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An Overlapping Community Detection Based Multi-Objective Evolutionary Algorithm for Diversified Social Influence Maximization

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An Overlapping Community Detection Based Multi-Objective Evo...

Congress on Evolutionary Computation

作者： Lei Zhang Fengiiao Sun Fan Cheng Haiping Ma Xiaoyan Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes' categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node's additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the tradeoff parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.

关键词： Optimization Encoding

Predefined-time terminal sliding mode control of robot manipulators

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arXiv 2020年

作者： Liang, Chang-Duo Ge, Ming-Feng Liu, Zhi-Wei Wang, Yan-Wu Karimi, Hamid Reza School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy

In this paper, we present a new terminal sliding mode control to achieve predefined-time stability of robot manipulators. The proposed control is developed based on a novel predefined-time terminal sliding mode (PTSM) surface, on which the states are forced to reach the origin in a predefined time, i.e., the settling time is independent to the initial condition and can be explicitly user-defined via adjusting some specific parameters called the predefined-time parameters. It is also demonstrated that the proposed control can provide satisfactory steady-state performance in the case of both external disturbances and parametric uncertainties. Besides, we present a formal systemic analysis method to derive the sufficient conditions for guaranteeing the predefined-time convergence of the closed-loop system. Finally, the effectiveness and performance of the presented control scheme are illustrated through both theoretical comparisons and numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sliding mode control

Finite-size scaling of geometric renormalization flows in complex networks

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arXiv 2020年

作者： Chen, Dan Su, Housheng Wang, Xiaofan Pan, Gui-Jun Chen, Guanrong School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Department of Automation Shanghai University Shanghai200072 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but only depends on the small-world properties of the network, namely, all networks located in the small-world phase have a uniform scaling exponent, while those located in the non-small-world phase and in their critical regions have another uniform scaling. More importantly, we perform extensive experiments on real evolutionary networks with small-world characteristics, and our results show that these observables also have uniform scaling in their geometric renormalization flows. Therefore, in a sense this exponent can be used as an effective measure for classifying universal small-world and non-small-world network models. © 2020, CC BY-SA.

关键词： Geometry

A Multi-Criteria Optimal Operation Framework for Renewable Energy Integrated Data Center Microgrid with Waste Heat Recovery

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A Multi-Criteria Optimal Operation Framework for Renewable E...

Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Yuanzheng Li Jingjing Huang Yun Liu Hao Wang Yongzhen Wang Xiaomeng Ai School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China School of Electric Power Engineering South China University of Technology Guangzhou China Monash University Melbourne VIC Australia Energy Internet Research Institute Tsinghua University Beijing China School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728186337

The rapid development of data centers (DCs) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish data center microgrid (DCMG) with renewable energy generation and waste heat recovery system to address these issues. However, the efficient energy management of DCMG is to be pursued. In this paper, we propose a multi-criteria optimal operation framework for the DCMG, considering coordinated scheduling of energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multi-criteria optimization (MCO) approach. Then, we adopt the augmented e-constraint algorithm to address the corresponding MCO problem. Afterwards, a balanced decision making method is further proposed to determine the optimal scheduling solution. Finally, case study and results analysis verify the effectiveness of proposed multi-criteria optimal operation framework for the DCMG.

关键词： Data centers Renewable energy sources Uncertainty Supply and demand Simulation Decision making Optimal scheduling

Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer operating systems

dense feature aggregation and pruning for RGBT tracking

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arXiv 2019年

作者： Zhu, Yabin Li, Chenglong Luo, Bin Tang, Jin Wang, Xiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei Anhui China

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

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Robotic Electrospinning Actuated by Non-Circular Joint Conti...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Zicong Wu Chuqian Lou Zhu Jin Shaoping Huang Ning Liu Yun Zou Mirko Kovac Anzhu Gao Guang-Zhong Yang Shanghai Jiao Tong University Shanghai P. R. China Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology Precision Robotics (Hong Kong) Limited Hong Kong China Aerial Robotics Laboratory Imperial College London London United Kingdom Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai P. R. China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

关键词： Drugs Electric potential Fluids Tissue engineering Medical treatment Bending Manipulators

Structural Performance Analysis of Wall Climbing Robot 2

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Structural Performance Analysis of Wall Climbing Robot

2019 2nd International Conference on Advanced Materials, intelligent Manufacturing and Automation, AMIMA 2019

作者： Huang, Li Liao, Shangchun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot. © Published under licence by IOP Publishing Ltd.

关键词： Robot applications

Infrared Dim and Small Target Detection and Tracking Based on Single Multi-Frame Algorithm under Sea Clutter Background

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Infrared Dim and Small Target Detection and Tracking Based o...

第三十八届中国控制会议

作者： Ting Zhao Taoshen Wang Yaoxin Cao Yunze Cai Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai Jiao Tong University Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology

Infrared dim and small target detection and track under sea clutter is significant and crucial in the infrared image process. It is a difficult task because of the disturbing clutter, the changing scenario and weak targets. In this paper, a complete process to detect and track dim small targets is proposed. Firstly, we use Mean and 3 D Filtering with Block-Matching(M3 DBM)algorithm to denoise infrared images. Inspired by the well-known BM3 D algorithm and NLM algorithm, M3 DBM can keep small targets from impair while denoising. Then, we use Butterworth high-pass filter and morphological filter to extract possible targets from single frame image. Finally, an optimal pipeline filtering algorithm(OPF) based on target position prediction and multi-target stable track is proposed. Optimal means that choose the best pipe for target and vice versa. OPF can track targets stably and accurately. The experimental results show that the proposed algorithm achieves good results in both accuracy and efficiency.

关键词： Infrared dim and small targets Sea clutter background M3DBM denoising Butterworth high-pass morphological Optimal pipeline filtering