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Phase-controlled asymmetric optomechanical entanglement against optical backscattering

Science China(Physics,Mechanics & Astronomy) 2023年第3期66卷 66-75页

作者： Jing-Xue Liu Ya-Feng Jiao Ying Li Xun-Wei Xu Qiong-Yi He Hui Jing Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Efects and ApplicationsHunan Normal UniversityChangsha 410081China Laboratory of Chemical Biology&Traditional Chinese Medicine Research Ministry of Education College of Chemistry and Chemical EngineeringHunan Normal UniversityChangsha 410081China State Key Laboratory for Mesoscopic Physics School of PhysicsFrontiers Science Center for Nano-optoelectronicsCollaborative Innovation Center of Quantum MatterPeking UniversityBeijing 100871China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Synergetic Innovation Academy for Quantum Science and Technology Zhengzhou University of Light IndustryZhengzhou 450002China

Quantum entanglement plays a key role in both understanding the fundamental aspects of quantum physics and realizing various quantum devices for practical applications. Here we propose how to achieve a coherent switch of optomechanical entanglement in an optical whispering-gallery-mode resonator, by tuning the phase difference of the driving lasers. We find that the optomechanical entanglement and the associated two-mode quantum squeezing can be well tuned in a highly asymmetric way,providing an efficient way to protect and enhance quantum entanglement against optical backscattering, in comparison with conventional symmetric devices. Our findings shed a new light on improving the performance of various quantum devices in the practical noisy environment, which is crucial in such a wide range of applications as noise-tolerant quantum processing and the backscattering-immune quantum metrology.

关键词： quantum entanglement optical backscattering optomechanics

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research on Optimization of Freight Train ATO Based on Elite Competition multi-Objective Particle Swarm Optimization

Research on Optimization of Freight Train ATO Based on Elite...

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作者： Lingzhi Yi Renzhe Duan Wang Li Yihao Wang Dake Zhang Bo Liu Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology College of Automation and Electronics Information Xiangtan University The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration

In view of the complex problems that freight train ATO(automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

关键词： Freight Train Automatic Train Operation Dynamics Model Competitive multi-Objective Particle Swarm Optimization Algorithm(CMOPSO) multi-Objective Optimization

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Performance of buildings integrated with a photovoltaic–thermal collector and phase change materials

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Procedia engineering 2017年 205卷 1337-1343页

作者： Yuekuan Zhou Xiaohong Liu Guoqiang Zhang College of Civil Engineering National Center for International Research Collaboration in Building Safety and Environment Hunan University Hunan Collaborative Innovation Center of Building Energy Conservation & Environmental Control Changsha 410082 Hunan China School of Architecture Hunan University Hunan 410082 China

To address the overheating of solar cell at midday in extreme hot summer regions as well as generate electricity with high and continuous photovoltaic efficiency, water-based photovoltaic/thermal-phase change material (PV/T-PCM) system has been proposed. A detailed simulation model for the system is presented. The effect of inlet water temperature as well as mass flow rate of the cooling water on performance parameters such as temperature of solar cell, photovoltaic efficiency is analysed. Results show that in comparison with the traditional photovoltaic panel, remarkable increase in photovoltaic efficiency has been actualized. Meanwhile, both increase in mass flow rate and decrease in inlet water temperature exert positive effect on performance parameters of PV/T-PCM system.

关键词： Photovoltaic thermal system with phase change material (PV/T-PCM) solar cell temperature photovoltaic efficiency

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Interval Optimization for Integrated Demand Response in A Battery-Equipped Household multi-energy System

Interval Optimization for Integrated Demand Response in A Ba...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Diankun Hu Yongxin Su Mao Tan Hunan National Center for Applied Mathematics Xiangtan China College of Automation and Electronic Information Engineering Xiangtan University Xiangtan China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China

ISBN: (纸本)9781665426312

The promotion of gas-electric devices and electricity storage devices brings necessity to realize integrated demand response (IDR) for household multi-energy systems (HMES). This paper proposed an interval optimization method for load scheduling in HMES for IDR, which addresses uncertainties in the system model and considers dynamic energy-source switching among gas, electricity and storage. controllable loads include washing machine, air conditioner and battery as electric devices, and water heater and kitchenware as gas-electric devices. For minimizing energy cost, an interval optimization model with tolerance degree is converted into a deterministic model based on the interval order relationship and interval probability. Then genetic algorithm is applied for solving the deterministic problem. Case studies show that the interval optimization with tolerance method is robust to HMES uncertainty. For a battery-equipped HMES, the energy cost savings are up to 14.7% compared with the traditional method without considering interval optimization and tolerance degree.

