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arXiv 2024年

作者： Guo, Yanqiang Zhang, Jianchao Hou, Jiahui Guo, Xiaomin Xiao, Liantuan Yanqiang, Guo College of Physics Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Standard weak measurement with an assistant pointer and single weak interaction constrains measurement precision and quantity of interaction parameters, and a compelling characterization of quantum effect featuring weak-value amplification (WVA) remains elusive. Here, we theoretically and experimentally demonstrate an enhanced dual-pointer WVA scheme based on multiple weak interactions and variable spectrum sources. Developing triple weak interactions, momentum P pointer reaches an optimal time-difference precision of 3.34×10−5 as at 6 nm spectral width, and intensity I pointer achieves a displacement resolution of 148.8 fm within 400 kHz linewidth. A quantum effect associated with an anomalous weak value is revealed by an observable violation of a Leggett-Garg inequality. The I-pointer weak value is measured to be 1478 using multiple weak interactions and high signal-to-noise detection, achieving a two-order-of-magnitude WVA enhancement compared to standard weak measurement. Our work opens up a practical avenue for minuscule quantumness measurements in challenging environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Design and Implementation of a Cross-Platform Real-Time Power system Simulation Software for the New power system 4

Design and Implementation of a Cross-Platform Real-Time Powe...

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4th International Conference on intelligent Power and systems, ICIPS 2024

作者： Huang, Ying Lu, Yuanhong Zhang, Jingyue Zhan, Jie Zhao, Yanjun Hu, Yufeng China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China China Southern Power Grid National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China China Southern Power Grid Guangzhou CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou510663 China

ISBN: (纸本)9798331532765

With the rapid increase in renewable energy installation nodes and the substantial enhancement of switching frequencies in the new power system, this paper proposes a design scheme for a cloud simulation software that employs the Latency Insertion Method (LIM) to decompose large-scale new power system models into several subsystems, enabling multi-platform integrated parallel computing. The self-developed platform meets the demands of ultra-large simulation scales, ultra-small simulation time steps, and massive simulation computations in power systems. Experimental results demonstrate that the accelerated simulation technology, which integrates model segmentation using LIM with multi-core parallel execution, can enhance the simulation speed by up to 858 times while maintaining simulation accuracy. This significantly improves simulation efficiency and is suitable for large-scale simulations of new power systems. © 2024 IEEE.

关键词： Power systems computer aided design

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance 5

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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5th International Conference on Computer Engineering and intelligent control, ICCEIC 2024

作者： Chen, Maolin Chen, Songhang Lian, Mingchang School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9798331507992

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs. © 2024 IEEE.

关键词： Automatic guided vehicles

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Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

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Infectious Disease Modelling 2024年第3期9卷 816-827页

作者： Xingxing Zhang Liuyang Yang Teng Chen Qing Wang Jin Yang Ting Zhang Jiao Yang Hongqing Zhao Shengjie Lai Luzhao Feng Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100073China State Key Laboratory of Respiratory Health and Multimorbidity China Key Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College) Ministry of EducationChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionChina Department of Management Science and Information System Faculty of Management and EconomicsKunming University of Science and TechnologyKunming650506China Department of Applied Mathematics&Statistics Stony Brook UniversityStony BrookNY11794-3600USA WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonSO171BJUK

Background Influenza is an acute respiratory infectious disease with a significant global disease ***,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of ***,comparative studies on the performance of innovative models and approaches used for influenza prediction are ***,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive *** The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to *** relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three *** Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in ***,the models’predictive performance declined as the weeks ahead ***,blocked cross-validation indicated that all models were robust to changes in data and had low risks of *** Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid ***,in the future,the ARMA—GARCH model may be used

关键词： Influenza-like illness Influenza Sentinel surveillance China Predicting Modeling

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Conflict Mechanism Analysis Method for Electric Vehicles Participating in Multiple Types of Power Grid Ancillary Services

Conflict Mechanism Analysis Method for Electric Vehicles Par...

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2023 International Conference on Power system technology, PowerCon 2023

作者： Xue, Yinjuan Chen, Qifang Cai, Haiqing Xia, Mingchao Gu, Haohan School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350300222

As the high-quality flexible resource of the grid, electric vehicles (EVs) can provide multiple auxiliary services for the grid at the same time. However, due to the one-way charging and discharging of EVs, as well as the limitation of power and capacity, which may cause availability conflicts when EVs participate in different auxiliary services simultaneously. This paper focuses on the analysis of the conflict mechanism of EVs participating in the secondary frequency regulation and peak shaving ancillary services at the same time. The impact mechanism between different auxiliary services in this conflict mode is revealed from the two aspects of limit conflict and charging and discharging instruction conflict, and the results of availability conflict of the two types of auxiliary services are verified and analyzed through simulation examples. © 2023 IEEE.

