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Optimal controller Design for Non-Affine Nonlinear Power systems with Static Var Compensators for Hybrid UAVs

Tsinghua Science and technology 2022年第1期27卷 196-206页

作者： Yanchu Li Qingqing Ding Shufang Li Stanimir Valtchev Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networkand School of Information&Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua UniversityBeijing 100084China the Nova School of Science and Technology(FCT/UNL) Caparica 2829-516LisbonPortugal Technical University of Sofia(TUS) Sofia 2125Bulgaria

A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var system(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATLAB-Simulink platform with the programming technique of its *** the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is *** simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.

关键词： Static Var Compensator(SVC) Unmanned Aerial Vehicles(UAV) power system non-affine nonlinear control inverse system method Linear Quadratic Regulation(LQR)

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TSN Disciplined Oscillators for Time Synchronization in Distributed Networks

TSN Disciplined Oscillators for Time Synchronization in Dist...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical communications, IPOC 2024

作者： Li, Zhifu Shou, Guochu Zhang, Xiaodong Chen, Li Zhao, Dong Zhao, Xiaoyu Xia, Kai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China

This paper proposes a TSNDO scheme utilizing time-sensitive networking to discipline clocks, providing precise time for distributed networks. The experimental results demonstrate that TSNDO achieves the time accuracy ... 详细信息

ISBN: (纸本)9798350379266

关键词： Clock distribution networks

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Time Error Bound of Deterministic Transmission in Cross-Domain Time-Sensitive Networking

Time Error Bound of Deterministic Transmission in Cross-Doma...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical communications, IPOC 2024

作者： Ma, Hao Li, Hongxing Chen, Li Zhao, Dong Zhao, Xiaoyu Xia, Kai Shou, Guochu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China

Based on coupled network theory, a bounded time error model for network determinism is proposed. The limit of traffic scheduling performance affected by time synchronization error in cross-domain time-sensitive networ... 详细信息

ISBN: (纸本)9798350379266

关键词： time synchronization Time-Sensitive Networking traffic scheduling

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A Classification and Recognition Model of Distributed Resources Based on Feature Extraction

A Classification and Recognition Model of Distributed Resour...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Lu, Jiaxin Wang, Yuqing Wang, Fei State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Information and Communication Technology R & D Center Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

The extensive integration of distributed resources such as distributed photovoltaic system and electric vehicles enhances the uncertainty of the load side greatly. Corresponding demand response strategies can be developed for different distributed resource types and characteristics of households' load, which is of great significance for new energy consumption and stable operation of the power system. However, only the net load power of households can be obtained through smart meters instead of the distributed resource types of households. Based on this, a distributed resource classification and identification model based on feature extraction is proposed in this paper, and effectively solves the problems of low accuracy and strong data dependence of existing recognition methods. Firstly, a generalized weather class generation algorithm based on clustering and voting is established, which is used to determine the weather class of each day to provide a basis for the following feature extraction. Then, based on the typical net load profiles under different generalized weather classes, a two-stage feature extraction and a classification identification model based on integrated learning algorithms are established to identify whether a user contains DPVS and EV, respectively. Finally, based on the category labels of each user for each stage, the final classification of the category to which the user belongs is carried out. Simulation experiments show that the recognition model built using the typical features extracted in the paper has good recognition accuracy. © 2023 IEEE.

关键词： classification and recognition Distributed photovoltaics system electric vehicles ensemble learning feature extraction

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Deciphering decadal urban ozone trends from historical records since 1980

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national Science Review 2024年第11期11卷 117-129页

作者： Haolin Wang Xiao Lu Paul I.Palmer Lin Zhang Keding Lu Ke Li Tatsuya Nagashima Ja-Ho Koo Hiroshi Tanimoto Haichao Wang Meng Gao Cheng He Kai Wu Shaojia Fan Yuanhang Zhang School of Atmospheric Sciences Sun Yat-sen University and Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Education Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) School of Geo Sciences University of Edinburgh National Centre for Earth Observation University of Edinburgh Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University State Key Joint Laboratory of Environmental Simulation and Pollution Control The State Environmental Protection Key Laboratory of Atmospheric Ozone Pollution ControlCollege of Environmental Sciences and Engineering Peking University Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science &Technology National Institute for Environmental Studies Department of Atmospheric Sciences Yonsei University Department of Geography Hong Kong Baptist University Department of Civil and Environmental Engineering University of California

Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade（2013–2022） in the northern hemisphere, warm-season（April–September） mean daily 8-h average maximum ozone increases much faster in urban regions with top ozone levels(mainly in the North China Plain, 1.2 ± 1.3 ppbv year-1) than in other, low-ozone regions(0.2 ± 0.9 ppbv year-1). These trends widen the ozone differences across urban regions, and increase extreme pollution levels and health threats from a global perspective. Comparison of historical trends in different urban regions reveals that ozone increases in China during 2013–2022 differ in magnitude and mechanisms to historical periods in other regions since *** reflects a unique chemical environment characterized by exceptionally high nitrogen oxides and aerosol concentrations, where reducing ozone precursor emissions leads to substantial ozone *** increase in China has slowed down in 2018–2022 compared to 2013–2017, driven by ongoing emission reductions, but with ozone-favorable weather conditions. Historical ozone evolution in Japan and South Korea indicates that ozone increases should be suppressed with continuous emission *** temperature and associated wildfires have also reversed ozone decreases in the USA and Europe,with anthropogenic ozone control slowing down in recent decades.

关键词： air quality surface ozone trends

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The Regional Distributed PV Ultra-short-term Power Forecasting Based on Static-dynamic Spatiotemporal Correlation Modeling

The Regional Distributed PV Ultra-short-term Power Forecasti...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Xu, Fei Su, Zinuo Zhen, Zhao Information and Communication Technology R & D Center State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

Accurate prediction of distributed photovoltaic power can help the arrange the scheduling plan reasonably to Maintain the safe and stable operation of the distribution network. However, distributed photovoltaic power generation exists lack of meteorological data and real-time monitoring data, the distribution range is large and dispersed, the installed capacity is relatively small and so on, the centralized photovoltaic power prediction method is not applicable to distributed photovoltaic power prediction, so it is necessary to carry out a regional power prediction study for distributed photovoltaic. Distributed PV is located in close proximity, in a similar external environment, and the power output between stations has strong spatial and temporal correlation, which can be captured to improve the accuracy of power forecasting. To this end, a regional distributed PV power prediction model based on static-dynamic spatiotemporal correlation modeling is proposed. The k-means is firstly used to cluster the regional distributed photovoltaic sites into several sub-regions, according to the geographical location and output characteristics that reflect the long-term static correlation between power stations. Then, graph convolutional neural network are applied to capture the dynamic correlation between subregions according to the power. In this way, the static and dynamic correlation are effectively considered in power prediction modeling, and the prediction accuracy has been improved. The superiority of the proposed method is verified by a real world dataset in China. © 2023 IEEE.

关键词： distributed photovoltaic graph convolution Regional power prediction

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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Causal Attention Transformer for Video Text Retrieval

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IET Image Processing 2025年第1期19卷

作者： Lan, Hua Lv, Chaohui School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Interactive computer graphics

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Cooperative Local Path Planning for Multi-unmanned Vehicles Formation Using the Enhanced Artificial Potential Field 3rd

Cooperative Local Path Planning for Multi-unmanned Vehicles...

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhang, Xin Duan, Xuting Zhou, Jianshan Zhang, Long Opening Project of Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport PRC Beijing China School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

ISBN: (纸本)9789819711024

An enhanced approach utilizing APF(artificial potential field) method is introduced in this paper. By adopting this approach, the challenge of local minima that may occur when dealing with local path planning for unmanned vehicles is tackled. When the unmanned vehicle approaches an obstacle, a virtual attractive potential field is implemented, utilizing the deviation in the heading angle of the vehicle to control the steering angle. Consequently, the vehicle can avoid obstacles, resolving the issue of local minima and enabling its application in path planning for multi-unmanned vehicles. The ROS with Gazebo simulation platform is used to verify the enhanced algorithm’s effectiveness in multi-unmanned vehicle path planning in an unknown environment. © Beijing HIWING Scientific and Technological information Institute 2024.

关键词： Motion planning

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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