped-tree growth of search space, but the search space only scales linearly in sparse graphs. We explore sparse-graph limitations by extending cropping to maximum depth while maintaining sublinear overhead, leveraging ancestor-node constraints and cropping-chain theorems for optimal efficiency. Compared with the optimal algorithm, the results in representative network types show that the robustness measurement of small-scale graphs can be enhanced to 100%, and the ratio of acceleration can be greater than 105. In large-scale graphs with millions of nodes, robustness can be improved to over 99%, while the time can be as low as tens of seconds. © 2013 IEEE.

关键词： Trees (mathematics)

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An improved distributed nonlinear observer for leader-following consensus via differential geometry approach

arXiv

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arXiv 2020年

作者： Xu, Haotian Wang, Jingcheng Wang, Bohui Wang, Hongyuan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an Shaanxi710021 China School of Aerospace Science and Technology Xidian University Xi'an710071 China

This paper is concerned with the leader-following output consensus problem in the framework of distributed nonlinear observers. In stead of certain hypotheses on the leader system, a group of geometric conditions is put forward to develop a novel distributed observer strategy with less conservatism, thereby definitely improving the applicability of the existing results. To be more specific, the improved distributed observer can precisely handle consensus problems for some nonlinear leader systems which are invalid for the traditional strategies with the certain assumption, such as Elastic Shaft Single Linkage Manipulator (ESSLM) systems and most of first-order nonlinear systems. We prove the sufficient conditions for the exponential stability of our distributed observer's error dynamic by proposing two pioneered lemmas to show the relationship between the maximum eigenvalues of two matrices appearing in Lyapunov type matrices. Then, a partial feedback linearization method with zero dynamic proposed in differential geometry is employed to design a purely decentralized control law for the affine nonlinear multiagent system. With this advancement, the existing results can be regarded as a specific case owing to that the followers can be chosen as an arbitrary minimum phase affine smooth nonlinear system. At last, the novel distributed observer and the improved purely decentralized control law are applied in the distributed control framework to construct a closed-loop system. We also prove the stability of closed-loop system to achieve leader-following consensus, i.e., the distributed control framework is proved to satisfy certainty equivalence principle. Our method is illustrated by ESSLM system and Van der Pol system as leader. Copyright © 2020, The Authors. All rights reserved.

关键词： Feedback linearization

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Neighborhood Interval Observer Based Coordination control for Multi-agent Systems with Disturbances ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10994-10999页

作者： Xiaoling Wang Guo-Ping Jiang Wen Yang Housheng Su Xiaofan Wang College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China and also with Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China School of Artifcial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China

This paper focuses on multi-agent systems with uncertain disturbances, in which only the bounding functions on the disturbances and the bounds on the initial state of each agent are known. By designing a neighborhood interval observer for this kind of multi-agent system, the estimation of the sum of the relative state of each agent associated with itself and its neighbors is frstly realized. Then, on the basis of these estimated information, local control algorithm is designed to drive the system to achieve bounded consensus.

关键词： Multi-agent system disturbance neighborhood interval observer bounded consensus

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