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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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A One-step Pruning-recovery Framework for Acceleration of Convolutional Neural Networks

A One-step Pruning-recovery Framework for Acceleration of Co...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Dong Wang Xiao Bai Lei Zhou Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Queensland Australia

Acceleration of convolutional neural network has received increasing attention during the past several years. Among various acceleration techniques, filter pruning has its inherent merit by effectively reducing the number of convolution filters. However, most filter pruning methods resort to tedious and time-consuming layer-by-layer pruning-recovery strategy to avoid a significant drop of accuracy. In this paper, we present an efficient filter pruning framework to solve this problem. Our method accelerates the network in one-step pruning-recovery manner with a novel optimization objective function, which achieves higher accuracy with much less cost compared with existing pruning methods. Furthermore, our method allows network compression with global filter pruning. Given a global pruning rate, it can adaptively determine the pruning rate for each single convolutional layer, while these rates are often set as hyper-parameters in previous approaches. Evaluated on VGG- 16 and ResNet-50 using ImageNet, our approach outperforms several state-of-the-art methods with less accuracy drop under the same and even much fewer floating-point operations (FLOPs).

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Continuous Control with Deep Reinforcement Learning for Mobile Robot Navigation

Continuous Control with Deep Reinforcement Learning for Mobi...

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Chinese Automation Congress (CAC)

作者： Jiaqi Xiang Qingdong Li Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing P.R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China

Autonomous navigation is one of the focuses in the field of mobile robot research. The traditional method usually consists of two parts: building the map of environment, localization of mobile robot and path planning. However, these traditional methods usually rely on high-precision sensor information. At the same time, mobile robots have no intelligent understanding of autonomous navigation. In this article, a deep reinforcement learning method, i.e. soft actor critic, is used to navigate in a mapless environment. It takes laser scanning data and information of the target as input, outputs linear velocity and angular velocity in continuous space. The simulation shows that this learning-based end-to-end autonomous navigation method can accomplish tasks as well as traditional methods.

关键词： Mobile robots Machine learning Navigation Autonomous robots Entropy Lasers

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Deep lidar odometry 4

Deep lidar odometry

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4th ISPRS Geospatial Week 2019

作者： Li, Q. Wang, C. Chen, S. Li, X. Wen, C. Cheng, M. Li, J. Fujian Key Lab. of Sensing and Comp. for Smart City and the Sch. of Info. Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Geometric and Visual Computing Group Louisiana State University LA70808 United States GeoSTARS Lab Department of Geography and Environmental Management University of Waterloo Canada

Most existing lidar odometry estimation strategies are formulated under a standard framework that includes feature selection, and pose estimation through feature matching. In this work, we present a novel pipeline called LO-Net for lidar odometry estimation from 3D lidar scanning data using deep convolutional networks. The network is trained in an end-to-end manner, it infers 6-DoF poses from the encoded sequential lidar data. Based on the new designed mask-weighted geometric constraint loss, the network automatically learns effective feature representation for the lidar odometry estimation problem, and implicitly exploits the sequential dependencies and dynamics. Experiments on benchmark datasets demonstrate that LO-Net has similar accuracy with the geometry-based approach. © Authors 2019.

关键词： Motion estimation

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All-electron periodic G0W0 implementation with numerical atomic orbital basis functions: Algorithm and benchmarks

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Physical Review Materials 2021年第1期5卷 013807-013807页

作者： Xinguo Ren Florian Merz Hong Jiang Yi Yao Markus Rampp Hermann Lederer Volker Blum Matthias Scheffler Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences 3rd South Str. 8 Beijing 100190 China Lenovo HPC Innovation Center Meitnerstr. 9 D-70563 Stuttgart Germany Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Department of Chemistry University of North Carolina Chapel Hill North Carolina 27599 USA Department of Mechanical Engineering and Materials Science Duke University Durham North Carolina 27708 USA Max Planck Computing and Data Facility Giessenbachstrasse 2 D-85748 Garching Germany Department of Chemistry Duke University Durham North Carolina 27708 USA Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 D-14195 Berlin Germany Physics Department Humboldt-Universität zu Berlin Zum Großen Windkanal 6 D-12489 Berlin Germany

