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Fully Distributed Low-Complexity UAV Formation control with Fixed-Time Full-State Error Performance

IEEE Access 2025年 13卷 68224-68231页

作者： Yu, Pengfei Li, Qing Beijing Information Science and Technology University School of Automation Beijing100192 China Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing100101 China

This paper presents a fully distributed, low-complexity UAV formation controller design with fixed-time full-state error performance, which is able to address the difficulties in obtaining global information and suppressing external disturbances without observer or adaptive law in existing solutions. The position of neighboring UAVs is the only information to be exchanged, and desired state observer is not required, thereby reducing communication costs, which is well-suited to larger-scale UAV formation systems. The proposed control scheme has the capability to handle external disturbances without observer or adaptive law, simplifying the controller's design and implementation process, reducing computational burden. Moreover, by imposing fixed-time performance constraints on tracking error and virtual error, fixed-time formation tracking error and virtual velocity tracking error can be achieved, which also improves the response speed and accuracy of UAV formation, making it more capable of meeting the time requirements of actual tasks and adapting to dynamically changing environments. Additionally, all closed-loop signals are rigorously proven to be semi-globally ultimately uniformly bounded. Finally, numerical simulation is conducted to verify the effectiveness of proposed control scheme. © 2013 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Joint Task Offloading and Resource Allocation Strategy for Space-Air-Ground Integrated Vehicular Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1027-1043页

作者： Yuanshuo Gang Yuexia Zhang Zhihai Zhuo School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

Space-Air-Ground integrated Vehicular Network(SAGVN)aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular ***,there are still challenges to overcome,including the scheduling of multilayered computational resources and the scarcity of spectrum *** address these problems,we propose a joint Task Offloading(TO)and Resource Allocation(RA)strategy in SAGVN(namely JTRSS).This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO,and enhances the edge-computing resources of the system by deploying edge *** minimize the system average cost,we use the JTRSS algorithm to decompose the original problem into a number of subproblems.A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational *** acquire the optimal TO decision,a differential fusion cuckoo search algorithm is *** simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.

关键词： edge computing Resource Allocation(RA) Space-Air-Ground integrated Vehicular Network(SAGVN) Task Offloading(TO)

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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers

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CSEE Journal of Power and Energy Systems 2025年第2期11卷 661-670页

作者： Mao Yang Xiaoxuan Shen Dawei Huang Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China

In recent years, a spurt of photovoltaic power generation has brought certain impact on stability of the power system, which puts forward higher requirements on accuracy of photovoltaic power prediction. Therefore, this paper proposes a hybrid power prediction model based on fluctuation classification and feature factor extraction. First, based on fluctuation characteristics of photovoltaic power, fluctuation classification is applied to forecast power before the day, and weather is divided into complex fluctuation types and simple types. Then, parallel factor algorithm is used to reduce prediction model redundancy, which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. Finally, the Long Short-Term Memory (LSTM) deep learning model is used to forecast very short-term photovoltaic power. The proposed hybrid model is compared with other methods, and photovoltaic data from several sites are selected for comparison and validation in this paper. Simulation results show that very short-term prediction method of photovoltaic power proposed in this paper can significantly improve prediction accuracy.

关键词： Deep neural networks fluctuation classification PARAFAC photovoltaic generation very short-term forecasting

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A Study for Inter-Satellite Cooperative Computation Offloading in LEO Satellite Networks

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China Communications 2025年第2期22卷 12-25页

作者： Gang Yuanshuo Zhang Yuexia Wu Peng Zheng Hui Fan Guangteng Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 102206China 2 National Innovation Institute of Defense Technology Academy of Military ScienceBeijing 100071China

Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground *** paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite ***,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization ***,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two *** terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational *** deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by *** results show that the ICCO algorithm can effectively reduce the delay and energy consumption.

关键词： computation offloading inter-satellite co-operation LEO satellite networks

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Reactive Power Optimization Model of Active Distribution Network with New Energy and Electric Vehicles

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Energy Engineering 2025年第3期122卷 985-1003页

作者： Chenxu Wang Jing Bian Rui Yuan Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control&Renewable Energy TechnologyMinistry of EducationJilin132011China

Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is ***,the k-medoids clustering algorithm is used to divide the reduced power scene into ***,the discrete variables and continuous variables are optimized in the same period of ***,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging *** to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution *** simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.

