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arXiv 2024年

作者： Wang, Shanshan Yu, Shoujun Cheng, Jian Jia, Sen Tie, Changjun Zhu, Jiayu Peng, Haohao Dong, Yijing He, Jianzhong Zhang, Fan Xing, Yaowen Jia, Xiuqin Yang, Qi Tian, Qiyuan Guo, Hua Li, Guobin Zheng, Hairong Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China United-Imaging Healthcare Shanghai China School of Biomedical Engineering Tsinghua University Beijing China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology China University of Electronic Science and Technology of China Chengdu China

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Prediction of ship fuel consumption based on Elastic network regression model

Prediction of ship fuel consumption based on Elastic network...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Li, Shanshan Li, Xinyu Zuo, Yi Li, Tieshan Dalian Maritime University Navig. Coll. Collab. Innov. Center of Maritime Big Data and Marine Artificial General Intelligence Dalian China University of Electronic Science and Technology of China College of Automation Engineering Chengdu China

ISBN: (纸本)9781665402453

Predicting the fuel consumption of ships sailing under different navigation conditions and improving the operation efficiency of shipping industry has become an important topic. There are many characteristic variables affecting ship fuel consumption during navigation, such as trim, draft, wind speed, wind direction and so on. And some variables are highly correlated, which is easy to produce multicollinearity problems. It makes the fuel consumption prediction complex. The study established an Elastic network regression model by combining the least absolute contraction and selection operator (LASSO) and Ridge regression algorithm. The model reduces the complexity and improves the interpretability and accuracy by selecting the characteristic variables affecting ship fuel consumption. The study is verified by the navigation data of a ferry within two months. The results show that compared with long short term memory (LSTM) and back-propagation neural network (BPNN), the Elastic network regression model can not only explain the relationship between fuel consumption and variables, but also predict fuel consumption more accurately and effectively. © 2021 IEEE.

关键词： Regression analysis

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LHAASO-KM2A detector simulation using Geant4

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Radiation Detection technology and Methods 2024年第3期8卷 1437-1447页

作者： Zhen Cao F.Aharonian Q.An Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi J.T.Cai Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng X.T.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao L.Q.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.Y.He X.B.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang Z.C.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang T.Ke D.Kuleshov K.Kurinov B.B.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li Jian Li Jie Li K.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu R.Lu Q.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov Z.W.Ou B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi Y.Q.Qi B.Q.Qiao J.J.Qin D.Ruffolo A.Sáiz D.Semikoz C.Y.Shao L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Q.W.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang K.Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang D.X.Xiao G.Xiao G.G.Xin Y.L.Xin Y.Xing Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang F.Yang F.F.Yang H.W.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang L.X.Zhang Li Zhang P.F.Zhang P.P.Zhang R.Zhang S.B.Zh Key Laboratory of Particle Astrophysics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institute for Nuclear Physics 69029Heidelberg103980Germany State Key Laboratory of Particle Detection and Electronics BeijingChina University of Science and Technology of China Hefei230026AnhuiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031SichuanChina School of Astronomy and Space Science Nanjing UniversityNanjing210023JiangsuChina Center for Astrophysics Guangzhou UniversityGuangzhou510006GuangdongChina Hebei Normal University Shijiazhuang050024HebeiChina Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023JiangsuChina Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityGuangzhouZhuhai 510275519000GuangdongChina School of Physics and Astronomy Yunnan UniversityKunming 650091YunnanChina Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237ShandongChina APC UniversitéParis CitéCNRS/IN2P3CEA/IRFUObservatoire de ParisParis 11975205France Department of Engineering Phys

KM2A is one of the main sub-arrays of LHAASO,working on gamma ray astronomy and cosmic ray physics at energies above 10 *** simulation is the important foundation for estimating detector performance and data *** is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units(>6000)with large altitude difference(30)and huge coverage(1.3).In this paper,the design of the KM2A simulation code G4KM2A based on Geant4 is *** process of G4KM2A is optimized mainly in memory consumption to avoid memory *** simplifications are used to significantly speed up the execution of *** running time is reduced by at least 30 times compared to full detector *** particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented,which show good agreement.