We present an all-electron, periodic G0W0 implementation within the numerical atomic orbital (NAO) basis framework. A localized variant of the resolution-of-the-identity (RI) approximation is employed to significantly reduce the computational cost of evaluating and storing the two-electron Coulomb repulsion integrals. We demonstrate that the error arising from localized RI approximation can be reduced to an insignificant level by enhancing the set of auxiliary basis functions, used to expand the products of two single-particle NAOs. An efficient algorithm is introduced to deal with the Coulomb singularity in the Brillouin zone sampling that is suitable for the NAO framework. We perform systematic convergence tests and identify a set of computational parameters, which can serve as the default choice for most practical purposes. Benchmark calculations are carried out for a set of prototypical semiconductors and insulators, and compared to independent reference values obtained from an independent G0W0 implementation based on linearized augmented plane waves (LAPWs) plus high-energy localized orbitals (HLOs) basis set, as well as experimental results. With a moderate (FHI-aims tier 2) NAO basis set, our G0W0 calculations produce band gaps that typically lie in between the standard LAPW and the LAPW+HLO results. Complementing tier 2 with highly localized Slater-type orbitals (STOs), we find that the obtained band gaps show an overall convergence towards the LAPW+HLO results. The algorithms and techniques developed in this work pave the way for efficient implementations of correlated methods within the NAO framework.

关键词： Band gap Density functional calculations GW method Random phase approximation

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ESO-based adaptive robust force control of linear electric load simulator

ESO-based adaptive robust force control of linear electric l...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Fu, Zhen Wang, Shaoping Wang, Xingjian School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

ISBN: (纸本)9781785617911

Structured and unstructured uncertainties always exist in physical servo systems and degrade the tracking accuracy. In this paper, an effective method named adaptive robust control (ARC) with extended state observer (ESO) is compounded to control linear electric load simulator (LELS) which is an important equipment to exert aerodynamic load to flight actuation system. Adaptive robust control can solve the structed uncertainties problem and guarantee the global robustness. The extended state observer is constructed to estimate not only the unmeasured system states, but also the unstructured uncertainties (i.e., nonlinear friction, external disturbances, and unmodeled dynamics). Furthermore, the controller theoretically guarantees the performance of force tracking with a strong position coupling disturbance, which is the most essential reason for the extra force. Abundant comp arative simulation results a re obtain ed to certify the high-performance of the proposed control strategy. © 2018 IET Conference Publications. All rights reserved.

关键词： Force control

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Crack propagation-based remaining useful life prediction of the belleville spring in linear oscillating motor

Crack propagation-based remaining useful life prediction of ...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Li, Yuan Wang, Shaoping Shi, Jian Geng, Yixuan School of Automation Science and Electncal Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

ISBN: (纸本)9781785617911

A novel prediction model for the remaining useful life (RUL) of a Belleville spring based on crack propagation is proposed in this paper The linear oscillating motor(LOM), which directly provides short stroke reciprocating motion, is extensively utilized in linear actuation systems. Belleville springs are used for mechanical resonance to counteract mover kinetic energy, and prove to be the vital component of LOM to achieve high efficiency. Failure resulting of Belleville springs from fatigue is the main failure mode of LOM in high-frequency applications. From fatigue crack initiation to complete failure, the timely and accurate prediction of Belleville spring's RUL has important significance on motor 'erformance evaluation and flight safety especially for aircrafts Linear Electro-Hydraulic Actuator (LEHA). The Paris formula is used to describe the propagation velocity and the damage process is cumulated by the Miner rule. The RUL of different configuration and material Belleville springs can be predicted by the proposed method, which improves the design of LOM. © 2018 IET Conference Publications. All rights reserved.