关键词： Frequency modulation

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Development of a novel high-performance balanced homodyne detector

arXiv

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arXiv 2025年

作者： Lin, Hong Liu, Mengmeng Guo, Xiaomin Luo, Yue Wang, Qiqi Song, Zhijie Guo, Yanqiang Xiao, Liantuan Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics Taiyuan University of Technology Taiyuan030024 China College of Physics Taiyuan University of Technology Taiyuan030024 China

True random numbers are extracted through measurements of vacuum fluctuations in quantum state components. We propose an improved scheme utilizing an optimization-based simulation methodology to enhance the temporal resolution of quantum state detection and processing efficiency of vacuum fluctuation signals in continuous-variable quantum random number generators (CV-QRNGs), while simultaneously maximizing the entropy content of quantum noise sources. This work presents the first application of optimization simulation methodology to balanced homodyne detector (BHD) circuit design, with particular emphasis on improving high-frequency transmission characteristics. The design framework prioritizes system stability and S-parameter sensitivity to optimize both circuit architecture and critical component parameters. The AC amplifier circuit was implemented through ADS high-frequency simulations using two ABA-52563 RF amplifiers in a cascaded configuration, with circuit modeling performed on Rogers 4350 substrate optimized for high-frequency applications. This approach enabled the development of a switched-configuration BHD featuring: 1) 1.9 GHz bandwidth, 2) 41.5 dB signal-to-noise ratio at 1.75 GHz, 3) 30 dB common-mode rejection ratio at 100 MHz, and 4) frequency response flatness within ±1.5 dB across 1.3-1.7 GHz. Additionally, the Husimi function is employed for entropy analysis to reconstruct vacuum state phase-space distributions, validating the detector's quantum measurement fidelity. The implemented system demonstrates a collective generation rate of 20.0504 Gbps across four parallel channels, with all output streams successfully passing NIST SP 800-22 statistical testing requirements. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Multi-Objective Optimization of Integrated Energy system for Cchp Based on Improved Cuckoo Search Algorithm

SSRN

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SSRN 2024年

作者： Zheng, KaiYue Zhao, ChuanZhen School of Control Science and Engineering Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China School of Electronics & Information Engineering Tianjin Key Laboratory of Optoelectronic Detection Technology and System Tiangong University Tianjin300387 China

This paper discusses the optimization and scheduling of an integrated energy system for combined cooling, heating and power (CCHP). It seeks to provide better scheduling of device outputs in the system and to decrease the daily economic and environmental operating costs of the system. We build an integrated energy system with wind turbine, micro gas turbine, electric cooling unit and so on, and establish a multi-objective optimization model with the goal of minimizing economic and environmental costs. In addition, we propose a novel optimal scheduling method. It combines chaotic mapping and cuckoo search algorithm to address issues such as slow convergence speed and high iteration counts in traditional optimization algorithms. Finally, the improved cuckoo search algorithm is used to solve the system optimization problem. The optimization solution is implemented in the case of satisfying the constraints of equipment output and power balance. The simulation results show that the CCHP system can effectively improve the economic efficiency and reduce the pollutant emissions compared with the common micro-grid system. Furthermore, compared with other algorithms, the improved cuckoo algorithm shows much better performance in solving the model and can decrease the whole system's total cost. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Cyber-Attack Risk Assessment and Prevention Strategy of Substations Based on Dynamic Attack and Defense Game 3

Cyber-Attack Risk Assessment and Prevention Strategy of Subs...

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3rd Power system and Green Energy Conference, PSGEC 2023

作者： Lin, Feng Mei, Yong Zhu, Yihua Chang, Dongxu Liu, Ren Guo, Hengdao China Southern Power Grid Power Dispatch and Control Center GuangZhou China Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc GuangZhou China Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre GuangZhou China China Southern Power Grid Csg Key Laboratory for Power System Simulation GuangZhou China Guangdong Provincial Key Laboratory of Intelligent Operation Control for New Energy Power System GuangZhou China

ISBN: (纸本)9798350340099

With the improvement of the informatization of the power system, the risk of cyber attack is also increasing. This paper proposes a risk assessment and prevention strategy for cyber attack against substations. Firstly, the cyber attack against substations are modeled based on the game theory of dynamic attack and defense. Secondly, an improved OPA (ORNL-PSERC-Alaska) model is utilized to analyze the cascade failure after cyber attacks and quantify the load loss of power system. Finally, a preventive strategy which can mitigate the expectation load loss caused by cyber attack against substations is put forward. Simulation experiments were conducted to validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Risk assessment

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Molecular dynamics study of the effects of deposition parameters on Si0.5Ge0.5 film growth on Ge(100) substrate

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Computational Materials Science 2025年 256卷

作者： Xie, Jianan Lin, Tao Wang, Cailin College of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

With the widespread application of SiGe films in the semiconductor field, precisely controlling the growth process to obtain high-quality films has become an important research direction. This study focuses on the growth of Si0.5Ge0.5 films on Ge (100) substrates and investigates the effects of different deposition parameters (such as deposition interval, incident energy, incident angle, and substrate off-angle) on film quality. Through molecular dynamics simulations, the effects of these deposition parameters on the crystal structure, dislocation density, and surface roughness of the films are systematically analyzed. The results show that shorter deposition intervals lead to faster atomic deposition, increasing the proportion of disordered atoms;longer deposition intervals effectively reduce the proportion of disordered atoms, but excessively long intervals may increase surface roughness and form island-like structures. Additionally, appropriate incident energy, incident angle, and substrate off-angle can significantly optimize the film quality, with the surface roughness being minimized when the incident energy is 1 eV. © 2025 Elsevier B.V.

关键词： Crystal atomic structure

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Target Recognition Inference Based on Knowledge Graph and Graph Neural Network

Target Recognition Inference Based on Knowledge Graph and Gr...

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Chinese Automation Congress (CAC)

作者： Wei Li Hongfeng Xu Yuan Li Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach.

关键词： Machine learning algorithms Target recognition Soft sensors Weapons Knowledge graphs Feature extraction Inference algorithms Vectors Data mining Convolutional neural networks

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