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DID Code:A Bridge Connecting the Materials Genome Engineering Database w让h Inheritable Integrated Intelligent Manufacturing

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Engineering 2020年第6期6卷 612-620页

作者： William Yi Wang Peixuan Li Deye Lin Bin Tang Jun Wang Quanmei Guan Qian Ye Haixing Dai Jun Gao Xiaoli Fan Hongchao Kou Haifeng Song Feng Zhou Jijun Ma Zi-Kui Liu Jinshan Li Weimin Liu State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi'an 710072China CAEP Software Center for High Performance Numerical Simulation Institute of Applied Physics and Computational MathematicsBeijing 100088China CRRC Tangshan Co. Ltd.Tangshan 063035China Beijing Star Travel Space Technology Co. Ltd.Beijing 100013China Department of Materials Science and Engineering Pennsylvania State UniversityUniversity ParkPA 16802USA

A data identifier(DID)is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering(ICME),inheritable integrated intelligent manufacturing(I3M),and the Industrial Internet *** the guidance and quick acceleration of the developme nt of advanced materials,as envisioned by official documents worldwide,more investigations are required to construct relative numerical standards for material *** work proposes a universal DID format consisting of a set of build chains,which aligns with the classical form of identifier in both international and national standards,such as ISO/IEC 29168-1:2000,GB/T 27766-2011,GA/T 543.2-2011,GM/T 0006-2012,GJB 7365-2011,SL 325-2014,SL 607-201&WS 363.2-2011,and QX/T *** build chain is made up of capital letters and numbers,with no ***,the total length of each build chain is not restricted,which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC *** on these rules,the proposed DID is flexible and convenient for extendi ng and sharing in and between various cloud-based ***,classical two-dimensional(2D)codes,including the Hanxin Code,Lots Perception Matrix(LP)Code,Quick Response(Q.R)code,Grid Matrix(GM)code,and Data Matrix(DM)Code,can be constructed and precisely recognized and/or decoded by either smart phones or specific *** utilizing these 2D codes as the fingerprints of a set of data linked with cloud-based platforms,progress and updates in the composition-processing-structure-property-performance workflow process can be tracked spontaneously,paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity,performance,and collaboration.

关键词： Data identifier Database Digital twin Integrated computational materials engineering

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A maneuvering penetration strategy via integrated flight/propulsion guidance and control method for air-breathing hypersonic vehicle

A maneuvering penetration strategy via integrated flight/pro...

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2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

作者： Xue, Kong Guodong, Ning Ming, Yang Songyan, Wang Harbin Institute of Technology Control and Simulation Center and Science and Technology Complex System Control and Intelligent Agent Cooperation Laboratory China Beijing Electro-Mechanical Institute China Harbin Institute of Technology Control and Simulation Center China

ISBN: (纸本)9781538611715

Given a maneuvering control strategy for air-breathing hypersonic vehicle, which adopt the compound integrated flight/propulsion guidance and control method, further excavation the capability of scramjet to run stable and low fuel consumption, on this basis, implement accelerate snake maneuver, improve the survival probability of weapons, guarantee the practical application ability of the project. Take one certain type of air-breathing hypersonic vehicle as an example, on the basis of an accurate simulation model, several groups of simulation analysis are carried out, the simulation results show that the method is effective and reliable. © 2018 IEEE.

关键词： Hypersonic vehicles

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Rely-Guarantee Reasoning about Messaging system for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of Software Engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning system) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning system

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Research on Credibility Evaluation for Radio Frequency Guided Hardware-In-the-Loop simulation system 36

Research on Credibility Evaluation for Radio Frequency Guide...

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第36届中国控制会议

作者： Yuxiao Wang Tao Chao Chaolei Wang Songyan Wang Harbin Institute of Technology Science and Technology on Special System Simulation Laboratory Beijing Simulation Center

ISBN: (纸本)9781538629185

To ensure the effectiveness of the RF（Radio Frequency） guided HILS（Hardware-In-the-Loop simulation） system,the credibility evaluation was *** present,most of the methods were applied in the case of the simulation system input being consistent with the real system input,which is difficult to achieve in some subsystem of the RF guided HILS *** this paper,the relative complete system reliability evaluation index system was *** were classified,for different categories of subsystem,corresponding reliability assessment methods were *** particular,reliability evaluation methods based on performance level were put forward,when certain subsystems cannot be evaluated by consistency analysis methods.A simple example was given to show the evaluation process.

