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Integrated system Conception of Low-altitude Air Defense and Air Traffic control Based on Cellular Mobile Communication Network

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IOP Conference Series: Materials Science and Engineering 2018年第1期408卷

作者： Zhao Guhao Wu Yarong Yang Yuan Zhu Jie Yao Dengkai Yang Nan Hao Chenlu State Key Laboratory of Air Traffic Management System and Technology Air Force Engineering University College of Air Traffic Control & Navigation

Low-altitude airspace as a national valuable strategic resource, because of the lack of effective monitoring method, and can not be fully utilized. Integration of air defense and air traffic control is an important problem in air traffic control establishment and this Paper has discussed a kind of integrated system of low-altitude air defense and air traffic control based on cellular mobile communication network, which can effectively meet demand of early warning detection in low altitude air defense and at the same time can meet demand of air traffic control to air surveillance, communication and navigation. The proposed method can effectively solve the low-altitude airspace detection, monitoring and communication problems, and give full play to the economic value of low-altitude airspace.

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Mobility in China, 2020: a tale of four phases

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National Science Review 2021年第11期8卷 110-120页

作者： Suoyi Tan Shengjie Lai Fan Fang Ziqiang Cao Bin Sai Bing Song Bitao Dai Shuhui Guo Chuchu Liu Mengsi Cai Tong Wang Mengning Wang Jiaxu Li Saran Chen Shuo Qin Jessica R.Floyd Zhidong Cao Jing Tan Xin Sun Tao Zhou Wei Zhang Andrew J.Tatem Petter Holme Xiaohong Chen Xin Lu College of Systems Engineering National University of Defense Technology WorldPop School of Geography and Environmental ScienceUniversity of Southampton School of Mathematics and Big Data Foshan University State Key Laboratory on Blind Signal Processing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Chinese Evidence-Based Medicine Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Big Data Research Center University of Electronic Science and Technology of China West China Biomedical Big Data Center West China Hospital Sichuan University Tokyo Tech World Hub Research Initiative Institute of Innovative Research Tokyo Institute of Technology School of Business Central South University Institute of Big Data and Internet Innovations Hunan University of Technology and Business

2020 was an unprecedented year,with rapid and drastic changes in human mobility due to the COVID-19 *** understand the variation in commuting patterns among the Chinese population across stable and unstable periods,we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020,ranging from the Lunar New Year travel season(chunyun), to the exceptional calm of COVID-19 lockdown,and then to the recovery *** observed that cross-city movements,which increased substantially in chunyun and then dropped sharply during the lockdown,are primarily dependent on travel distance and the socio-economic development of *** the Lunar New Year holiday,national mobility remained low until mid-February,and COVID-19 interventions delayed more than 72.89 million people returning to large *** network analysis revealed clusters of highly connected cities,conforming to the social-economic division of urban agglomerations in *** the mass migration back to large cities was delayed,smaller cities connected more densely to form new *** the recovery period after travel restrictions were lifted,the netflows of over 55% city pairs reversed in direction compared to before the *** findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response,transportation planning and regional economic development,among others.

关键词： human mobility travel restrictions COVID-19 mobile phone data behavioral response

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Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

arXiv

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arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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燃烧源氨排放对灰霾期间北京大气铵的重要贡献

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Science Bulletin 2024年第7期69卷 978-987页

作者：吴礼彬王芃张强任红 Zongbo Shi 胡伟陈静谢巧蓉李林杰越思瑶韦莲芳宋琳琳张永根王子涵陈爽韦琬王小曼章炎麟孔少飞葛宝珠杨婷方运霆任露洁邓君俊孙业乐王自发张宏亮胡建林刘丛强 Roy M.Harrison Qi Ying 傅平青 Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China Department of Atmospheric and Oceanic Sciences Fudan UniversityShanghai 200438China IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan UniversityShanghai 200438China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Air Environmental Modeling and Pollution Controlling Key Laboratory of Sichuan Higher Education Institute Chengdu University of Information TechnologyChengdu 610225China Division of Environmental Health&Risk Management School of GeographyEarth&Environmental SciencesUniversity of BirminghamBirmingham B152TTUK School of Environmental Science and Engineering Tianjin UniversityTianjin 300072China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110164China Yale-NUIST Center on Atmospheric Environment International Joint Laboratory on Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjing 210044China Department of Atmospheric Sciences School of Environmental Studies and Department of Environmental Science and TechnologySchool of Environmental StudiesChina University of GeosciencesWuhan 430074China Department of Environmental Science and Engineering Fudan UniversityShanghai 200438China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlNanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental Sciences/Center of Excellence in Environmental Studies King Abdulaziz UniversityJeddah 21589Saudi Arabia Zachr

