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2024 International Conference on Energy and Electrical engineering, EEE 2024

作者： Zhu, Yi Li, Jiguang Zhang, Kai Tian, Hui Zhou, Kan College of Field Engineering Army Engineering University of Pla Nanjing China School of Aircraft Xi'an Aeronautical Institute Xi'an China College of Command and Control Engineering Army Engineering University of Pla Nanjing China

ISBN: (纸本)9798350341768

To address the issue of geometric consistency in the formation of unmanned aerial vehicles (UAVs) during flight, from the perspective of formation control, an identification method for the core nodes of the temporal network is introduced to analyze and evaluate the geometric position error of the UAV formation. A decoupling controller is designed to improve the geometric consistency of the UAV formation. During the design process, the UAVs' dynamics model is established and a Leader-Follower formation controller is designed based on LMI and virtual Leader. The node importance identification of the UAV formation is determined by constructing a time series network according to the similarity based super adjacency matrix (SSAM) of nodes, and the controller is improved in the light of the results of node importance recognition. In the simulation process, the tracking effect of the dual aircraft formation was first verified and analyzed. Then, taking the 6-aircraft formation as an example, the relationship between node importance and geometric consistency was analyzed, and the controller was improved. The simulation results showed that the improved controller, taking into account the factors affecting node importance, can improve the geometric consistency of the formation, providing meaningful reference value for the study of maintaining and consistency control of Formation organization and formation maintenance formations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Prioritization Method of Test Data for Intelligent Software based on Multi-objective Optimization 9

Prioritization Method of Test Data for Intelligent Software ...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Xia, Chunyan Huang, Yifan Huang, Song Zheng, Changyou Wang, Yutong Wang, Yu College of Command and Control Engineering Army Engineering University of Pla China College of Computer and Information Technology Mudanjiang Normal University China Illinois Institute of Technology United States

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04% 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing. © 2022 IEEE.

关键词： Multiobjective optimization

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An Approach to Coordinated control of Structured Unmanned Swarm based on Evolutionary Game

An Approach to Coordinated Control of Structured Unmanned Sw...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Minggang Yu Ming He Dongge Zhang Ling Luo Jintao Liu Legui Zhang Institute of Command and Control Engineering Institute of Communication Engineering Army Engineering University of PLA Nanjing China Institute of Command and Control Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

To solve the problem of autonomous collaboration of unmanned cluster in military operations, an approach to coordinated control of structured unmanned swarm based on evolutionary game is proposed. It starts with the requirement analysis of task collaboration in unmanned cluster; and the collaboration model based on evolutionary game theory is established; Then the evolutionary dynamics and equilibrium is constructed by theoretical derivation, furthermore, the formation conditions of autonomous collaboration is obtained; Finally the strategy evolution process of swarm on regular lattice and general topology is simulated via case study, which proves the rationality and feasibility of the proposed model and method. The conclusion of this paper provides a preliminary and meaningful exploration for the transformation from theories to practical applications.

关键词： Programmable logic arrays command and control systems Games Zirconium Mathematical model Lattices

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Fractal evaluation model based on fractal dimension and its applications in reinforcement learning

Fractal evaluation model based on fractal dimension and its ...

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第35届中国控制与决策会议

作者： Li Yuan Wang Jun Li Ge Zhang Ren Wen Sun Rui Jin Army Engineering University of PLA Command Control Engineering Institute

Game reinforcement learning has many inherent difficulties in the research process,such as the existence of pure strategy Nash equilibrium,the slow convergence speed of the algorithms,and the uncertainty in reinforcement learning *** characterization of the uncertainty will determine the scientificity and rationality of the *** test of the generality of the algorithms is mainly reflected in whether the algorithm can solve discrete problems and continuous *** main idea of the evaluation model based on fractal dimension is that the agent's strategy selection is aimed at maximizing the fractal dimension of the current *** fractal dimension at this time can be a set or a function and the above two cases correspond to discrete and continuous *** the discussion of Lyapuno stability theory,it can be concluded that the larger the fractal dimension,the more stable the state.

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Learning predictive control of wheeled mobile robot with communication delays 3

Learning predictive control of wheeled mobile robot with com...

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2024 3rd International Conference on Electrical, control and Information Technology, ECITech 2024

作者： Wu, Ruizhuo Xu, Xin He, Ming Liu, Daxue Zhang, Xinglong College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China Institute of Command and Control Engineering Army Engineering University Jiangsu Nanjing210007 China

This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Mobile robots

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Research on ldos attack detection and defense mechanism in software defined networks 6th

Research on ldos attack detection and defense mechanism in s...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Xie, Shengxu Xing, Changyou Zhang, Guomin Wei, Xianglin Hu, Guyu Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China The 63rd Research Institute National University of Defense Technology Nanjing210007 China

ISBN: (纸本)9789811590306

The LDoS (Low rate Denial of Service) attack that aims at exhausting the limited SDN switch buffer resource is hard to detect and degrade network performance seriously. To solve such a problem, this paper proposes an SDN LDoS detection and defense mechanism ADAR (Attack-flow Detection and Attack-port Recognition), which can detect the attack flows based on the collected statistical data, and identify and suppress these attack flows. The experimental results show that ADAR can effectively detect the SDN switch buffer overflow LDoS attacks, and mitigate their impact by using the attack port suppression method. Meanwhile, it can also effectively alleviate the problem of switch buffer overflow caused by the normal traffic burst in the network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Software defined networking

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Name Disambiguation Based on Entity Relationship Graph in Big Data 7th