关键词： Uncertainty Costs Atmospheric modeling Water heating Optimization methods Dynamic scheduling Demand response

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Integrated energy storage system based on triboelectric nanogenerator in electronic devices

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Frontiers of Chemical Science and engineering 2021年第2期15卷 238-250页

作者： Xiao Feng Yang Zhang Le Kang Licheng Wang Chongxiong Duan Kai Yin Jinbo Pang Kai Wang School of Electrical Engineering Qingdao UniversityQingdao 266071China Department of Energy“Galileo Ferraris” Politecnico di TorinoTurin 10129Italy School of Materials Science and Engineering Xi’an University of Science and TechnologyXi’an 710054China School of Information Engineering Zhejiang University of TechnologyHangzhou 310023China School of Materials Science and Energy Engineering Foshan UniversityFoshan 528231China Hunan Key Laboratory of Super Microstructure and Ultrafast Process School of Physics and ElectronicsCentral South UniversityChangsha 410012China Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinan 250022China

The emergence of electronic devices has brought earth-shaking changes to people's ***,an extemal power source may become indispensable to the electronie devices due to the limited capacity of *** one of the possible solutions for the extermal power sources,the triboelectric nanogenerator(TENG)provides a novel idea to the increasing mumber of personal electronic *** is a new type of energy ollector,which has become a hot spot in the field of *** is widely used at the acquisition and conversion of mechan-ical enegy to electrice energy through the principle of electrostatic *** this basis,the TENG could be integrated with the energy stonage system into a self-powered system,W hich can supply power to the electronic devices and make them work *** this review,TENG's basic structure as well as its working process and working mode are firstly *** integration method of TENGs with enegy storage systems and the related research status are then introduced in *** the end of this paper,we put forward some problems and discuss the prospect in the future.

关键词： electronic devices triboelectrice nanogenerator mechanical energy sel-powered system

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Inverse design of quasi-bound states in the continuum metasurface for the polarization independent enhancement of Goos-H?nchen shift

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Science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 79-86页

作者： Zuhai Ma Youzhi Shi Yu Chen Yu Xue Gan Wan Chi Zhang Hui Jing Le-Man Kuang Xinxing Zhou Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education School of Physics and ElectronicsHunan Normal UniversityChangsha 410081China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong ProvinceInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhen 518060China Key Laboratory of Physics and Devices in Post-Moore Era College of Hunan ProvinceChangsha 410081China Academy for Quantum Science and Technology Zhengzhou University of Light IndustryZhengzhou 450002China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha 410081China

Bound states in the continuum(BIC)have been widely researched and applied in optics due to their unique electromagnetic ***,there are still difficulties in predicting and customizing BIC *** address this issue,we design an efficient combined neural network for highly accurate prediction of quasi-bound states in the continuum(q-BIC)spectrum,as well as for the inverse design of the polarization independent enhancement of the Goos-H?nchen(GH)***,we propose a C_(4)symmetric metasurface for achieving q-BIC spectrum and providing the condition of enhanced GH *** employing a combined neural network,the intensity,position,shape,and phase of q-BIC spectrum with ultra-narrow resonance can be accurately predicted and on-demand customized,even under a small ***,we develop a screening algorithm for the q-BIC spectrum to quickly realize the polarization independent enhancement of GH *** an application,an ultra-high sensitivity refractive index sensor has been proposed,whose sensitivity can reach 2.31×10~7μm/RIU for TM polarization and 1.03×10~6μm/RIU for TE ***,this work brings new solutions for quick prediction of q-BIC spectrum and the development of flexible polarization photonic devices.