Aerosol ammonium(NH_(4)^(+)),mainly produced from the reactions of ammonia(NH_(3))with acids in the atmosphere,has significant impacts on air pollution,radiative forcing,and human *** the source and formation mechanism of NH_(4)^(+)can provide scientific insights into air quality ***,the sources of NH_(3)in urban areas are not well understood,and few studies focus on NH_(3)/NH_(4)^(+)at different heights within the atmospheric boundary layer,which hinders a comprehensive understanding of aerosol NH_(4)^(+).In this study,we perform both field observation and modeling studies(the Community Multiscale Air Quality,CMAQ)to investigate regional NH_(3)emission sources and vertically resolved NH_(4)^(+)formation mechanisms during the winter in *** stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH_(3)emissions,including fossil fuel sources,NH_(3)slip,and biomass burning,are important sources of aerosol NH_(4)^(+)with more than 60%contribution occurring on heavily polluted *** contrast,volatilization-related NH_(3)sources(livestock breeding,N-fertilizer application,and human waste)are dominant on clean ***-related NH_(3)is mostly local from Beijing,and biomass burning is likely an important NH_(3)source(~15%–20%)that was previously *** effective control strategies such as the two-product(e.g.,reducing both SO_(2)and NH_(3))control policy should be considered to improve air quality.

关键词： Particulate ammonium Nitrogen isotope PM2.5 The Community Multiscale Air Quality(CMAQ) control policy

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Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis system Based on EEG

Group Feature Learning and Domain Adversarial Neural Network...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chen-Chen Fan Haiqun Xie Liang Peng Hongjun Yang Zhen-Liang Ni Guan’an Wang Yan-Jie Zhou Sheng Chen Zhijie Fang Shuyun Huang Zeng-Guang Hou 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 Chinese Academy of Sciences Beijing China First People's Hospital of Foshan Foshan China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.

关键词： Deep learning Neural networks Medical services Human factors Sensitivity and specificity Feature extraction Robot sensing systems

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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Soft-Landing Dynamics of a Type of Four-Legged Space Lander

SSRN

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SSRN 2024年

作者： Shi, Tengfei Shi, Tengfei Yang, Yinping Zhang, Zhijuan Liu, Cuicui Liu, Caishan Ma, Daolin Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province School of Mechanics and Aerospace Engineering Southwest Jiaotong University Chengdu610031 China State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China Institute of Dynamics and Control College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100871 China China Academy of Space Technology Beijing100094 China School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System Ministry of Education Shanghai200240 China

Analyzing the landing dynamics response is essential for enhancing the success rate of surface explorations on Mars, the moon, or asteroids. This paper develops a comprehensive dynamics model for a space lander equipped with four sets of buffer legs, each consisting of a primary strut, two secondary struts, and a footpad, during its soft landing process. The degrees of freedom of the main body and the footpads are defined as generalized coordinates to describe the motion of the space lander. The compression force of the buffer struts is obtained by interpolating real experimental data, and the contact force between the footpad and the ground is calculated using Archimedes law for granular media. Then, the dynamical model for the lander in terms of minimal degrees of freedom can be constructed by applying the Lagrange equations of the second kind. The proposed dynamics model is validated by the experiments of a single-legged lander vertical touchdown and a four-legged lander free touchdown. Moreover, histories of buffer force, acceleration of the main body, and other key physical quantities are computed to investigate the landing process. This highly efficient and accurate dynamics model provides a method to perform extensive simulations to design and optimize a soft landing process. © 2024, The Authors. All rights reserved.

关键词： Landing

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Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot

Spiking Neuron Networks based Energy-Efficient Object Detect...

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Chinese Automation Congress (CAC)

作者： Bo-Chao Liu Qian Yu Jing-Wen Gao Shuang Zhao Xiong-Chang Liu Yan-Feng Lu Beijing Information Science and Technology University Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426480

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot.

关键词： Visualization Energy consumption Neurons Object detection Energy efficiency Real-time systems Mobile robots

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A fast and compact saliency score regression network based on fully convolutional network

arXiv

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arXiv 2017年

作者： Xi, Xuanyang Luo, Yongkang Li, Fengfu Wang, Peng Qiao, Hong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China LSEC Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing100190 China

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time). Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles

Point Cloud Completion by Larger Receptive Fields for Intell...

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International Conference on Intelligent Transportation

作者： Yunqing Zhang Xiaotong Zhang Kunhua Liu Yisheng Lv Wei He Long Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Waytous Inc. Beijing China

Point cloud completion is a critical task for intelligent vehicles in the Intelligent Transportation systems (ITS). Due to the disordered nature of point clouds, current point-based point completion methods utilize max pooling operations and Convolutional Neural Networks (CNNs) with a convolution kernel size of 1 to extract point features. However, a network with a kernel size of 1 has a small receptive field and can only extract one point feature at a time; as a result, such a network struggles to precisely extract highly abstracted features. To address this issue, we propose a point cloud completion network with larger receptive fields, termed PLRF-Net, which is composed of two parts: a reconstruction network and a completion network. The reconstruction network focuses on repairing the structures of partial points and includes an offset-attention embedding module, a rearrangement module, several convolution layers with varying kernel sizes, a pooling and repeating layer, and transposed convolution layers with different kernel sizes. Meanwhile, the completion network strives to improve the precision of the completed point cloud and consists of several convolution layers, along with a downsampling and upsampling module. Experimental results indicate that our approach achieves superior quantitative and qualitative outcomes on both the Multiview Partial Point Cloud (MVP) and KITTI datasets.

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