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10th International Symposium on Neural Networks, ISNN 2013

作者： Chen, Guangyi Bui, Tien Dai Krzyzak, Adam Liu, Weihua Department of Computer Science and Software Engineering Concordia University 1455 de Maisonneuve West Montreal QC H3G 1M8 Canada State Key Lab. of Virtual Reality Technology and Systems Beihang University No 37 Xueyuan Rd. Haidian District Beijing ZipCode 100191 China

ISBN: (纸本)9783642390647

In the past two decades, Support Vector Machine (SVM) has become one of the most famous classification techniques. The optimal parameters in an SVM kernel are normally obtained by cross validation, which is a time-consuming process. In this paper, we propose to learn the parameters in an SVM kernel while solving the dual optimization problem. The new optimization problem can be solved iteratively as follows: (a) Fix the parameters in an SVM kernel;solve the variables α i in the dual optimization problem. (b) Fix the variables α i;solve the parameters in an SVM kernel by using the Newton-Raphson method. It can be shown that (a) can be optimized by using standard methods in training the SVM, while (b) can be solved iteratively by using the Newton-Raphson method. Experimental results conducted in this paper show that our proposed technique is feasible in practical pattern recognition applications. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Support vector machines

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Invariant object recognition using radon and fourier transforms

Invariant object recognition using radon and fourier transfo...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Chen, Guangyi Bui, Tien Dai Krzyzak, Adam Zhao, Yongjia Department of Computer Science and Software Engineering Concordia University 1455 de Maisonneuve West Montreal QC H3G 1M8 Canada State Key Lab. of Virtual Reality Technology and Systems Beihang University No 37 Xueyuan Rd. Haidian District Beijing ZipCode 100191 China

ISBN: (纸本)9783642390647

In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Object recognition

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Pursuit-evasion differential games of players with different speeds in spaces of different dimensions

arXiv

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arXiv 2022年

作者： Li, Shuai Wang, Chen Xie, Guangming The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China The National Engineering Research Center of Software Engineering Peking University Beijing100871 China

We study pursuit-evasion differential games between a faster pursuer moving in 3D space and an evader moving in a plane. We first extend the well-known Apollonius circle to 3D space, by which we construct the isochron for the considered two players. Then both cases with and without a static target are considered and the corresponding optimal strategies are derived using the concept of isochron. In order to guarantee the optimality of the proposed strategies, the value functions are given and are further proved to be the solution of Hamilton-Jacobi-Isaacs equation. Simulations with comparison between the proposed strategies and other classical strategies are carried out and the results show the optimality of the proposed strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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Path-following Control of Fish-like Robots: A Deep Reinforcement Learning Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8163-8168页

作者： Tianhao Zhang Runyu Tian Chen Wang Guangming Xie National Engineering Research Center for Software Engineering Peking University Beijing 100871 China The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing 100871 China Institute of Ocean Research Peking University Beijing 100871 China

In this paper, we propose a deep reinforcement learning (DRL) approach for path-following control of a fish-like robot. The desired path may be a randomly generated Bézier curve. First, to implement the locomotion control of the fish-like robot, we design a modified Central Pattern Generated (CPG) model, using which the fish achieves varied swimming behaviors just by adjusting a single control input. To reduce the reality gap between simulation and the physical system, using the experimental data of the real fish-like robot, we build a surrogate simulation environment, which also well balances the accuracy and the speed of training. Second, for the path-following control, we select the advantage actor-critic (A2C) approach and train the control policy in the surrogate simulation environment with a straight line as the desired path. Then the trained control policy is directly deployed on a physical fish-like robot to follow a randomly generated Bézier curve. The experimental results show that our proposed approach has good practical applicability in view of its efficiency and feasibility in controlling the physical fishlike robot. This work shows a novel and promising way to control biomimetic underwater robots in the real world.