关键词： Active distribution network new energy electric vehicles dynamic reactive power optimization kmedoids clustering hybrid optimization algorithm

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Orthogonal Time Frequency Space with Sub-Band Index Modulation

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IEEE Communications Letters 2025年第5期29卷 1047-1051页

作者： Zhang, Si-Yu Zhang, Yue-Xia Shahrrava, Behnam Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing100101 China University of Windsor Department of Electrical and Computer Engineerning Windsor N9B 3P4 Canada

This letter proposes an innovative orthogonal time-frequency space with sub-band index modulation (OTFS-SBIM) scheme. To overcome the complexity and spectral efficiency (SE) limitations inherent in the OTFS-IM, this study introduces a transmission mechanism where sub-carriers are equally divided into two sub-bands, and the index modulation (IM) operations are restricted to a single sub-band. To offset the potential SE loss, the proposed OTFS-SBIM employs a band-controlling bit β and performs IM independently in the in-phase and quadrature (I/Q) dimensions. These improvements ensure both the diversity gain and enhanced bit error rate (BER) performance. Further, a low-complex maximum likelihood (ML) detection is proposed for bit recovery. Finally, analytical expressions for average bit error probability (ABEP) and peak-to-average power ratio (PAPR) are derived and used to evaluate the OTFS-SBIM’s performance. Simulation results demonstrate that the proposed scheme outperforms the OTFS-IM scheme in terms of the BER and PAPR metrics while maintaining higher SE. © 1997-2012 IEEE.

关键词： Signal modulation

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MPGA-NET: A MULTI-PATH GUIDED ATTENTION NETWORK FOR DESERT SEISMIC NOISE ATTENUATION

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International Journal of Innovative Computing, Information and control 2025年第3期21卷 667-682页

作者： Zhong, Tie Song, Yaling Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education China School of Electrical Engineering Northeast Electric Power University No 169 Changchun Road Jilin 132012 China

Desert seismic exploration is a key technology for detecting natural resources. However, due to the unique surface conditions and complex environmental factors in desert regions, seismic data are often contaminated by strong noise, which poses serious challenges for subsequent data processing. Furthermore, background noise not only exhibits high intensity and a wide frequency band but also overlaps with effective seismic signal in the frequency spectrum, rendering it challenging for traditional signal processing techniques to separate noise from signals. In recent years, denoising models based on convolutional neural network (CNN) have shown significant potential for denoising tasks due to their strong feature extraction and learning capabilities. However, existing models have limitations in feature extraction and representation, which may affect denoising performance. To address this shortcoming, this paper proposes a new Multi-Path Guided Attention Network (MPGA-Net). Specifically, MPGA-Net integrates three paths: the primary core path is responsible for multi-scale feature extraction;the guided enhancement path enhances feature representation;and the attention assisted path focuses on key information. Experimental results show that MPGA-Net not only effectively suppresses noise but also recovers weak reflected signals masked by noise, achieving denoising performance that is significantly superior to both traditional methods and existing CNN approaches. © 2025, ICIC International.

关键词： Convolutional neural networks

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Bandwidth enhancement and the flattening of a chaotic laser with a microring resonator

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Optics Express 2025年第9期33卷 19535-19545页

作者： Guo, Zhi-Yong Li, Jian-Cheng Qiao, Li-Jun Wei, Xiao-Jing Zhang, Ming-Jiang College of Physics and Optoelectronic Engineering Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Key Laboratory of Precision Measurement Physics Taiyuan University of Technology Taiyuan030024 China

We propose and experimentally demonstrate what we believe to be a novel scheme for bandwidth enhancement and flattening of a chaotic laser with a microring resonator. By leveraging the multi-beam interference effect within the MRR, the multiple chaotic laser exhibited improved bandwidth and power spectrum flatness. With a feedback strength of 6 and a laser bias current of 11.8 mA, the chaotic laser with a power spectrum that extends to 22.65 GHz within -3 dB is experimentally achieved. Additionally, we investigated the impact of the different feedback strengths and bias currents on the bandwidth and flatness of the chaotic laser. Finally, we investigated the correlation between the two channels, which can produce single-channel chaotic lasers with correlations below 0.25 over a wide range. This method offers promising chaotic light sources for applications in chaotic distributed fiber optic sensing, secure communication, and chaotic LIDARs. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Bandwidth

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GD-YOLO:A Network with Gather and Distribution Mechanism for Infrared Image Detection of Electrical Equipment

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Computers, Materials & Continua 2025年第4期83卷 897-915页

作者： Junpeng Wu Xingfan Jiang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China

As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical *** response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution *** combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel ***,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution ***,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over *** GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,***,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.

关键词： Infrared image detection aggregation and distribution mechanism sample imbalance strategy lightweight structure

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Investigating Black-Box Model for Wind Power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 227-242页

作者： Mao Yang Chuanyu Xu Yuying Bai Miaomiao Ma Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Daqing Power Industry Bureau Daqing 163000China

Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power *** learning(ML)algorithms have recently attracted increasing attention in the field of ***,opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling *** study develops a method for identifying risky models in practical applications and avoiding the ***,a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model *** that basis,a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in ***,by revealing the operational mechanism for local samples,human interpretability of the black-box model is examined under different accuracies,time horizons,and *** interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting ***,further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection *** results from a wind farm in China show that error can be robustly reduced.

关键词： Black-box model correlation analysis feature trust index local interpretability local interpretable modelagnostic explanations(LIME) wind power forecasting

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