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16th Web information systems and Applications Conference, WISA 2019

作者： Pang, Jun Yu, Minghe Gu, Yu School of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen518061 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430065 China School of Computer Science and Engineering Northeastern University ShenyangLiaoning110819 China College of Software Northeastern University ShenyangLiaoning110819 China

ISBN: (纸本)9783030309510

A multi-graph is a set consisting of multiple graphs. Multi-graph similarity search aims to find the multi-graphs similar to the query multi-graphs from the multi-graph datasets. It plays important role in a wide range of application fields, such as finding similar drugs, searching similar molecule groups and so on. However, existing algorithms of multi-graph similarity search are memory-based algorithms, which are not suitable for the large amount of multi-graph scenarios. In this paper, we propose a parallel algorithm based on the MapReduce programming model to solve the problem of the large-scale multi-graph similarity search. Our proposed algorithm consists of two MapReduce jobs, one for indexing and the other for filtering and validation. Specially, we adapt the localization strategy to further improve the performance of our algorithm, which not only reduces the communication cost, but also mitigates the load imbalance. Extensive experimental results show that our algorithm is effective and efficient. © 2019, Springer Nature Switzerland AG.

关键词： MapReduce

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An operation network for abstractive sentence compression 27

An operation network for abstractive sentence compression

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27th International Conference on Computational Linguistics, COLING 2018

作者： Yu, Naitong Zhang, Jie Huang, Minlie Zhu, Xiaoyan State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Dept. of Computer Science and Technology Tsinghua University Beijing China Microsoft Search Technology Center

ISBN: (纸本)9781948087506

Sentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression. In this paper, we propose Operation Network, a neural network approach for abstractive sentence compression, which combines the advantages of both delete-based and generate-based sentence compression models. The central idea of Operation Network is to model the sentence compression process as an editing procedure. First, unnecessary words are deleted from the source sentence, then new words are either generated from a large vocabulary or copied directly from the source sentence. A compressed sentence can be obtained by a series of such edit operations (delete, copy and generate). Experiments show that Operation Network outperforms state-of-the-art baselines. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

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Boosting generative models by leveraging cascaded meta-models

arXiv

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arXiv 2019年

作者： Bao, Fan Su, Hang Zhu, Jun Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084

Deep generative models are effective methods of modeling data. However, it is not easy for a single generative model to faithfully capture the distributions of complex data such as images. In this paper, we propose an approach for boosting generative models, which cascades meta-models together to produce a stronger model. Any hidden variable meta-model (e.g., RBM and VAE) which supports likelihood evaluation can be leveraged. We derive a decomposable variational lower bound of the boosted model, which allows each meta-model to be trained separately and greedily. Besides, our framework can be extended to semi-supervised boosting, where the boosted model learns a joint distribution of data and labels. Finally, we combine our boosting framework with the multiplicative boosting framework, which further improves the learning power of generative models. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Backscatter-Aided Hybrid Data Offloading for Mobile Edge Computing via Deep Reinforcement Learning 4th

Backscatter-Aided Hybrid Data Offloading for Mobile Edge Com...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Xie, Yutong Xu, Zhengzhuo Xu, Jing Gong, Shimin Wang, Yi Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China SUSTech Institute of Future Networks Southern University of Science and Technology Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9783030323875

Data offloading in mobile edge computing (MEC) allows the low power IoT devices in the edge to optionally offload power-consuming computation tasks to MEC servers. In this paper, we consider a novel backscatter-aided hybrid data offloading scheme to further reduce the power consumption in data transmission. In particular, each device has a dual-mode radio that can offload data via either the conventional active RF communications or the passive backscatter communications with extreme low power consumption. The flexibility in the radio mode switching makes it more complicated to design the optimal offloading strategy, especially in a dynamic network with time-varying workload and energy supply at each device. Hence, we propose the deep reinforcement learning (DRL) framework to handle huge state space under uncertain network state information. By a simple quantization scheme, we design the learning policy in the Double Deep Q-Network (DDQN) framework, which is shown to have better stability and convergence properties. The numerical results demonstrate that the proposed DRL approach can learn and converge to the maximal energy efficiency compared with other baseline approaches. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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Recovering compressed images for automatic crack segmentation using generative models

