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Monthly Reduced Time-Period Scheduling of Thermal Generators and Energy Storage Considering Daily Minimum Chargeable Energy of Energy Storage

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Energy Engineering 2025年第4期122卷 1469-1489页

作者： Xingxu Zhu Shiye Wang Gangui Yan Junhui Li Hongda Dong Chenggang Li Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of the Ministry of Education Northeast Electric Power UniversityJilin132012China State Grid Jilin Electric Power Co. Ltd.Electric Power Science Research InstituteJilin130021China

To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was ***,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down *** approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly ***,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was *** on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load ***,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was *** studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.

关键词： Monthly scheduling thermal generators energy storage daily minimum chargeable energy decision time-period reduction unit start-up and shut-down unit commitment renewable energy

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Moving Target Trajectory Reconstruction Based on the LM Algorithm in WasSAR system

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chen, Jingwei An, Daoxiang Ge, Beibei Chen, Leping Zhou, Zhimin National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Science and Technology on Complex Aviation System Simulation Laboratory Beijing100076 China

Wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) has garnered attention due to its capability to monitor a stationary area over an extended period and its ability to track moving targets. Recently, the research on moving target trajectory reconstruction has also raised interest. However, the low positioning accuracy and impractical assumption of the traditional method make it hardly a practical application. We propose a novel precise trajectory reconstruction of a moving target in this article. First, we segment the echo into subapertures, after clutter rejection and radial velocity estimation, quasi-maximum likelihood (QML) is applied to estimate the polynomial phase signal (PPS) parameters of the phase history. Then, the Levenberg-Marquardt (LM) algorithm is applied to decouple the motion parameters. Based on the motion parameters, the coarse trajectory can be reconstructed. After that, the envelope history of the moving target is extracted. Modeling the motion trajectory of the moving target in segments and estimating the trajectory of each segment using the corrected envelope history and coarse trajectory. Finally, after processing all segments, the precise trajectory of the moving target is obtained. The effectiveness and practicability of the proposed algorithm were demonstrated by the processing results of experimental wide-angle staring synthetic aperture radar (WasSAR) data. © 1980-2012 IEEE.

关键词： Polynomials

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Analysis of the SEIR mean-field model in dynamic networks under intervention

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Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters

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Future Internet 2025年第4期17卷 168-168页

作者： Li, Peng Qing, Chen Liu, Hao National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China Southwest China Institute of Electronic Technology Chengdu610036 China Beijing100876 China

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. © 2025 by the authors.

关键词： Deep neural networks

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Appointed-Time Fuzzy Fault-Tolerant Control of Hypersonic Flight Vehicles With Flexible Predefined Behaviors

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IEEE Transactions on Automation science and Engineering 2025年 22卷 13995-14007页

作者： Luo, Ruining He, Guangjun Li, Yulun Bu, Xiangwei Li, Qiuni Air Force Engineering University Air Defense and Antimissile School Xi’an710051 China Chinese Academy of Sciences Key Laboratory of Electronics and Information Technology for Complex Aerospace System National Space Science Center Beijing101499 China Air Force Engineering University Aviation Engineering School Xi’an710051 China

This paper explores a novel fuzzy fault-tolerant control scheme for hypersonic flight vehicles (HFVs) that accounts for parameter perturbations and system disturbances, thereby achieving flexible prescribed properties and designated-time convergence. In contrast to existing studies, this framework incorporates the more detrimental elevator stuck fault within the context of prescribed performance control (PPC). To mitigate the fragility issues associated with PPC in fault-tolerant control, we propose an innovative approach that integrates a fault sensing system along with a readjustment prescribed envelope. This is accomplished by transforming the original HFVs model into an imprecise pure feedback model, allowing for the design of low-complexity fuzzy controllers with minimal model dependence through the utilization of higher-order error functions and fuzzy logic systems. Finally, numerical simulations validate both the effectiveness and superiority of the proposed method. © 2004-2012 IEEE.

关键词： Hypersonic vehicles

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年第11期12卷 16761-16776页

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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Random Coefficient-Moving Product Based Wireless Key Generation

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China Communications 2025年第2期22卷 199-212页

作者： Lu Xinjin Lei Jing Li Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China China South Industries Group Corporation Zhi Yuan Institute310012China

The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.