关键词： Radio Frequency Guided Hardware-In-the-Loop simulation Credibility Evaluation Index system Performance Index

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The Star Scene Modeling and Generation for Hardware-in-the-loop simulation

The Star Scene Modeling and Generation for Hardware-in-the-l...

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2018 International Conference on Computational, Modeling, simulation and Mathematical Statistics(CMSMS 2018)

作者： Ying ZHANG Hui-jie DU Yu ZHANG Yi-yuan MA Hui-yang QU Science and Technology on Special System Simulation Laboratory Beijing Simulation Center National Key Laboratory of Intelligent Manufacture System Technology for Complicated Products

The star sensor simulation system is used to test the star sensor performance on the ground, which is designed for star identification and spacecraft attitude determination of the spacecraft. The computer star scene based on the astronomical star chat is generated for hardware-in-the-loop simulation of the star sensor simulation system using OpenGL. The results of star scene generation are shown in this paper and applied in the real time star sensor simulation.

关键词： Star scene Modeling simulation Astronomical chat

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Lightweight and conformal acousto-ultrasonic sensing network for multi-scale structural health monitoring: A review

FlexMat

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FlexMat 2024年

作者： Yehai Li Shifeng Guo Zhongqing Su Keqin Ding Xian Jun Loh Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China The Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences Shenzhen China Department of Mechanical Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Technology Innovation Center of Structural Health Monitoring of State Market Regulation China Special Equipment Inspection & Research Institute Beijing China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) Singapore Singapore

Structural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with different coverage (i.e., global, large-area, and local monitoring) and sensitivity. Mechanical acousto-ultrasonic-based methods have formed a big family in SHM technologies. Multiple wave/resonance modes have been utilized for versatile SHM tasks. The permanently integrated sensing networks play a significant role in achieving a cost-effective and reliable SHM system, with major concerns including weight increase for large-scale deployment and conformity for complex geometry structures. In this review, typical acousto-ultrasonic sensors made of different material systems are discussed, along with advantages and limitations. Moreover, advanced network installation methods have been introduced, including surface-mounting with pre-integrated networks on substrates and in situ printing, and embedding with composite layup and metal additive manufacturing. Sensor versatility and usage in multi-scale SHM techniques are then highlighted. Different wave/resonance modes are transmitted and received with corresponding elements and network designs. In conclusion, this systematic review mainly covers a collection of acousto-ultrasonic sensors, two modalities of network installation, and their employment with various SHM methods, hopefully providing a useful guide to building lightweight and conformal networks with passive or active-passive sensors, and developing complete and reliable SHM strategies by integrating different damage identification methods on multiple scales.

关键词： acousto-ultrasonic conformal installation lightweight sensing networks structural health monitoring

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Publisher Correction: Ozone pollution mitigation strategy informed by long-term trends of atmospheric oxidation capacity

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Nature Geoscience 2024年 172页

作者： Wenjie Wang Xin Li Ying Liu Sihua Lu Yusheng Wu Shiyi Chen Keding Lu Min Hu Limin Zeng Min Shao Yuanhang Zhang Hang Su Yafang Cheng Ruijing Ni Ulrich Pöschl Zhaofeng Tan Franz Rohrer Andreas Wahner David D. Parrish Cheng Huang Xudong Tian K. M. Leung Liangfu Chen Meng Fan Qiang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing China State Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Max Planck Institute for Chemistry Mainz Germany International Joint Laboratory for Regional Pollution Control Ministry of Education Beijing China Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing China Institute of Carbon Neutrality Peking University Beijing China Institute for Environmental and Climate Research Jinan University Guangzhou China State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex Shanghai Academy of Environmental Sciences Shanghai China Institut für Energie-und Klimaforschung: Troposphäre (IEK-8) Forschungszentrum Jülich Jülich Germany Key Laboratory of Ecological and Environmental Monitoring Forewarning and Quality Control of Zhejiang Zhejiang Ecological and Environmental Monitoring Center Hangzhou China Environmental Protection Department The Government of the Hong Kong Special Administrative Region Hong Kong China State Key Laboratory of Remote Sensing Science The Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Earth System Science Tsinghua University Beijing China