关键词： LHAASO KM2A Simulation GEANT4

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Unraveling the adsorption and diffusion behavior of H and O atoms at Zr(0001) surface doped with a Nb/Sn atom: A first-principles study

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Heliyon 2025年第4期11卷 e42376页

作者： Pan, Rongjian Tang, Aitao Qin, Jiantao Wang, Qingqing Wu, Lu The First Sub-Institute Nuclear Power Institute of China Sichuan Chengdu 610005 China Collaborative Innovation Center of Chemistry for Energy Material Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Key Laboratory of Computational Physical Science Department of Chemistry Fudan University Shanghai 200433 China College of Materials Science and Engineering Chongqing University Chongqing 400044 China National Key Laboratory of Nuclear Reactor Technology Nuclear Power Institute of China Chengdu 610041 China

With the formation of a surface oxide or hydride layer of the zirconium alloy, the ductility of the nuclear rod decreases obviously. Thus, it is significant to develop a deeply understanding of this process, which may assist in future corrosion and hydrogen embrittlement resistance design for the zirconium alloy. A special focus of this work is an atomistic insight for unraveling the influence of doped alloying elements Nb/Sn on the early stages of oxide and hydride formation of zirconium alloys. The results show that for the effect of Nb/Sn on the adsorption of H at Zr(0001) surface, the adsorption energy of H on the surface of Zr alloys is tend to decrease with the addition of Nb, but for the addition of Sn, it is not significant. For the effect of Nb/Sn on the adsorption of O at Zr(0001) surface, the adsorption energy of O on the surface of Zr alloys is reduced with the addition of Nb and Sn, and the effect of Nb was more remarkable than that of Sn. In case of the diffusion barrier of H from Zr(0001) surface to the interior of Zr bulk, it is reduced with the addition of Nb in Zr(0001), while the addition of Sn does not change much. For the diffusion barrier of O, both are decreased with the addition of Nb or Sn in Zr(0001). © 2025

关键词： Adsorption Diffusion First-principles calculations Surface Zirconium

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Research on the training mode of electric power talents in ethnic areas with the Causal cycle model of the information and integration of production and education

Research on the training mode of electric power talents in e...

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2020 International Conference on Information Science and Education, ICISE-IE 2020

作者： Liu, Ruihua Wang, Rong Zhao, Ying Liu, Qunying Yuan, Juan Yang, Li Xi, Chuan Sichuan Electric Vocational and Technical College Chengdu610071 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

ISBN: (纸本)9781665422611

Relying on the advantages of electric power enterprises in running schools, integrating enterprise, academic and political resources, we are committed to the pre-service and post service training of electric power talents in ethnic underdeveloped areas, build a lifelong learning platform, and help the development of electric power industry. Through the pre-service and post service talent training system, we can form a deep integration of teaching and training, which is in line with the integrity, pertinence, structure, dynamics and environment of talent training objectives, and feedback principle model runs through the whole process of electric power workers' career in underdeveloped areas. According to different post groups, based on information and integration of production and education, Vensim software is used to construct the causal cycle model of industry education integration, establish post curriculum system, implement target power skills course training and learning, and gradually improve the training and learning content. © 2020 IEEE.