关键词： Reinforcement learning control autonomous underwater vehicles biomimetic underwater robots path following deep learning

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Attention based Graph Covolution Networks for Intelligent Traffic Flow Analysis

Attention based Graph Covolution Networks for Intelligent Tr...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Hongxin Zhang Jiaxin Liu Ying Tang Gang Xiong State Key Lab of CAD & CG Zhejiang University China College of Software Engineering Zhejiang University of Technology China State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Predicting traffic flow is one of the fundamental issue to smart cities. However it is still challenging in vehicular cyber-physical systems because of ever-increasing urban traffic data. Most previous models for traffic flow prediction suffers the problem with variance error depends on time and location. In this paper, a novel graph convolution network (GCN) approach is proposed for predicting citywide traffic flow. Our key idea is to introduce spatial and temporal attention to the GCN model to lessen the impact of urban data complexity. A framework of flow-based graph convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose practical strategies that efficiently learn parameters of the model. Experimental results demonstrate that the proposed approach to traffic flow prediction outperforms state-of-the-art approaches.

关键词： Predictive models Data models Analytical models Convolution Urban areas Chebyshev approximation Roads

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Distributed circle formation control over directed networks with communication constraints ⁎

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IFAC-PapersOnLine 2019年第3期52卷 108-113页

作者： Peng Xu Jiayan Wen Chen Wang Guangming Xie College of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou 545006 China National Engineering Research Center for Software Engineering Peking University Beijing 100871 China The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China

This paper investigates distributed circle formation problems of multi-agent systems (MAS) subject to limited information communication under a class of weight-unbalanced directed graphs, in which the communication topology contains a directed spanning tree and each agent can only perceive the distances from itself to the nearest neighbor in counterclockwise direction as well as the counterpart in the clockwise direction. Towards this end, a novel algorithm combined with an encoder-decoder framework has been proposed. We show that, under the proposed policy, the resulting network executions can drive the states of all mobile agents to converge to some expected equilibrium point. Numerical simulation results have been given to demonstrate the effectiveness of the proposed algorithm.

关键词： Multi-agent systems Communication Bandwidth Directed Network Circle Formation Control

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalab.lity. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Multi-Heterogeneity Public Goods Evolutionary Game on Lattice

SSRN

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SSRN 2022年

作者： Xie, Kai Liu, Xingwen Wang, Huazhang Jiang, Yulian Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China College of Electrical Engineering Southwest Minzu University Sichuan Chengdu610225 China

The topic of how cooperation emerges and develops in the real world has been intensively studied. Many mechanisms and models have been proposed to explain this complex phenomenon with the idealized assumption that all players are homogeneous or have little heterogeneity. This paper investigates the evolution of the public goods game with multiple heterogeneities. Roughly speaking, the quantity of investment (by a cooperator), punishment (received by a defector), or cost (paid by a punisher) is not fixed but depends on specific evolutionary situation. Moreover, heterogeneity is also introduced in expected payoff of each player for the strategy updating rule. Multi-heterogeneity makes the considered model more realistic. The simulation experiment shows that the combination of modified punishment mechanism and strategy updating rule can promote cooperation density in both the multi-heterogeneity and homogeneity models. © 2022, The Authors. All rights reserved.

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A cloud-based taxi trace mining framework for smart city

A cloud-based taxi trace mining framework for smart city

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作者： Liu, Jin Yu, Xiao Xu, Zheng Choo, Kim-Kwang Raymond Hong, Liang Cui, Xiaohui State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Third Research Institute of Ministry of Public Security Shanghai China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Information Technology and Mathematical Science University of South Australia AdelaideSA Australia School of Information Management Wuhan University Wuhan China International School of Software Wuhan University Wuhan China

As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding;(2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark;and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalab.lity of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

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Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning

Decentralized Circle Formation Control for Fish-like Robots ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Tianhao Zhang Yueheng Li Shuai Li Qiwei Ye Chen Wang Guangming Xie The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Microsoft Research Asia Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalab.e with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.

关键词： Solid modeling Automation Simulation Conferences Reinforcement learning Manuals Aerospace electronics

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