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27th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2019

作者： Nie, Xiaoying Hu, Yong Su, Zhiyuan Shen, Xukun State Key Laboratory of Virtual Reality Technology and System Beihang University China School of New Media Art and Design Beihang University China

ISBN: (纸本)9783038680994

We propose a novel method to reconstruct fluid's volume movement and surface details from just a monocular video for the first time. Although many monocular video-based reconstruction methods have been developed, the reconstructed results are merely one layer of geometry surface and lack physically correct volume particles' attribute and movement. To reconstruct 3D fluid volume, we define two kinds of particles, the target particles and the fluid particles. The target particles are extracted from the height field of water surface which is recovered by Shape from Shading (SFS) method. The fluid particles represent the discrete form of the 3D fluid volume and conform to the flow hydrodynamic properties. The target particles are used to guide the physical simulation of fluid particles based on the Smoothed Particle Hydrodynamics (SPH) model. To formulate this guidance, a new external force scheme is designed based on distance and relative motion between target particles and fluid particles. Additionally, in order to integrate and maintain geometric and physical features simultaneously, we adopt a two-scale decomposition strategy for the height field, and only apply the low frequency coarse-scale component to estimate the volumetric motion of liquid, while serve high frequency fine-scale component as noise to preserve fluid surface details in the stage of rendering. Our experimental results compare favorably to the state-of-the-art in terms of global fluid volume motion features and fluid surface details and demonstrate our approach can achieve desirable and pleasing effects. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Hydrodynamics

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Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

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Physical Review C 2022年第3期105卷 034611-034611页

作者： X. Zhang (张鑫) X. Liu (刘星泉) Y. Huang (黄宇) W. Lin (林炜平) H. Zheng (郑华) R. Wada A. Bonasera Z. Chen (陈志强) L. Chen (陈蕾) J. Han (韩纪锋) R. Han (韩瑞) M. Huang (黄美容) Q. Hu (胡强) Q. Leng (冷强钟) C. W. Ma (马春旺) G. Qu (曲国峰) P. Ren (任培培) G. Tian (田国玉) Z. Xu (徐子虚) Z. Yang (杨振雷) L. Zhang (张丽娜) Key Laboratory of Radiation Physics and Technology of the Ministry of Education Sichuan University Chengdu 610064 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA Laboratori Nazionali del Sud INFN via Santa Sofia 62 95123 Catania Italy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China School of Physics Henan Normal University Xinxiang 453007 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China School of Information Sciences and Engineering Lanzhou University Lanzhou 730000 China

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.

关键词： Low & intermediate energy heavy-ion reactions Convolutional neural networks Machine learning Nuclear data analysis & compilation

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China's greenhouse gas budget during 2000-2023

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National Science Review 2025年第4期 24-36页

作者： Wenping Yuan Minqi Liang Yuanyi Gao Ling Huang Li Dan Hongtao Duan Songbai Hong Fei Jiang Weimin Ju Tingting Li Ziyang Lou Shilong Luan Xiao Lu Zhangcai Qin Lishan Ran Lulu Shen Fei Teng Xiangjun Tian Yilong Wang Jing Wei Jiangzhou Xia Xiaosheng Xia Lijun Yu Xu Yue Haicheng Zhang Wen Zhang Yuzhong Zhang Xu Zhao Qiuan Zhu Shilong Piao Xuhui Wang Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Lake and Watershed Science for Water Security Nanjing Institute of Geography and LimnologyChinese Academy of Sciences State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of Sciences School of Urban Planning and Design Shenzhen Graduate School Peking University Frontiers Science Center for Critical Earth Material Cycling Nanjing University Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of Sciences Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery School of Environmental Science & Engineering Shanghai Jiao Tong University Department of Geography The University of Hong Kong Department of Atmospheric and Oceanic Sciences School of PhysicsPeking University Institute of Energy Environment and Economy Tsinghua University State Key Laboratory of Tibetan Plateau Earth System Resources and Environment (TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of Sciences Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric PhysicsChinese Academy of Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of

National greenhouse gas（GHG） budget,including CO2,CH4and N2O has increasingly become a topic of concern in international climate *** is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate *** estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in *** study used comprehensive methods,including emission factor methods,process-based models,atmospheric inversions,and data-driven models,to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000-2023,and produced an up-to-date China GHG Budget dataset（CNGHG）.The total gross emissions of the three GHGs show a 3-fold increase from 5.0（95% CI:4.9-5.1） Gt CO2-eq yr-1（in 2000） to 14.3（95% CI:13.8-14.8） Gt CO2-eqyr-1（in2023）.CO2emissions represented 81.8% of the GHG emissions in 2023,while 12.7% and 5.5% were for CH4and N2O,*** the largest CO2source,the energy sector contributed 87.4% *** contrast,the agriculture,forestry and other land use sector was the largest sector of CH4and N2O,representing 50.1% and 6 6.3% emissions,***,China's terrestrial ecosystems serve as a net CO2sink(1.0 Gt CO2yr-1,9596 CI:0.2-1.9 Gt CO2yr-1) during 2012 to 2021,equivalent to an average of 14.3% of fossil *** GHG emission estimates showed a general consistency with national GHG inventories,with gridded and sector-specific estimates of GHG fluxes over China,providing the basis for curtailing GHG emissions for each region and sector.