关键词： Integration

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Constructing Pdo Reinforced Cuo/Al2O3 Mesoporous Nanostructures as Highefficiency Catalysts for Hydrazine Oxidation Reaction (Hzor)

SSRN

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SSRN 2024年

作者： Khan, Safia Arshad, Ifzan Aftab, Saima Arshad, Javeria Khan, Mariam Shah, Syed Sakhawat Janjua, Naveed Kausar Li, Hu Shenzhen Research Institute of Shandong University Shenzhen518057 China Shandong Technology Centre of Nanodevices and Integration School of Integrated Circuits Shandong University Jinan250101 China Institute for Advanced Studies Shenzhen University Shenzhen518060 China College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Chemistry Quaid-i-Azam University Islamabad43520 Pakistan National University of Technology Islamabad44000 Pakistan Ångström Laboratory Department of Materials Science and Engineering Uppsala University Uppsala75121 Sweden

Direct hydrazine fuel cells (DHFC) demand the evolved and persistent electrocatalysts for anodic hydrazine oxidation reaction (HzOR). Herein, PdO promoted CuO heterostructures supported on γ-Al2O3 are presented as efficient electrocatalysts for HzOR. γ-Al2O3 is prepared by precipitation method while metal precursors are incorporated by co-impregnation technique. Physiochemically characterized PdO-CuO/Al2O3 mesoporous composites displayed large electrochemical active surface area (ECSA) i.e., 0.18 cm2, high current density (j) i.e., 35.7 mAcm-2, larger diffusion coefficient (D°) i.e., 29.3 × 10-4 (cm2s-1), large apparent rate constant (kapp) i.e., 13.2 cms-1 with low charge transfer resistance (Rct) i.e., 3.6 kΩ shown by the best catalyst i.e., 1% PdO-CuO/Al2O3. Cyclic voltammetry indicated that the fabricated working electrodes offer high efficiency towards HzOR in alkaline medium in such a way that 1% PdO-CuO/Al2O3 produced 600 times higher oxidation current than CuO/Al2O3 composite. Owing to stability and reproducibility, PdO modified CuO/Al2O3 would achieve a huge catalytic significance in multiple electrochemical oxidation reactions with economic and ecological benefits. © 2024, The Authors. All rights reserved.

关键词： Cyclic voltammetry

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Neutron-induced nuclear recoil background in the PandaX-4T experiment

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Chinese Physics C 2022年第11期46卷 262-274页

作者： Zhou Huang Guofang Shen Qiuhong Wang Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Yunshan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Yanlin Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Nasir Shaheed Changsong Shang Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Dan Zhang Minzhen Zhang Peng Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou School of Physics and Astronomy Shanghai Jiao Tong UniversityKey Laboratory for Particle Astrophysics and Cosmology(MoE)Shanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China School of Physics Beihang UniversityBeijing 102206China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary SciencShandong UniversityQingdao 266237China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong UniversityQingdao 266237China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai200240China School of Physics Nankai UniversityTianjin 300071China School of Physics Peking UniversityBeijing 100871China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing102206China School of Physics and Microelectronics Zhengzhou UniversityZhengzhouHenan 450001China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai200240China

Neutron-induced nuclear recoil background is critical to dark matter searches in the PandaX-4T liquid xenon *** this study,we investigate the features of neutron background in liquid xenon and evaluate its contribution in single scattering nuclear recoil events using three *** first method is fully based on Monte Carlo *** last two are data-driven methods that also use multiple scattering signals and high energy signals in the *** the PandaX-4T commissioning data with an exposure of 0.63 tonne-year,all these methods give a consistent result,i.e.,there are 1.15±0.57 neutron-induced backgrounds in the dark matter signal region within an approximated nuclear recoil energy window between 5 and 100 keV.

关键词： dark matter neutron background multiple scattering high energy gamma

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Extraction of offshore navigation characteristics based on crossing-line analysis 7

Extraction of offshore navigation characteristics based on c...

