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2018中国自动化大会(CAC2018)

作者：汤健王科净刘卓余刚赵建军 Junfei Qiao Xiaojie Zhou 北京工业大学信息学部计算智能与智能系统北京市重点实验室流程工业国家重点实验室东北大学矿冶过程自动控制技术国家重点实验室矿冶过程自动控制技术北京市重点实验室 Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

维持球磨机运行在最佳负荷状态是实现磨矿过程运行优化与反馈控制的关键因素之一。基于磨机振动/振声等信号构建的数据驱动磨机负荷软测量模型因存在磨机研磨机理不清、标记样本不完备、模型不能合理阐释等问题而难以工业应用。针对这... 详细信息

维持球磨机运行在最佳负荷状态是实现磨矿过程运行优化与反馈控制的关键因素之一。基于磨机振动/振声等信号构建的数据驱动磨机负荷软测量模型因存在磨机研磨机理不清、标记样本不完备、模型不能合理阐释等问题而难以工业应用。针对这些问题,本文提出了基于数值仿真的虚拟振动信号生成方法,旨在解决筒体振动过程无法精确建模的难题。首先,结合研磨钢球的运动过程对筒体振动产生机理进行定性分析;接着,提出由实验设计、数值仿真、信号处理等模块组成的虚拟振动信号生成与分析框架;最后,通过耦合离散元方法、有限元方法等仿真工具产生虚拟加速度信号。通过构建缩小长度的实验磨机数值仿真模型验证了所提方法的有效性。

关键词：磨机负荷数值仿真筒体振动虚拟振动信号

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The FAST HI 21-cm absorption blind survey. II. – Statistic Exploration for Associated and Intervening systems

arXiv

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arXiv 2024年

作者： Hu, Wenkai Wang, Yougang Li, Yichao Pen, Ue-Li Wang, Jie Jing, Yingjie Zhu, Ming Zhang, Xin Yang, Wenxiu Xu, Yidong Xu, Chen Chen, Jingze Zheng, Zheng Li, Di Chen, Xuelei Chen, Qingze Wu, Fengquan Department of Physics and Astronomy University of the Western Cape Robert Sobukwe Road Bellville7535 South Africa Australia National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China College of Sciences Northeastern University Shenyang110819 China Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Canadian Institute for Theoretical Astrophysics University of Toronto 60 Saint George Street TorontoONM5S 3H8 Canada Canadian Institute for Advanced Research 180 Dundas St West TorontoONM5G 1Z8 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada Perimeter Institute of Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Department of Astronomy Tsinghua Univerisity 30 Shuangqing Road Beijing100084 China Guizhou Radio Astronomical Observatory Guizhou University Guiyang550000 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China

We present an extragalactic HI 21-cm absorption lines catalog from a blind search at z ≤ 0.35, using drift-scan data collected in 1325.6 hours by the ongoing Commensal Radio Astronomy FasT Survey (CRAFTS) and FAST All Sky HI Survey (FASHI), which spans a sky area of 6072.0 deg2 and covers 84533 radio sources with a flux density greater than 12 mJy. 14 previously identified HI absorbers and 20 newly discovered HI absorbers were detected, comprising 15 associated systems, 10 intervening systems, and 9 systems with undetermined classifications. Through spectral stacking, the mean peak optical path, mean velocity-integrated optical path, mean FWHM and mean HI column density are measured to be 0.47 and 0.30;27.19 and 4.36 km s−1;42.61 and 9.33 km s−1;0.49 and 0.08 Ts× 1020cm−2K−1, for the associated and intervening samples, respectively. Statistical analysis also reveals that associated systems tend to be hosted by red (g − r >0.7) galaxies at lower redshifts, whereas galaxies hosting intervening HI absorption are typically found at higher redshifts and are of a bluer (g−r ≤0.7) type. A noticeable difference is observed in the positions of foregrounds, backgrounds of intervening systems, and high-redshift and low-redshift associated systems on the WISE color-color diagram. All identified foreground sources in our sample have W1-W2 magnitudes below 0.8, suggesting no Active Galactic Nuclei (AGN). In contrast, backgrounds of intervening systems tend to have W1-W2 magnitudes above 0.8, indicating AGN presence. For associated absorption, most low-redshift (z ≤0.5) systems show W1-W2 values below 0.8, while higher-redshift associated absorption (z >0.5) displays a broader range of W1-W2 values. Copyright © 2024, The Authors. All rights reserved.

关键词： Moon

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第Sup1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.

关键词： Convolutional Neural Network High Resolution Compute Tomography Intensity Inhomogeneity Kernel Support Vector Machine Clonal Selection Algorithm

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Author Correction: Maximizing carbon sequestration potential in Chinese forests through optimal management

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Nature communications 2024年第1期15卷 4020页

作者： Zhen Yu Shirong Liu Haikui Li Jingjing Liang Weiguo Liu Shilong Piao Hanqin Tian Guoyi Zhou Chaoqun Lu Weibin You Pengsen Sun Yanli Dong Stephen Sitch Evgenios Agathokleous Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. liusr@***. Key Laboratory of Forest Management and Growth Modelling China's National Forestry and Grassland Administration Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry 100091 Beijing China. Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI) Department of Forestry and Natural Resources Purdue University West Lafayette IN 47907 USA. College of Forestry Northwest agriculture and Forestry University Yangling 712100 China. Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University 100871 Beijing China. Schiller Institute for Integrated Science and Society Department of Earth and Environmental Sciences Boston College Chestnut Hill Massachusetts MA 02467 USA. Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. Department of Ecology Evolution and Organismal Biology Iowa State University Ames IA 50011 USA. College of Forestry Fujian Agriculture and Forestry University Fuzhou 350002 China. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 10

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

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