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Deep neural network based interpolation of sparse samples for time-dense power load forecasting 4

Deep neural network based interpolation of sparse samples fo...

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Li, Bo Zhao, Ruifeng Lu, Jiangang Xin, Kuo Huang, Jinhua Lin, Guanqiang Chen, Jinrong Pang, Xueyue Wu, Shunxin Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Dispatching and Control Center of China Southern Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Huizhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Huizhou China Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangdong Guangzhou China School of Electrical Engineering Chongqing University State Key Laboratory of Power Equipment & System Security and New Technology Chongqing China

ISBN: (纸本)9798350347548

Large amount of power load data with high-sampling rate plays a crucial role in the training of time-dense power load forecasting network. However, in many cases, there is a large amount of data with low-sampling rate, which can't directly train a time-dense forecasting network. Therefore, this paper proposes a deep learning based dataset interpolation method, which uses the available high-sampling rate power load datasets to train a dataset interpolation network, then interpolates the low - sampling rate datasets with trained network to obtain high-sampling rate datasets, and finally uses the interpolated datasets for time-dense forecasting network training. The results show that the data obtained based on the method proposed in this paper fits the ground-truth closer than the traditional interpolation, and the results of the network trained based on this dataset are more accurate in the subsequent forecasting tasks. © 2023 IEEE.

关键词： Interpolation

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Surrogate representation of sink strengths and the long-term role of crystalline interfaces in the development of irradiation-induced bubbles

arXiv

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arXiv 2020年

作者： Luo, Jing Xin, Yong Zhou, Zhengcheng Zhu, Yichao Guo, Xu Department of Engineering Mechanics Dalian University of Technology Dalian116023 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology China International Research Center for Computational Mechanics Dalian University of Technology China

The present article addresses an early-stage attempt on replacing the analyticity-based sink strength terms in rate equations by surrogate models of machine learning representation. Here we emphasise, in the context of multiscale modelling, a combinative use of machine learning with scale analysis, through which a set of fine-resolution problems of partial differential equations describing the (quasi-steady) short-range individual sink behaviour can be asymptotically sorted out from the mean-field kinetics. Hence the training of machine learning is restrictively oriented, that is, to express the local and already identified, but analytically unavailable nonlinear functional relationships between the sink strengths and other local continuum field quantities. With the trained models, one is enabled to quantitatively investigate the biased effect shown by a void/bubble being a point defect sink, and the results are compared with existing ones over well-studied scenarios. Moreover, the faster diffusive mechanisms on crystalline interfaces are distinguishingly modelled by locally planar rate equations, and their linkages with rate equations for bulk diffusion are formulated through derivative jumps of point defect concentrations across the interfaces. Thus the distinctive role of crystalline interfaces as partial sinks and quick diffusive channels can be investigated. Methodologicalwise, the present treatment is also applicable for studying more complicated situation of long-term sink behaviour observed in irradiated materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Optimization design of the reactor coolant pump flywheel comprised of multi-ring packing heavy metal alloy

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Journal of Physics: Conference Series 2021年第1期1777卷

作者： L Jiang C W Wu X J Shao Y Zhang M D Yu National Key Laboratory of Science and Technology on Reactor System Design Technology Nuclear Power Institute of China Chengdu 610041 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian 116024 China

To improve energy storage performance of the multi-ring RCP flywheel comprised of inner hub, tungsten alloy ring and outer retainer, optimization design process for the radial thicknesses of components and interference fit assembly is demonstrated. Radial thicknesses of these components and magnitude of interference fit are set as design parameters to construct parametric analysis model. design of experiments (DOE) technique is adopted to generate design matrix for sensitive analysis. Subsequently, radial basis function (RBF) approximation is employed to search for the optimal design considering structural integrity requirement. Finally, a proper range of the magnitude of interference fit is obtained which can not only prevent neither slippage nor separation between the inner hub and tungsten alloy ring but also ensure the safety of structural strength. In addition, it is found that to ensure a high moment of inertia and energy density, a reasonable large radial thickness of tungsten alloy ring and a reasonable small radial thickness of outer retainer are required.

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Overcoming Van der Waals Forces in reconfigurable nanostructures

arXiv

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arXiv 2022年

作者： Zhang, Wang Wang, Hao Tan, Alvin T.L. Ranganath, Anupama Sargur Zhang, Biao Wang, Hongtao Chan, John You En Ruan, Qifeng Liu, Hailong Ha, Son Tung Wang, Dong Ravikumar, Venkat K. Low, Hong Yee Yang, Joel K.W. Engineering Product Development Singapore University of Technology and Design Singapore487372 Singapore Northwestern Polytechnical University 127 West Youyi Road Xi'An710072 China Institute of Materials Research and Engineering Singapore138634 Singapore Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Advanced Micro Devices Singapore Pte Ltd Singapore Singapore

