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Bulletin of the American meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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A Siamese network Tracking Algorithm Based on Hierarchical Attention Mechanism

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hu Zhang Dong Hu Yingcan Qiu Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing 210003 China

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.

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Towards single-molecule optoelectronic devices

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Science China Chemistry 2018年第11期61卷 1368-1384页

作者： Lijue Chen Anni Feng Maoning Wang Junyang Liu Wenjing Hong Xuefeng Guo Dong Xiang State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Collaborative Innovation Center of Chemistry for Energy Materials Xiamen University Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics College of Electronic Information and Optical Engineering Nankai University Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable SpeciesCollege of Chemistry and Molecular Engineering Peking University

Benefiting from the development of molecular electronics and molecular plasmonics, the interplay of light and electronic transport in molecular junctions has attracted growing interest among researchers in both fields, leading to a new research direction of "single-molecule optoelectronics". Here, we review the latest developments of photo-modulated charge transport,electroluminescence and Raman spectroscopy from single-molecule junctions, and suggest future directions for single-molecule optoelectronics.

关键词： molecular electronics molecular optoelectronics single-molecule junction

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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Research on New Energy Power Forecast and meteorological Disaster Warning Platform

Research on New Energy Power Forecast and Meteorological Dis...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Peihua Xu Zhenghong Chen Ling Mou Yun Liang Jun Liu Yang Cui New energy Meteorological Service/Department of Computer Science and Technology Normal University Wuhan China New energy Meteorological Service Hubei Meteorological Service Center Wuhan China Hubei Energy Group New Energy Development Co. Ltd Wuhan China State Grid Henan Electric Power Research Institute Zhengzhou China Department of Network Electronic Engineering Insitute Hefe China

ISBN: (数字)9781728145747

ISBN: (纸本)9781728145754

With the large-scale grid connected operation of new energy power plants, the safe operation of grid and new energy power plants has been faced with unprecedented challenges, especially the influence of meteorological conditions. Due to the inherent volatility of new energy, accurate prediction of generation power can effectively reduce the impact on the electricity grid system. In order to improve the prediction accuracy, a dynamic meteorological element correction method considering multiple meteorological elements is introduced, and a centralized power forecasting platform is established. At the same time, due to the frequent occurrence of extreme weather events in recent years, such as wind fan icing, lightning gale, hail, mountain torrents and so on, all of them have brought challenges to the safe operation of new energy power plants, so a fan icing warning model has been developed. Based on the Internet automatic published information plantform of meteorological disaster has established which greatly reduced the loss of power plants caused by the extreme weather.

关键词： Forecasting Neural networks Wind speed Wind turbines Wind forecasting

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Review on Video Object Tracking Based on Deep Learning

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Journal of New Media 2019年第2期1卷 63-74页

作者： Fangming Bi Xin Ma Wei Chen Weidong Fang Huayi Chen Jingru Li Biruk Assefa College of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China Mine Digitization Engineering Research Center of the Ministry of Education China University of Mining and TechnologyXuzhou221116China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai201899China Information Communication Technology Department Wollo UniversityDessie EthiopiaEthiopia

Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so ***, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.

关键词： Object tracking deep learning neural work

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Practical phase-modulation stabilization in quantum key distribution via machine learning

arXiv

引用

arXiv 2019年

作者： Liu, Jing-Yang Ding, Hua-Jian Zhang, Chun-Mei Xie, Shi-Peng Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China Broadband Wireless Communication and Sensor Network TechnologyKey Lab of Ministry of Education Ministry of Education Nanjing210003 China Telecommunication and NetworksNational Engineering Research Center NUPT Nanjing210003 China

In practical implementation of quantum key distributions (QKD), it requires efficient, real-time feedback control to maintain system stability when facing disturbance from either external environment or imperfect internal components. Usually, a"scanning-and-transmitting" program is adopted to compensate physical parameter variations of devices, which can provide accurate compensation but may cost plenty of time in stopping and calibrating processes, resulting in reduced efficiency in key transmission. Here we for the first propose to employ a well known machine learning model, i.e., the Long Short-Term Memory network (LSTM), to predict those physical parameter variations in advance and actively perform real-time control on corresponding QKD devices. Experimentally, we take the phase-coding scheme as an example and run the LSTM model based QKD system for more than 10 days. Experimental results show that we can keep the same level of quantum-bit error rate as the traditional"scanning-and-transmitting" program by employing our new machine learning method, but dramatically reducing the scanning time and resulting in significantly enhanced key transmission efficiency. Furthermore, our present machine learning model should also be applicable to any other QKD systems using any coding scheme or QKD protocols, and thus seems a very promising candidate in large-scale application of quantum communication network in the near future. Copyright © 2019, The Authors. All rights reserved.