关键词： Kinetic energy

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Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint

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Journal of Computer science & Technology 2018年第5期33卷 966-983页

作者： Ying-Lin Zhao Jian-Lei Yang Wei-Sheng Zhao Aida Todri-Sanial Yuan-Qing Cheng Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China Qingdao Research Institute Beihang University Qingdao 266041 China School of Computer Science and Engineering Beihang University Beijing 100191 China Laboratory of Informaties Robotics and Mieroeleetronics University of Montpellier Montpellier 34095 France National Center for Scientific Research MontpeUier 34095 France

Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise （PSN） interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network （PDN） solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.

关键词： MPSoCs power supply noise （PSN） power delivery network （PDN） task scheduling algorithm temperature 3D

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GECAM Localization of High Energy Transients and the Systematic Error

arXiv

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arXiv 2022年

作者： Zhao, Yi Xue, Wang-Chen Xiong, Shao-Lin Wang, Yuan-Hao Liu, Jia-Cong Luo, Qi Zhang, Yan-Qiu Sun, Jian-Chao Zhao, Xiao-Yun Cai, Ce Xiao, Shuo Huang, Yue Li, Xiao-Bo Zhang, Zhen Liao, Jin-Yuan Yang, Sheng Qiao, Rui Guo, Dong-Ya Zheng, Chao Yi, Qi-Bin Xie, Sheng-Lun Guo, Zhi-Wei Li, Chao-Yang Wang, Chen-Wei Tan, Wen-Jun Wang, Yue Peng, Wen-Xi Zheng, Shi-Jie He, Jian-Jian Wang, Ping Wang, Jin Ma, Xiang Song, Xin-Ying Zhang, Hong-Mei Li, Bing Zhang, Peng Wu, Hong Du, Yan-Qi Liang, Jing Zhao, Guo-Ying Li, Xin-Qiao Wen, Xiang-Yang An, Zheng-Hua Sun, Xi-Lei Xu, Yan-Bing Zhang, Fan Zhang, Da-Li Gong, Ke Liu, Ya-Qing Liang, Xiao-Hua Liu, Xiao-Jing Gao, Min Wang, Jin-Zhou Song, Li-Ming Chen, Gang Zhang, Ke-Ke Han, Xing-Bo Wu, Hai-Yan Hu, Tai Geng, Hao Lu, Fang-Jun Zhang, Shu Zhang, Shuang-Nan Lu, Gao-Peng Zeng, Ming Yu, Heng Department of Astronomy Beijing Normal University Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China College of Physics Hebei Normal University 20 South Erhuan Road Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China Institute of Astrophysics Central China Normal University Wuhan430079 China College of Physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Province Baoding City071002 China Physics and Space Science College China West Normal University Nanchong637002 China College of Electronic and information Engineering Tongji University Shanghai201804 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China Ministry of Education Beijing100084 China Department of Engineering Physics Tsinghua University Beijing100084 China

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is ∼ 2.5◦. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

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Performance analysis of meehanieal seal in aviation axial piston pump

Performance analysis of meehanieal seal in aviation axial pi...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Wang, Jie Zhang, Chao Wang, Shaoping School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

ISBN: (纸本)9781785617911

Performance of mechanical seal in piston pump is directly affected by thermal elastohydrodynamic lubrication An accurate analysis of performance provides with theoretical foundation for further researches on its failure mechanism and remaining useful life. It's generally acknowledged that the oil wedge reflects the performance of seal. Researches on this field normally preset the thickness distribution of oil wedge and then continue study. But hypothetic wedge cannot display the real condition. In order to solve this problem, an original method is put forward in this paper depending on as less empirical equations as possible. Numerical simulation verifies the proposed models and obtains the results consisting of thickness of oil wedge, leakage rate and torque in different conditions, which in turn verifies the availability of the method if there are subsequent physical experiments. It's equally important that combining with finite element method, the proposed method can be appfied to other types of mechanical seals which are made of different materials or having different structures. © 2018 IET Conference Publications. All rights reserved.

关键词： Numerical methods

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