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2024 China Automation Congress, CAC 2024

作者： Wang, Xiaocheng Li, ZeLong Sun, Pengjiao Han, Qiaoni Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China School of Electrical and Information Engineering Tianjin University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China

ISBN: (纸本)9798350368604

With the rapid development of new energy technology, it has become a trend to combine wind and solar power generation into the smart grid. In this paper, wind power generation and photovoltaic power generation are introduced into the smart grid, and taking into account the consumption capacity of consumers at different levels, a tiered pricing scheme considering the uncertainty of wind and solar energy is proposed. Firstly, we establish probability density functions of power generation for wind power generation and photovoltaic power generation respectively to express the uncertainty of wind energy and solar energy. To meet the energy demand of consumers at all levels, we present the energy demand model of consumers after considering their electricity demand and temperature influence. Then, we formulate the optimal pricing problem for the electricity market under the framework of tiered pricing, so as to make the energy market get satisfactory income while meeting the energy demand of consumers. Finally, we use the particle swarm optimization algorithm to determine the optimal pricing strategy of the energy market. The simulation results show that when meeting the energy demand of consumers in different temperature conditions, the optimal pricing can be found that meets the satisfactory income of the energy market. © 2024 IEEE.

关键词： Clean energy

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A Pricing Strategy for Smart Grid Based on PSO and Distributed Iterative Algorithm

A Pricing Strategy for Smart Grid Based on PSO and Distribut...

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International Conference on communications, Signal Processing, and systems, CSPS 2023

作者： Wang, Xiaocheng Ma, Haogang Yin, Yuan Li, Zelong Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9789819975556

This paper proposes a pricing strategy for the Market by using a Stackelberg game-based bi-level programming model. In the model, the wholesale price and the retail price is optimized by the Market to increase its profit. In the model, the Energy Provider (EP) optimize optimizes the adjustment coefficient to increase the trading probability with the Utility Company (UC). Besides, the Market can sell the power to the UC directly. Then, the UCs determine their retail price by non-cooperative game to increase their profit. The simulation results reveal that the Market determines the optimal price to maximize profits. On the other hand, the proposed price strategy of EP can increase the trading probability, which promotes more UCs to trade with EP, thus increasing its benefit. Finally, UCs get a bash equilibrium through the non-cooperative game. The research proves that the proposed strategy is a win–win strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Profitability

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image 16

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Xie, Shuo Ma, Yingjie Xu, Wei Qiu, Song Sun, Yifei East China Normal University School of Communication and Electronic Engineering China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350330755

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets. © 2023 IEEE.

关键词： Image enhancement

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Research on head way control of a guided transport system based on intersection condition evaluation

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Transportation Safety and Environment 2023年第4期5卷 29-35页

作者： Xiao Yu Yuan Cao Yongkui Sun Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijing 100081China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

ln order to ensure the safety and efficiency of the section tracking operation of a guided transport system,a safety headway control method of section tracking based on intersection conditions is proposed in this *** the difference of signal phase,the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method,*** on the artificial potential field method,the time-varying hybrid artificial potential field(TH-APF)method was proposed,and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport *** simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions;the tracking headway can be maintained at about 120 *** tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method,thus improving the operating efficiency.

关键词： guided transport system intersection condition evaluation artificial potential field method tracking headway control

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A Value Set Approach for Stability Region Determination for Time Delayed Power systems with Multiple Uncertain Parameters 7

A Value Set Approach for Stability Region Determination for ...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Shi, Xiubao Ye, Hua Gao, Lei Hou, Kaiyuan Wang, Kefei Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China China Electric Power Research Institute Power System Department Beijing China State Grid Cooperation of China Dispatch and Communication Center Northeast Branch Shenyang China

ISBN: (纸本)9798350359558

This paper develops a stability region computation method for time delayed power systems based on the value set method. It is designed to accommodate systems with multiple uncertain parameters and time delays. This method serves to analyze the small signal stability margin of power systems and offer guidance for system parameter design. Different from many previous works, this method achieves relatively high efficiency without any conservativeness. First, it processes polynomials with exponentials through polynomial transformations. Second, by continuously subdividing and discriminating the uncertain space, the parameter stability region of the system is obtained. Finally, the effectiveness and accuracy of the proposed method are validated through simulation examples in MATLAB/Simulink. © 2024 IEEE.