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Vegetation Rather than Climate Dominate the Bowen Ratio Over Terrestrial Ecosystems of China

SSRN

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SSRN 2023年

作者： Sun, Mingyu Yu, Guirui Chen, Zhi Zhu, Xianjin Zhang, Weikang Han, Lang Liu, Zhaogang Ma, Lexin Dou, Xiaojun Yao, Yuan Wang, Jilong Luo, Wenxing Lin, Yong Chen, Shiping Deng, Zhengmiao Dong, Gang Du, Hu Gao, Yanhong Gu, Fengxue Hao, Xiangxiang Hao, Yanbin He, Qihua He, Yongtao He, Jinsheng Ji, Xibin Jiang, Shicheng Jiang, Zhengde Li, Xinhu Li, Yingnian Li, Yuqiang Li, Yuzhe Liu, Ran Liu, Shaomin Luo, Weijun Mo, Xingguo Sha, Liqing Shi, Peili Song, Qinghai Sun, Dan Tan, Junlei Tang, Yakun Wang, Fei Wang, Huimin Wang, Jianlin Wei, Wenxue Wu, Jiabing Wu, Zhixiang Xin, Xiaoping Yan, Junhua Zhang, Fawei Zhang, Yangjian Zhang, Yiping Zhang, Yucui Zhao, Fenghua Zhao, Liang Zhou, Li Zhu, Jiaojun Zhu, Zhilin Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin300072 China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of Sciences Beijing101408 China College of Agronomy Shenyang Agricultural University Shenyang100161 China Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station Tianjin University Tianjin300072 China State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences Beijing100093 China Key Laboratory for Agro-ecological Processes in Subtropical Region Institute of Subtropical Agriculture Chinese Academy of Sciences Changsha410125 China Shanxi University Taiyuan030006 China Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou730000 China Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences Beijing100081 China Center for Agricultural Technology Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Harbin150081 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Ecology College of Urban and Environmental Sciences Key Laboratory for Earth Surface Processes the Ministry of Education Peking University Beijing100871 China State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems College of Pastoral Agriculture Science and Technology Lanzhou University Lanzhou China School of Life Sciences Northeast Normal University Changchun130024 China Institute of Applied Ecology Chinese

Bowen ratio (β) is the ratio of sensible heat (H) to latent heat (LE), which reflected the energy balance and partitioning process between soil, vegetation, and atmosphere. Revealing the spatial patterns of β would benefit understanding the variation of energy partitioning in terrestrial ecosystems and its reciprocal relationship with environmental change. Here, we calculated β by integrating sensible and latent heat flux values from 80 flux observation sites based on eddy covariance method in ChinaFLUX to analyze the spatial pattern and dominant factors of β in China. Results showed that terrestrial ecosystems in China had an average β of 0.64±0.47, suggesting the strong regulating effect of ecosystems on the radiative warming in China. β significantly varied with ecosystem types in which desert has the highest β (2.08) while wetland showed the lowest β (0.37). β of terrestrial ecosystems in China showed a significant latitudinal pattern, increasing linearly with the increase of latitude, and showed specific patterns in forest and cropland ecosystems. However, β did not present a clear longitudinal and vertical pattern in different ecosystems except cropland. The spatial pattern of β was dominated by vegetation factors including gross primary productivity (GPP). However, the dominant role of vegetation factors varied with the climate and soil conditions, which showing that the dominant role of GPP gradually decreased from the hot and humid subtropical evergreen broad-leaved forest region to the low temperature arid temperate desert region. These findings demonstrated the spatial variation and its mechanism of terrestrial ecosystems β and provided insight into the future of vegetation mitigation of global climate change. © 2023, The Authors. All rights reserved.

关键词： Heat flux

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