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The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

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Journal of Physics: Conference Series 2021年第1期2132卷

作者： Zhiyong Wang Shishen Liu Hao Sun Jingjing Zhang Ruyou Li Chen Liu School of Automation China University of Geosciences Wuhan Hubei 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude.

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Network Resilience

arXiv

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arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： complex networks

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12.2: A 3D Display Parallel System: Light Field Re-rendering and Depth Sense Optimization

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Renjing Pei Kui Ma Feiyue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences East Zhongguanchun Road No. 95 Haidian District Beijing School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing Parallel Optics Technology Innovation Center Qingdao Academy of Intelligent Industries Qingdao

The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye's visual mechanism, unartful 3D scene structure design, or bad viewing condition always emerges poor depth perception experience or even physiological discomfort during the watching time, which is often sub-optimal for mass high-quality 3D display productions. To solve this problem, we propose a novel 3D display parallel system for depth sense optimization and it empirically guides how the light field should be re-rendered. Structurally, the parallel system consists of an artificial perception measurement system, a display evaluation model and a light field display rendering system, which includes the display calibration, scene capture, light field data processing and display. Particularly, the system can systematically analyze and model various factors affecting the depth sense which learned through the measurement system, like scene structure, objects’ speeds in 3D video and so on. And those sense factors can be personally modified or increased according to the viewer's demands or technical improvement. Moreover, the light field could be real-time re-rendered, based on some image processing technology, optical flow analysis and object segmentation (or tracking) (especially the one-shot video segmentation). Theory and algorithms are developed and experimental validation results show a superior performance.

关键词： 3D Display Parallel System Depth Sense Light Field Re-rendering 3D Video Processing One-shot Segmentation

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A Fast Calculation Method for Bus Body Based on Equivalent Stiffness Element Method

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Journal of Physics: Conference Series 2020年第1期1626卷

作者： Xinshen Huang Hao Li Zhenkun Zhang Jianguo Zheng Hailong Pei Guangzhou Customs Technology Center Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou China

In view of complex modeling and inefficient calculation at existing stage finite element analysis method in bus body skeleton analysis. This paper presents a method on simulating complex large section beams by equivalent stiffness element. Equivalent stiffness element model is analyzed theoretically. Experiments are carried out to verify that the equivalent stiffness element method can be used in rapid analysis and calculation for girder, body frame and body optimization. It not only reduces modeling complexity, but also improves calculation efficiency. To a certain extent, it can provide a better analysis method for large-scale vehicle modeling and simulation.

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Approximate Stiffness Element Method for complex Section Beam Modeling

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Journal of Physics: Conference Series 2020年第1期1605卷

In this paper, a finite element method of simulating complex section beam element with approximate stiffness element is presented. By establishing an approximate stiffness element, the basic mechanical properties of the element are equivalent to the original complex section beam element's. The approximate stiffness element has several sub-section beam elements, which intersect with other beam elements in space. The problem that missing of intersection points among complex section beam and other beams, which is caused by using conventional beam element, is solved. The error between the approximate stiffness element and the original complex section beam element is analyzed by theoretical deduction, and the feasibility and accuracy of the approximate stiffness element method are verified by the physical test of a bus body.

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Correction: Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study

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Ultraschall in der Medizin - European Journal of Ultrasound 2024年第3期45卷 e2-e2页

作者： Yangyang Zhu Zheling Meng Hao Wu Xiao Fan Wenhao lv Jie Tian Kun Wang Fang Nie Medical Center of Ultrasound Lanzhou University Second Hospital Lanzhou China CAS Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of the Chinese Academy of Sciences School Beijing China Gansu Province Medical Engineering Research Center for Intelligence Ultrasound Lanzhou China

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An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism

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Journal of Physics: Conference Series 2020年第1期1693卷

作者： Tianyuan Chen Hongfei Wang Hao Liu Peng Wu Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing 100094 China University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net [1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method.

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Forest Fire Smoke Recognition Based on Multiple Feature Fusion

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IOP Conference Series: Materials Science and engineering 2018年第1期435卷

作者： Chang Lu Mingqi Lu Xiaobo Lu Min Cai Xiaoqiang Feng School of Software Engineering University of Science and Technology of China Hefei Anhui 230026 China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Nanjing 210096 China Enbo Technology Company Limited Nanjing 210022 China

In order to discover the forest fire at the early stage, the video based fire smoke detection system should be developed. Three static features and three dynamic features are selected to recognize the fire smoke through the analysis of forest monitoring video. To solve the fusion problem of different features, a cascade classifier based on BP neutral network is designed. Each feature vector would be assigned a classifier as the first stage of the cascaded classifier. The output of the first level of the classifier used as the input of the second level of the classifier. The weight for each feature that given by the trainer is more scientific. The result of the experiments show that the cascade classifier has better performance compared to the single BP neural network.

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Moving Target Detection Algorithm for Forest Fire Smoke Recognition with Improved ViBe

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Chang Lu Yichao Cao Xiaobo Lu Min Cai Xiaoqiang Feng School of Software Engineering University of Science and Technology of China Hefei Anhui 230026 China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Nanjing 210096 China Enbo Technology Company Limited Nanjing 210022 China

In order to discover the forest fire at the early stage, the video based fire smoke detection system should be developed. This paper proposes an improved ViBe algorithm to overcome the drawbacks of ViBe which is widely used in forest fire smoke recognition. To make the algorithm faster, the improved ViBe algorithm introduces a switch variable to control the model updating every 2 frames. Also, the new ViBe algorithm contains a method to detect if the scene is changing, and upon finding the changing, a fast updating strategy would be used to increase the adaptive ability. The experiments are done from 2 aspects, to test the time consuming and adaptive ability of the improved algorithm. The experiments results show that the improved ViBe algorithm has better performance compared to ViBe algorithm and GMM algorithm, and can detect the forest fire smoke accurately in different conditions.

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