Reconfigurable metamaterials1-3 require constituent nanostructures to demonstrate switching of shapes with external stimuli. For generality, such nanostructures would touch and stick to other surfaces in one of its configurations. Yet, a longstanding challenge is in overcoming this stiction caused by Van der Waals forces, which impedes shape recovery. Here, we introduce a stiff yet self-recovering material system based on acrylic acid, and tested it in high-aspect ratio structures, where recovery is weak. This designer material has a storage modulus of ~5.2 GPa at room temperature and ~90 MPa in the rubbery state at 150 ℃, an order of magnitude higher than previous reports. A high-resolution resin for two-photon lithography was developed based on this polymer system, enabling 3D printing of nanopillars with diameters of ~400 nm and aspect ratio as high as ~10. Experimentally, we observed self-recovery as collapsed and touching structures overcome stiction to stand back up. We developed a theoretical model to explain the recoverability of these sub-micron structures. Reconfigurable structural colour prints and holograms were demonstrated, indicating potential applications of the material system as a shape memory polymer suitable for sub-micron reconfigurable metamaterials. Copyright © 2022, The Authors. All rights reserved.

关键词： Van der Waals forces

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Site experimental study on smart de-icing device based on the de-icing method by the transferred current of bundled conductors

Site experimental study on smart de-icing device based on th...

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22nd International Symposium on High Voltage Engineering (ISH 2021)

作者： G. Yang X. Jiang X. Ren W. Zhou J. Ma Z. Qin State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing University No.174 Shazhengjie Shapingba Chongqing China Chongqing Electric Power Design Institute Co. Ltd No.60 Shanan street Shapingba Chongqing China

Icing seriously threatens the safety of transmission lines. Transferring the current of bundled sub-conductors through smart de-icing device is a promising de-icing method. In this study, site experiments on smart de-icing device based on the de-icing method by the transferred current of bundled conductors were carried out in the natural environment at the xuefeng mountain national field science observatory of chongqing university. The smart de-icing device was hanged on the glaze tower and connected between the 4-bundled conductors (4×LGJ400/35) and a rectifier which was equipped with other device so that a high current is acceptable for this study. In order to complete the test loop the 3-bundled conductors (3×LGJ240/30) were connected with the 4-bundled conductors (4×LGJ400/35) and the rectifier. The site experiments included three tests: the intelligent of the smart de-icing device test (test 1), the heating rate of the conductor test (test 2) and the practical effect of melt-icing test (test 3). The experimental results in this paper proved the feasibility of the transferred current of bundled conductors de-icing method, and the smart de-icing device showed a good performance, which shows the specific potential and broad application prospects

关键词： melting overhead line conductors power transmission lines transmission network calculations de-icing

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A transfer learning based geometric position-driven machining error prediction method for different working conditions*

A transfer learning based geometric position-driven machinin...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Teng Zhang Hao Sun Lin Zhou Shengqiang Zhao Fangyu Peng Rong Yan National NC System Engineering Research Center School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Digital Design and Manufacturing Innovation Center Co. Ltd China Wuhan China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665431545

Machining error is one of the most important indicators to evaluate the processing quality of thin-walled parts. With the development of Data Science, the data-driven methods have become popular. But the condition that the model can work accurately on a new task is that the feature space and distribution of the data are the same. A sample-based transfer learning method driven by geometric position is utilized to quickly predict the machining errors of thin-walled parts under different working conditions. This method can fully learn the knowledge related to machining errors contained in the data through model training, and can apply this knowledge to accurately and quickly forecast machining errors under new working conditions. In the experimental scenario, this method has outstanding predictive performance. The average determination coefficient of the four groups of target domain experiments reached 0.96, and the average root mean square error is less than the machining error acquisition time to 22% of the original, reducing the dependence on time-consuming and expensive measurements greatly.

关键词： Employee welfare Training Mechatronics Transfer learning Machining Predictive models Data models

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Abnormal flow of α particles in heavy-ion collisions at intermediate energies

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Physical Review C 2021年第4期103卷 044607-044607页

作者： G. Qu (曲国峰) Y. Huang (黄宇) D. Peng (彭丹) Z. Xu (徐子虚) W. Lin (林炜平) H. Zheng (郑华) G. Tian (田国玉) R. Han (韩瑞) C. Ma (马春旺) M. Huang (黄美容) P. Ren (任培培) J. Han (韩纪锋 ) Z. Yang (杨振雷) X. Liu (刘星泉) R. Wada Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064China School of Physics Henan Normal University Xinxiang 453007 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA

The experimentally observed abnormal α flow behavior from heavy-ion collisions at intermediate energies is investigated using the events of Ca40+Ca40 at 35 MeV/nucleon simulated with an improved antisymmetrized molecular dynamics model with specific consideration of the Fermi motion in the nucleon-nucleon collision process. Its possible origins in the processes from the fragment formation to the experimental flow extraction, i.e., dynamical process, sequential decay, experimental detection, and data analysis, are closely examined. It is found that the observed abnormal α flow behavior originates from the reconstruction of reaction planes in the flow extraction. How the abnormal α flow behavior is generated from the reaction plane reconstruction procedure is discussed.

关键词： Collective flow Low & intermediate energy heavy-ion reactions Models & methods for nuclear reactions

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