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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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Multi-stage Attention Mechanism based Deep Learning for Wind Power Forecasting 43

Multi-stage Attention Mechanism based Deep Learning for Wind...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Guanzhi Cao, Yundong Jin, Huaikang Fan, Shouyuan Wang, Bin Jin, Huaiping Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China Huaneng Renewables Co. Ltd Yunnan Branch Kunming650000 China China Energy Engineering Group Yunnan Electric Power Design Institute Kunming650051 China

ISBN: (纸本)9789887581581

The accurate prediction of wind power is of great significance for the stable operation of the power system. However, due to the inherent intermittency and randomness of wind speed, existing time series prediction models struggle to capture the complex nonlinear relationship between wind speed and wind power, resulting in low prediction accuracy. Therefore, this study proposes a multi-stage attention long short-term memory network (MA-LSTM) prediction model. Firstly, convolution and attention mechanisms are fused to form the first stage of attention, extracting multi-dimensional features of dynamic changes in wind power data. Secondly, the feature vectors with spatial importance weights are input into the LSTM network for temporal feature extraction. Subsequently, a second stage attention mechanism assigns different weights to hidden state features at various time points. Finally, to counteract the low quality of the second stage attention extraction hidden state caused by the randomness of wind power data, a random difference structure is employed, forming multi-stage attention and LSTM units in parallel to develop the proposed model. The proposed method is evaluated using a real wind farm dataset, and the experimental results show that the MA-LSTM model can better capture the spatiotemporal characteristics of historical wind power data and provide higher quality time step hidden features, thereby significantly improving the accuracy of wind power prediction. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Wind forecasting

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Deep Learning Wind Power Forecasting Method Integrating Architecture Comparison-based Evolutionary Neural Architecture Search 43

Deep Learning Wind Power Forecasting Method Integrating Arch...

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43rd Chinese Control Conference, CCC 2024

作者： Zhou, Wang Cao, Yundong Jin, Huaikang Fan, Shouyuan Wang, Bin Jin, Huaiping Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China Huaneng Renewables CO. Ltd Yunnan Branch Kunming650000 China China Energy Engineering Group Yunnan Electric Power Design Institute Kunming650051 China

ISBN: (纸本)9789887581581

Wind energy has become an important part of the power system due to its versatility, low cost, and environmental friendliness. However, the instability and volatility of wind energy pose a huge challenge to the grid-connected operation of wind power, affecting the effective scheduling of the power grid. In this situation, accurate forecasting of wind power becomes crucial, and deep neural networks have been widely used in this field due to their excellent ability to process high-dimensional nonlinear spatiotemporal data. However, excellent deep neural network architectures are often manually designed by users with extensive expertise, which is time-consuming and results in high uncertainty. In order to automatically design suitable deep neural network architectures for different wind power forecasting tasks, this paper proposes a deep learning wind power forecasting method based on architecture comparison evolutionary neural architecture search. This method first encodes neural network architectures and then uses evolutionary algorithms to search for the optimal architecture. Among them, to address the problem of slow fitness calculation in evolutionary algorithms, architectures comparison-based the method is used instead of fitness calculation for architecture search, greatly accelerating search efficiency. The effectiveness and superiority of the proposed method were verified through case studies on actual wind farm datasets. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Wind forecasting

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Second Nearest-Neighbor Modified Embedded Atom Method Interatomic Potential for Cu-Ni-Sn Ternary System

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Computers, Materials & Continua 2025年第4期83卷 65-77页

作者： Jialiang Dong Xuemao Dong Zhongxue Feng Caiju Li Jianhong Yi Jun Tan School of Materials Science and Engineering Guizhou Minzu UniversityGuiyang550025China Guangdong Key Laboratory for Advanced Metallic Materials Processing and Forming National Engineering Research Center of Near-Net-Shape Forming for Metallic MaterialsSouth China University of TechnologyGuangzhou510640China Faculty of Electronic and Information Engineering Anshun UniversityAnshun561000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China College of Materials Science and Engineering Chongqing UniversityChongqing400044China

To explore atomic-level phenomena in the Cu-Ni-Sn alloy,a second nearest-neighbor modified embedded-atom method(2NN MEAM)potential has been developed for the Cu-Ni-Sn system,building upon the work of other *** potential demonstrates remarkable accuracy in predicting the lattice constant,with a relative error of less than 0.5%when compared to density functional theory(DFT)results,and it achieves a 10%relative error in the enthalpy of formation compared to experimental data,marking substantial advancements over prior *** bulk modulus is predicted with a relative error of 8%compared to ***,the potential effectively simulates the processes of melting and solidification of Cu-15Ni-8Sn,with a simulated melting point that closely aligns with the experimental value,within a 7.5%*** serves as a foundation for establishing a 2NN MEAM potential for a flawless Cu-Ni-Sn system and its microalloying systems.

关键词： 2NN MEAM Cu-Ni-Sn interatomic potential atomistic simulation

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A Survey of Deep Learning Target Detection Algorithms under Candidate Regions 2

A Survey of Deep Learning Target Detection Algorithms under ...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Zou, Huayu Wang, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Province Kunming650504 China

ISBN: (纸本)9781665442763

Target detection is a research hotspot in the field of computer vision. The target detection algorithm based on candidate regions has high accuracy, but the structure is complex, and the detection speed is slow. The regression-based target detection algorithm has simple structure and fast detection speed, and it is in real-time target detection The field has high application value, but the detection accuracy is relatively low. Based on this research background, this paper first analyses the mechanism of shadow generation, understands the characteristics of shadows and human visual characteristics, and summarizes and classifies the current existing features. Based on the similar shadow detection algorithm, a shadow detection algorithm based on the RGB colour model is proposed. The algorithm in this paper is verified through experiments. The article proves that the algorithm can detect and remove the shadows of moving targets well. © 2022 IEEE.