关键词： Efficiency

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A Deep Learning Framework with Spatial-Temporal Attention Mechanism for Cellular Traffic Prediction

A Deep Learning Framework with Spatial-Temporal Attention Me...

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IEEE Globecom Workshops

作者： Yun Gao Xin Wei Liang Zhou Haibing Lv College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center for Communication and Information Technology (NUPT) Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China Wireline Product Planning Dept Wireline Product Operation ZTE Corporation Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Currently, traffic prediction in cellular communication has become an important way to relieve traffic congestion, and then guarantee users' quality of experience (QoE) in multimedia service. However, when concerning traffic prediction in the context of big data, traditional deep learning models only have limited memory lengths and thus do not satisfy high prediction accuracy demand. In this paper, we design a new deep learningbased framework, which is built on our proposed novel spatialtemporal attention mechanism, to further increase the prediction accuracy of cellular traffic resource. Specifically, we firstly design a big data hierarchical architecture with four levels to extract meaningful information. Then, considering that cellular traffic resource from base stations characterizes spatial-temporal dependencies, we integrate conventional temporal attention mechanism for target area with that from spatial neighbor areas, proposing a novel spatial-temporal attention mechanism. Finally, we utilize this spatial-temporal attention mechanism based LSTM (STaLSTMs) to predict cellular traffic resource from base stations. Experimental results demonstrate that our proposed framework has better performances in cellular traffic prediction than other competing models. Importantly, in some application scenarios, this framework can also maintain high accuracy but only with 60% data.

关键词： Big Data Machine learning Data models Predictive models Base stations Quality of experience Time series analysis

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IONSS: Indirect Observation Node Selection Scheme in Trust Management for UAV network

IONSS: Indirect Observation Node Selection Scheme in Trust M...

引用

IEEE Globecom Workshops

作者： Weidong Fang Wuxiong Zhang Wei Chen Weiwei Gao Guoqing Jia Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Micro-system and Information Technology Chinese Academy of Sciences Shanghai China Shanghai Research Center for Wireless Communication Shanghai China School of Computer Science and Technology China University of Mining and Technology Xuzhou China College of Physics and Electronic Information Engineering Qinghai University for Nationalities Xining China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

As a three-dimensional distributed network, the UAV network is very critical for communication security. The current security approaches for UAV are mainly traditional secure schemes, such as encryption, authentication, and etc. Unfortunately, these secure schemes could not defend against the internal attacks. Simultaneously, as an effective technology to defend against the internal attacks, this is, a trust management technology, has security vulnerabilities in the third party recommendation. Nevertheless, there is also the risk of cyber holes in UAV network. In this paper, we propose an indirect observation node selection scheme (IONSS) to solve these issues. For all current indirect observation nodes, their energy levels are sorted to select the appropriate nodes. The Grey-decision calculation is performed and sorted with the attribute of these nodes, and the previous nodes are preferentially selected as the indirect observation nodes. The simulation results show that the balance between security and energy efficiency.

关键词： Unmanned aerial vehicles Protocols Wireless sensor networks Encryption Authentication

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Power allocation optimization in MC-NOMA systems for maximizing weighted sum-rate 24

Power allocation optimization in MC-NOMA systems for maximiz...

引用

24th Asia-Pacific Conference on Communications, APCC 2018

作者： Chen, Ruilu Wang, Xiaoming Xu, Youyun School of Telecommunication and Information Engineering Nanjing Univ. of Posts and Telecom. Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing Univ. of Posts and Telecom. Nanjing China National Engineering Research Center of Comm. and Network Nanjing Univ. of Posts and Telecom. Nanjing China

ISBN: (纸本)9781538669280

In this paper, we investigate a power allocation scheme in downlink multi-carrier non-orthogonal multiple access (MC-NOMA) systems for maximizing weighted sum-rate. Taking user-priority into account, we add the weighting factors into the formulated optimization problem. Firstly, we give sufficient and necessary concavity conditions that should be satisfied by the proposed weighted sum-rate maximization problem. Then, for more generally non-concave case, we use a first order approximation to convert the optimization problem and provide an iterative power allocation algorithm to find the globally optimal solution. Simulation results show that our proposed algorithm is superior to orthogonal frequency division multiple access (OFDMA) scheme and average power allocation scheme. © 2018 IEEE.

关键词： Frequency division multiple access

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