arXiv

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arXiv 2020年

作者： Huang, Yong Zhang, Haoyu Li, Hui Wu, Stephen Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructures Ministry of Industry and Information Technology Harbin Heilongjiang150090 China Ministry of Education Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin China Institute of Statistical Mathematics Research Organization of Information and Systems 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan Graduate University for Advanced Studies SOKENDAI 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan

In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy-efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. The conventional CS method is based on the principle that, through a regularized optimization, the sparsity property of the original signals in some domain can be exploited to get the exact reconstruction with a high probability. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard for real crack images. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparing with three existing CS algorithms. Furthermore, we show that our framework is extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and *** Codes 62P30, 97M50 Copyright © 2020, The Authors. All rights reserved.

关键词： Compressed sensing

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Research on High Precision Control of Joint Position Servo System for Hydraulic Quadruped Robot

Research on High Precision Control of Joint Position Servo S...

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第三十八届中国控制会议

作者： CHEN Zhihua WANG Shoukun XU Kang WANG Junzheng ZHAO Jiangbo NIU Shanshuai Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology

Electro-hydraulic servo valve-controlled cylinder(EHSVCC) is a highly integrated and high-performance electrohydraulic servo driving unit(EHSDU), which is the basic driving unit of the leg joint for hydraulic quadruped robot. The robot impedance control mostly adopts control methods based on position inner loop and impedance outer loop, the high precision control of position inner loop is the key to improve impedance control. Therefore, it is of great significance to study a high precision position control method. Firstly, the electro-hydraulic servo drive unit of the foot robot is taken as the research object. The model of the nonlinear system is established, and the feedback linearization of the system is carried out. Secondly, in order to improve the tracking performance and anti-jamming ability of the position inner loop input/output a sliding mode variable structure robust controller(SMVSRC) with switching function, variable structure control rate and improved boundary layer function is designed. Finally, the electro-hydraulic servo drive unit(EHSDU) and the hydraulic foot robot test platform are tested, the influence of the two control algorithms on the position response and tracking error for EHSDU is analyzed and compared under the control of sinusoidal signals with different amplitudes and frequencies and diagonal gait signals. The experimental results show that the improved SMVSRC algorithm is feasible and effective, which provides a theoretical basis for the later study of impedance control based on position inner loop.

关键词： Hydraulic quadruped robot Position control Feedback linearization Sliding mode controller

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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Preface

Advances in Intelligent Systems and Computing

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Advances in intelligent systems and Computing 2021年 1081 AISC卷 vii-xvi页

作者： Atanassov, Krassimir T. Atanassova, Vassia Kacprzyk, Janusz Krawczak, Maciek Owsiński, Jan W. Sotirov, Sotir Sotirova, Evdokia Szmidt, Eulalia Zadrożny, Slawomir Department of Bioinformatics and Mathematical Modelling Bulgarian Academy of Sciences Institute of Biophysics and Biomedical Engineering Sofia Bulgaria Systems Research Institute Polish Academy of Sciences Warsaw Poland WIT-Warsaw School of Information Technology Warsaw Poland Intelligent Systems Laboratory Faculty of Technical Sciences Bourgas Bulgaria Polish Acadamy of Sciences Systems Research Institute Warsaw Poland Intelligent Systems Laboratory Prof. Assen Zlatarov University Burgas Bulgaria

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Diameter Detection of Silicon Single Crystal Based on Brain Storm Optimization Algorithm Hough Transform

Diameter Detection of Silicon Single Crystal Based on Brain ...

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第三十八届中国控制会议

作者： Lu Kang Ding Liu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In order to improve the speed and accuracy of silicon single crystal diameter detection, this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection Brain storm optimization algorithm Hough transform

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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