关键词： Security systems

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Target Detection and Multi-Feature Fusion Based on Deep Learning and CamShift Algorithm 22

Target Detection and Multi-Feature Fusion Based on Deep Lear...

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3rd Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Zou, Huayu Wang, Jian Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Province Kunming650504 China

ISBN: (纸本)9781450395786

Aiming at the problem that the continuous adaptive mean shift algorithm (Camshift) deep learning tracking algorithm based on the colour probability distribution can easily cause the tracking target to fail when the same colour interference appears in the background, we propose an improved Camshift tracking algorithm. The article first conducts target detection before Camshift tracks the target, and compares three detection algorithms through frame difference method, optical flow method, and background difference method. Then, when tracking the target, the window centre of the CamShift algorithm is compared with the centre of the target area calculated by the difference method to determine the subsequent frame search window to avoid the loss of target tracking;finally, the experiment proves that the method can effectively track the target, and it still has a very high accuracy rate when the target colour and the background colour are small. © 2022 ACM.

关键词： Target tracking

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Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction for Visual Grounding

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IEEE Transactions on Multimedia 2025年

作者： Xie, Minghong Wang, Mengzhao Li, Huafeng Zhang, Yafei Tao, Dapeng Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University FIST LAB School of Information Science and Engineering Kunming650091 China

Visual grounding has attracted wide attention thanks to its broad application in various visual language tasks. Although visual grounding has made significant research progress, existing methods ignore the promotion effect of the association between text and image features at different hierarchies on cross-modal matching. This paper proposes a Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction Visual Grounding method. It first generates a mask through decoupled sentence phrases, and a text and image hierarchical matching mechanism is constructed, highlighting the role of association between different hierarchies in cross-modal matching. In addition, a corresponding target object position progressive correction strategy is defined based on the hierarchical matching mechanism to achieve accurate positioning for the target object described in the text. This method can continuously optimize and adjust the bounding box position of the target object as the certainty of the text description of the target object improves. This design explores the association between features at different hierarchies and highlights the role of features related to the target object and its position in target positioning. The proposed method is validated on different datasets through experiments, and its superiority is verified by the performance comparison with the state-of-the-art methods. The source code of the proposed method is available at. © 2025 IEEE.

关键词： Visual languages

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Centromere Landscapes Resolved from Hundreds of Human Genomes

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 61-71页

作者： Shenghan Gao Yimeng Zhang Stephen J. Bush Bo Wang Xiaofei Yang Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China Center for Mathematical Medical The First Affiliated HospitalXi'an Jiaotong UniversityXi'an 710061China School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZThe Netherlands

High-fidelity(HiFi)sequencing has facilitated the assembly and analysis of the most repetitive region of the genome,the ***,our current understanding of human centromeres is based on a relatively small number of telomere-to-telomere assemblies,which have not yet captured its full *** this study,we investigated the genomic diversity of human centromere higher order repeats(HORs)via both HiFi reads and haplotype-resolved assemblies from hundreds of samples drawn from ongoing pangenome-sequencing projects and reprocessed them via a novel HOR annotation pipeline,*** used this wealth of data to provide a global survey of the centromeric HOR landscape;in particular,we found that 23 HORs presented significant copy number variability between *** detected three centromere genotypes with unbalanced population frequencies on chromosomes 5,8,and *** inter-assembly comparison of HOR loci further revealed that while HOR array structures are diverse,they nevertheless tend to form a number of specific landscapes,each exhibiting different levels of HOR subunit expansion and possibly reflecting a cyclical evolutionary transition from homogeneous to nested structures and back.

关键词： Centromere sequence Genome evolution High-fidelity sequencing technology Higher order repeat annotation Human population

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Mn single-atoms decorated CNT electrodes for high-performance supercapacitors

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Nano Research 2024年第5期17卷 4039-4046页

作者： Qian Rong Chao Yuwen Yingkai Liu Yuanrong Liu Chunyang Wang Yang Wang Ding Zhang Zhen Wen Yunnan Key Laboratory of Opto-Electronic Information Technology Institute of Physics and Electronic InformationYunnan Normal UniversityKunming 650500China Faculty of Metallurgical and Energy Engineering Kunming University of Science and TechnologyKunming 650093China Department of Physics and Astronomy University of CaliforniaIrvineCA 92697USA School of Materials Science and Engineering National Institute for Advanced MaterialsSmart Sensing Interdisciplinary Science CenterNankai UniversityTianjin 300350China Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices Institute of Functional Nano and Soft Materials(FUNSOM)Soochow UniversitySuzhou 215123China

Developing highly robust and efficient electrode materials is of critical importance to promoting the energy density of current supercapacitors for ***,we report an efficient catalyst with monodispersed Mn single-atoms embedded in carbon nanotubes(Mn-CNTs)for enhancing the electrode performance of supercapacitors.A high specific capacitance(1523.6 F·g^(-1) at 1.0 A·g^(-1))can be achieved,which is about twice as high as the specific capacitance of the electrode material without the introduction of Mn ***,the asymmetric electrochemical capacitor created with Mn-CNT and activated carbon exhibits a high energy density of 180.8 Wh·kg^(-1) at a power density of 1.4 kW·kg^(-1),much higher than most reported *** study shows that the integration of Mn atoms into the CNT can enhance the charge transport capacity and the number of polar active sites of Mn-CNT and then facilitate chemical interactions between Mn-CNT and OH-.This work provides a novel strategy to enable high-energy storage in supercapacitors by introducing single-atoms into carbon nanotubes to improve electrodes’energy density and cycle life.

关键词： carbon nanotube(CNT) supercapacitor single-atoms power density energy density

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