关键词： Goos-Hanchen shift bound states in the continuum deep learning

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GSHPS application to the temperature and humidity independent control system in hot and humid areas

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Paper Asia 2018年第Compendium3期2018卷 45-47页

作者： Liu, Jianlong Li, Zhihao Liu, Li Xia, Xiaoqian Liang, Xinglu College of Civil Engineering Hunan University of Technology Zhuzhou412007 China Collaborative Innovation Center of Building Energy Conservation and Environmental Control Zhuzhou412007 China

This paper develops a research on the energysaving technology of the combination of the groundsource heat pump system (GSHPS) and the independent temperature-humidity control system. It introduced an air-conditioning system of an office building in Zhuzhou, which used the ground-source heat pump system (GSHPS) and the terminal system used dry fan-coil unit, and the air conditioning system merged together with the hot water-supply system of the building. It calculates the annual hourly cool load and the annual energy consumption by DEST software, conclude the amount of the savings of coal, and make a comprehensive analysis of economy of the air-conditioning system.

关键词： energy conservation

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Ultra-Short-Term Wind Speed Forecasting Based on Meta Learning with Signal Trend and Fluctuation Decomposition

Ultra-Short-Term Wind Speed Forecasting Based on Meta Learni...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Zhengzhi Wang Yongxin Su Hui Li Hunan National Center for Applied Mathematisc Xiangtan University Xiangtan China College of Automation and Electronic Information Engineering Xiangtan University Xiangtan China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China

ISBN: (纸本)9781665426312

Accurate wind speed prediction for each wind turbine is a critical basis of information for intelligent management and control of wind power system. How to precisely realize rapid prediction with small sample size is a critical open problem. This paper proposes an ultra-short-term wind speed prediction method based on meta learning algorithm with trend and fluctuation decomposition of wind speed signal. A meta learning prediction model with long short-term memory (LSTM) and recurrent neural network (RNN) is constructed as the base-learner, where low-frequency signal trend and high-frequency signal fluctuation are taken as the input of LSTM and RNN respectively. The forecasting results show that the mean absolute percentage error (MAPE) of the wind speed prediction scheme proposed in this paper is about 2.54%, and the sample size and training time costed in training are about 2.5% and 1.7% of traditional LSTM network. Results indicate that this method realizes ultra-short-term wind speed prediction with high accuracy as well as high efficiency.

关键词： Training Adaptation models Fluctuations Recurrent neural networks Wind speed Predictive models Market research

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DeePMD-kit v3: A multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Performance Analysis of Earth-air Heat Exchangers in Hot Summer and Cold Winter Areas

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Procedia engineering 2017年 205卷 1672-1677页

作者： Jiang Liu Zhun (Jerry) Yu Zhengxuan Liu Di Qin Jin Zhou Guoqiang Zhang College of Civil Engineering Hunan University Changsha China 410082 National Center for International Research Collaboration in Building Safety and Environment Hunan University Changsha China 410082 Hunan Collaborative Innovation Center of Building Energy Conservation & Environmental Control Changsha China 410082

As one of the energy-efficient heating technologies, earth air heat exchangers (EAHE) use renewable energy (i.e. geo-thermal energy) to first heat outdoor air, and then supply it to indoors for space heating in winter. The aim of this paper is to study the thermal performance of EAHE in winter in hot summer and cold winter areas. An experimental setup of EAHE was established in Changsha, China. Different variables, including soil temperature, supply air temperature and outdoor air temperature were measured for one week. The thermal performance was evaluated based on the data analysis. Results show that the supply air temperature reached the soil temperature around the pipe at the same length of 20m for different inlet air temperature. The COP and Q decreased along with the increase of inlet air temperature but increased along with the pipe length. Moreover, the thermal saturation of soil occurred in the fifth day of the week, and thermal interference between pipes can be ignored under the 1m distance between pipes. Our future work includes developing a numerical model to analyze and optimize the thermal performance of EAHE, thereby providing a reference for its design and application in hot summer and cold winter areas in China.

关键词： Earth air heat exchanger Hot summer cold winter Thermal performance Renewable energy Application

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