Name Disambiguation Based on Entity Relationship Graph in Bi...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Li, Gengsong Li, Hongmei Pan, Yu Li, Xiang Liu, Yi Zheng, Qibin Diao, Xingchun Defense Innovation Institute Beijing100071 China Academy of Military Sciences Beijing100091 China Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9789811989902

Aiming at the problem of insufficient utilization of author information and low accuracy of the existing name disambiguation methods, a name disambiguation method based on commonly used author information entity relationship graph is proposed. The entity relationship graph is constructed according to the information of the co-authors and the authors’ affiliated institutions, years of birth, gender and degrees, and the edges in the graph is divided into two categories: the vertices in the first type edges are the authors, and the vertices in the second type edges must include any one of the affiliated institution, year of birth, gender and degree. The connection strength of two authors with the same name in the graph is calculated by following steps: first, the length of the paths is limited;second, the first type edges and the second type edges are searched respectively in the graph;then, the connection strengths of different types of paths are calculated and normalized according to the number and the length of paths, and weighted summed to obtain the connection strength between two authors;finally, the obtained connection strength is compared with the threshold to realize name disambiguation. The experimental results show that the proposed method has higher accuracy than baselines. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Effect of Neutrinos on Angular Momentum of Dark Matter Halo

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Research in Astronomy and Astrophysics 2023年第8期23卷 355-363页

作者： Yu Chen Chang-Zhi Lu Yu Lu Tingting Zhang Tong-Jie Zhang Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Department of Astronomy Beijing Normal UniversityBeijing 100875China Beijing Planetarium Beijing Academy of Science and TechnologyBeijing 100044China College of Command and Control Engineering PLA Army Engineering UniversityNanjing 210017China Institute for Astronomical Science Dezhou UniversityDezhou 253023China

Massive neutrinos are expected to affect the large-scale structure formation,including the major component of solid substances,dark matter *** halos are influenced by neutrinos is vital and interesting,and angular momentum(AM)as a significant feature provides a statistical perspective for this *** halos from TianNu N-body cosmological simulation with the co-evolving neutrino particles,we obtain some concrete ***,by comparing the same halos with and without neutrinos,in contrast to the neutrino-free case,over 89.71%of halos have smaller halo moduli,over 71.06%have smaller particle-mass-reduced(PMR)AM moduli,and over 95.44%change their orientations of less than 0°.***,the relative variation of PMR modulus is more visible for low-mass ***,to explore the PMR moduli of halos in dense or sparse areas,we divide the whole box into big cubes,and search for halos within a small spherical cell in a single *** the two-level divisions,we discover that in denser cubes,the variation of PMR moduli with massive neutrinos decreases more *** distinction suggests that neutrinos exert heavier influence on halos'moduli in compact *** massive neutrinos,most halos(86.60%)have lower masses than without neutrinos.

关键词： (cosmology:)dark matter-neutrinos-(cosmology:)large-scale structure of universe-methods data analysis

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Output Kernel Learning for Multi-Output Regression

Output Kernel Learning for Multi-Output Regression

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International Conference on Instrumentation, Measurement, Computer, Communication and control (IMCCC)

作者： Yanqin Tang Zhisong Pan Yanyan Zhang Institute of Command Control Engineering Army Engineering University of PLA Nanjing

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682470

Multi-output regression is the extension of single output learning, which requires one input sample to predict multiple real-valued output variables simultaneously. Previous studies have shown that multi-output learning yield to a better predictive performance, mostly because of the multivariate nature and the compound dependencies between the multiple feature/target variables. But these relationships are sometimes hard to detect directly, so it is a challenge to reveal the relationship between these input features and output variables. In order to improve the accuracy of the prediction, we propose a new kernel-based multi-output regression method to study the internal relationship between output variables, called KMOR. Our method is searched the framework between multiple functions and output into a reproducing kernel Hilbert space of vector-valued functions. It constructs input kernel by user-defined to represent the relationship between input characteristics and output kernel by auto-learned to represent the relationship between output variable, we also use an efficient alternating optimization to solve the objective function. We have done experiments on fifteen data sets from a variety of interesting application domains and use relative root mean squared error (RRMSE) as performance measurement, which show that this method can improve the accuracy of regression and has very fast convergence speed in comparison with state-of-the-art methods for multi-output regression.

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A Semisupervised Graph Convolutional Neural Network--based Earth Station Network Planning Method

A Semisupervised Graph Convolutional Neural Network--based E...

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Ubiquitous Communication (Ucom), International Conference on

作者： Honghua Zhao Xuemiao Sun Guyu Hu Ke Ding Institute of Command and Control Engineering Army Engineering University NANJING CHINA

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Network planning is used for the rational allocation of satellite communication resources. In this paper, the low utilization and disorderly allocation problems of satellite communication network resources caused by the increasing demand for satellite communication are addressed. An algorithm based on a semisupervised graphical convolutional neural network for network planning is proposed to achieve a rational division of communication resources while improving the resource utilization rate. Based on the network management architectures of existing satellite communication systems, a system model is constructed based on the interoperability conditions of satellites and Earth stations, and the semisupervised graph convolution method is applied to Earth station network planning. Reasonable simulation parameters are set according to the characteristics of geosynchronous satellite networks, and simulation experiments are conducted on the Python platform. The simulation results show that the proposed method can rapidly group Earth stations according to the set optimization objectives and improve user experience and network resource utilization.

关键词： Earth Convolution Simulation Neural networks User experience Planning Resource management Convolutional neural networks Satellite communications Optimization

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