关键词： Time delay

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Wireless power transmission system with CC and CV output based on hybrid LCC-S/P topology 5

Wireless power transmission system with CC and CV output bas...

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5th International Conference on Electronics and communication, Network and Computer Technology, ECNCT 2023

作者： Li, Kun Gong, Zheng Meng, Jiaheng Liu, Qing Tianjin University of Technology Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9798350328660

Wireless Power Transfer (WPT) technology provides a convenient and efficient power supply solution for mobile devices. The utilization of constant current (CC) and constant voltage (CV) control in WPT systems proves valuable for charging battery applications. This paper presents a novel WPT system with CC and CV output characteristics, employing a switching topology. Specifically, the LCC-S and LCC-P topologies are applied and rigorously simulated. The proposed MCR-WPT system's output characteristics in both CC and CV modes are meticulously validated using Saber software. The simulation results distinctly demonstrate the system's capability to achieve CC and CV outputs within a varying load resistance range of 5Ω to 30Ω, thus conclusively establishing the efficacy of the topology switching technology. © 2023 IEEE.

关键词： Topology

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Research on Hybrid Path Planning Algorithm Based on Anti-Collision

Research on Hybrid Path Planning Algorithm Based on Anti-Col...

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2023 International Conference on Culture-Oriented Science and Technology, CoST 2023

作者： Ren, Penghao Li, Mengnan Su, Zhibin Beijing Radio & Television Station Beijing100022 China Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism China Communication University of China Beijing Key Laboratory of Modern Entertainment Technology Beijing100024 China

ISBN: (纸本)9798350357998

In order to solve the problem of fast switching of special equipment in the cultural complex, the improved Moth-Flame Optimization algorithm (MFO) is combined with the improved Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in different scenarios of cultural complexes in this paper. Specifically, the traditional Moth-Flame Optimization algorithm is improved and applied to the global path planning problem. On the basis of the planned global path, the intersection of dynamic obstacles with the global path is detected, and the improved Dynamic Window Approach is used for local obstacle avoidance in the section with collision risk, finally, the hybrid path planning in the complex environment of the cultural complex is realized. The simulation experimental results prove the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Motion planning

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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Dunhuang murals image inpainting method based on fusion classification pre-training model

Dunhuang murals image inpainting method based on fusion clas...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Ren, Hui Zhang, Chengya Sun, Ke Ye, Chenyu Communication University of China School of Information and Communication Engineering Beijing100024 China Ministry of Culture and Tourism Key Laboratory of Audiovisual Technology and Intelligent Control System Beijing100024 China Beijing Key Laboratory of Modern Performing Arts Technology Beijing100024 China University of Pennsylvania School of Engineering and Applied Science PA19104 United States

ISBN: (纸本)9798350380347

Murals are an important part of China's cultural heritage. Because of their age, Dunhuang murals have suffered from discoloration, fading and damage. With the development of computer image restoration technology, digital image processing provides strong support for mural restoration. Although there are many research results in the field of ancient mural images, there is no pre-trained model based on ancient mural images available for migration. There is a method for Dunhuang murals image restoration based on Generative Adversarial Network (GAN) with improved ResNet50 structure as an encoder in the generator. This research result proposes a generative adversarial network model with a parallel double convolutional extraction of deep features for the generator, combined with a ternary heterogeneous joint discriminator. On this basis, this paper proposes a binary classification task to distinguish ancient mural images from modern natural style images, so that the model pre-learns features such as color, texture, and structure of mural images, which are inputted into the restoration network as a priori information, in order to achieve the effect of reducing the probability of incomplete restoration of the model, and better generalization ability. The experimental results show that the method proposed in this paper has an overall improvement in the evaluation indexes such as Peak Signal to Noise Ratio(PSNR) and Structural Similarities(SSIM) when compared with other methods, and the restored images are more in line with human subjective vision, which proves the validity of the method. © 2024 IEEE.

关键词： Generative adversarial networks

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