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7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020

作者： Song, Wanbing Zhu, Yongjie Zuo, Yi Li, Tieshan Philip Chen, C.L. Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728162461

The offshore channel is an important stage of preparation for the docking because it exists a higher frequency of ship traffic accidents. The analysis of channel is a critical premise for ship risk assessment and an indispensable part for improving the efficiency of shipping management. A channel analysis based on crossing-line has been widely used in inland channel research, but it is seldom in offshore one. In order to reasonably analyze the navigation characteristics of offshore ships and provide a basis for an effective simulation of offshore navigation traffic model, a modified crossing-line analysis for offshore channel based on Automatic identification system (AIS) data is proposed. Our method is applied to the offshore channel analysis of Dalian Port, and its results show that this method has smaller statistical error in the analysis of curved channel and sector channel, which is effective to analyze the navigation characteristics of offshore channel. The proposed method could be used to simulate the ship speed model on the offshore curved channel and establish an intelligent channel system to optimize the ship tracking path. © 2020 IEEE.

关键词： Navigation

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Application of Compressed Sensing in NB-IoT-Based Structural Health Monitoring of Rail Tracks 24

Application of Compressed Sensing in NB-IoT-Based Structural...

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24th International Workshop on Electromagnetic Nondestructive Evaluation, ENDE 2019

作者： Li, Xin Yan, Yan Hua, Pan Zhang, Qing Wang, Haitao Wang, Chao Chen, Yifan Tian, Guiyun College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China China School of Life Science and Technology University of Electronic Science and Technology of China China

ISBN: (纸本)9781643681184

Ultrasonic guided-wave testing is one of the most widely used technology for Structural Health Monitoring (SHM) of rail tracks. Currently, cable is the main tool of signal transmission for guided wave-based track monitoring systems. The installation of cables can significantly increase the system cost and restrict the flexibility of system deployment. In recent years, the NB-IoT technology has been gradually appï£¡ied to the field of SHM, it offers long-range wireless communication among a large-scale sensor networks at the cost of minimum construction and maintenance. One primary obstacle hindering the integration of NB-IoT and guided wave-based track monitoringsystem is that the limited channel bandwidth of NB-IoT leads to significant transmission delay whentransmitting the ultrasonic guided-wave signal sampled at Nyquist rate. In this paper, a CompressedSensing (CS) framework for NB-IoT based rail-track monitoring system is proposed. The proposed CS framework utilizes the sparsity feature of the ultrasonic lamb-wave signal to enable sub-Nyquist sampling and maintain the feature of the measured signal at a low compression rate. To validate theproposed CS framework, the propagation time of lamb-wave is selected as the performance metrics. The experimental results show that compared with the traditional sampling method, the propagation time of lamb wave in rail track can be accurately extracted when the sampling rate is set to 100kHz, therefore, the channel bandwidth of NB-IoT can meet the delay-free data transmission of a single ultrasonic sensor. © 2020 The authors and IOS Press.

关键词： Structural health monitoring

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An interpretable hierarchical temporal aggregation network for RUL prediction

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Expert Systems with Applications 2025年 289卷

作者： Zixu Wang Meng Li Yong Chen Zhilong Liu School of Automation Engineering University of Electronic Science and Technology of China Chengdu 611731 China Institute of Reactor Power Engineering Nuclear Power Institute of China Chengdu 610000 China

In this paper, the issue of the prediction of the remaining useful life of rolling bearing is studied, an interpretable hierarchical temporal aggregation prediction method is proposed. Firstly, a method by predicting a new divergence-based degradation metric is proposed, which is more interpretable and provide more degradation information than the end-to-end methods. Then, to predict the degradation metrics, an attention hierarchical temporal aggregation net is presented, which move the process of iterative prediction inside the network. Thirdly, a hierarchical temporal aggregation prediction strategy is proposed, which greatly reduce the number of the iteration steps in prediction by the coordination of four attention hierarchical temporal aggregation nets. Furthermore, to gain the remaining useful life from the predicted degradation metrics, a dynamic degradation degree discriminator is designed. Finally, extensive experiments are carried out on intelligent maintenance system (IMS) bearing dataset and a dataset collected from a nuclear main cycle pump to verify this proposed method.

关键词： Remaining useful life (RUL) prediction Hierarchical temporal aggregation model Degradation metric

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