关键词： moving product random coefficient static environment wireless key generation

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Efficient Dynamics Modeling and Adaptive Tracking Control for a Rigid-Flexible Manipulator

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International Journal of Robust and Nonlinear Control 2025年

作者： Shi, Mingming Rong, Bao Rui, Xiaoting Wang, Guoping Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing China

Vibration suppression and trajectory tracking are pivotal areas of current robotics research, significantly influencing the operational performance of flexible manipulators. This article proposes a novel dynamics modeling strategy for flexible manipulators based on the multi-body system transfer matrix method, which achieves comparable accuracy to the traditional Lagrange method while improving computational efficiency by approximately 35.5% and 35.9% in two representative cases, respectively. On this basis, an adaptive hybrid control strategy is developed by integrating the dynamics model with an improved particle swarm optimization (PSO) algorithm and a fuzzy approximation-based backstepping controller. The improved PSO accelerates convergence and reduces residual elastic vibrations to 5.28E (Formula presented.) 5 and 7.16E (Formula presented.) 5 m under two typical cases, representing a reduction of over 80% compared to baseline trajectory planning methods. The proposed controller further decreases residual vibration to 5.94E (Formula presented.) 5 m while ensuring trajectory tracking errors remain below (Formula presented.), even under external disturbances. These simulation results validate the effectiveness of the proposed approach, demonstrating clear improvements in both vibration suppression and tracking accuracy compared to conventional PD and Legendre polynomial-based controllers. © 2025 John Wiley & Sons Ltd.

关键词： Lagrange multipliers

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Fully Distributed Output Regulation of Linear Discrete-Time Multi-Agent systems With Markov Switching Topology

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IEEE Transactions on Circuits and systems I: Regular Papers 2025年 1-9页

作者： Liu, Jiamin Li, Zhao-Yan Zhou, Bin Jimei University School of Science Xiamen361021 China Harbin Institute of Technology School of Mathematics Harbin150001 China Zhengzhou Research Institute of HIT Zhengzhou450000 China Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin150001 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China

This paper studies the stochastic cooperative output regulation problem for linear discrete-time multi-agent systems (MASs) with Markov switching topologies and communication/input delays. First, a moment stability criterion for an interconnected time-homogeneous Markov jump linear systems is builded. Then, based on the moment stability criterion, a fully distributed output regulation protocol is designed to solve the stochastic cooperative output regulation problem successively. Finally, a numerical simulation is provided to validate the efficiency of the designed protocol. © 2004-2012 IEEE.

关键词： Topology

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Magnitude,distribution and temporal trend of nitrous oxide emissions from China's natural soils over 1980–2022

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science China Earth sciences 2025年第4期68卷 1074-1085页

作者： Songbai HONG Zimeng LI Mingsong TANG Fa LI Yitong YAO Yanzi YAN Mingzhu HE Xuhui WANG Hui ZENG Shilong PIAO School of Urban Planning and Design Shenzhen Graduate SchoolPeking UniversityShenzhen518055China Key Laboratory of Earth Surface System and Human-Earth Relations Ministry of Natural Resources of ChinaShenzhen Graduate SchoolPeking UniversityShenzhen518055China Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking UniversityBeijing100871China Department of Earth System Science Stanford UniversityStanfordCA94305USA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA91125USA Department of Soil and Environment Swedish University of Agricultural SciencesUppsala75007Sweden Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance Beijing Normal UniversityZhuhai519087China School of National Safety and Emergency Management Beijing Normal UniversityZhuhai519807China

Soils represent the largest contributor to global nitrous oxide(N_(2)O)***,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from natural soils,another significant source,remain *** this study,we explored the magnitude,spatiotemporal dynamics,and drivers of N_(2)O emissions from China's natural soils over the period 1980–2022 via machine learning and a compiled novel dataset of 319 field *** results revealed that the N_(2)O flux per unit area was generally higher in the southeast compared to the northwest,and higher in forests than in *** spatial and biome heterogeneity was strongly regulated by local hydrothermal conditions and nitrogen *** averaged N_(2)O emissions over the study period were 646.2±27 Gg N_(2)O yr^(-1),with approximately equivalent contributions from forests and ***,there exhibited a significant and increasing trend from 1980 to 2022,with a rate of 2.7 Gg N_(2)O yr^(-2).Such an increase was primarily driven by the expansion of forested area and elevated nitrogen *** data-driven study presents a long-term and gridded estimate of N_(2)O emissions from China's natural soils,emphasizing the need for future research on changes of greenhouse gas emissions induced by land use change and nitrogen deposition.

关键词： Nitrous oxide Spatiotemporal dynamics Nitrogen deposition Forests Grasslands Data-driven estimates

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