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Optimized Selection Method of Air Combat Course of Action under Stochastic Uncertainty

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Journal of Systems Science and Systems engineering 2024年第4期33卷 494-518页

作者： Yun Zhong Jieyong Zhang Peng Sun Lujun Wan Kepeng Wang information and Navigation college Air Force Engineering UniversityXi’an710077China Equipment Management and Unmanned Aerial Vehicle engineering college Air Force Engineering UniversityXi’an710051China Air Traffic Control and Navigation college Air Force Engineering UniversityXi’an710051China Unit 94040 of PLA Korla841000China

Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic influence net(DIN)theory,stochastic simulation technique,feedforward neural network(FNN)function approximation technique and multi-objective artificial fish school algorithm(MOAFSA),this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm ***,on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling,the static and dynamic causal relationship modeling and optimization problem modeling were carried out ***,the probability propagation mechanism of DIN was established,which mainly included two aspects,i.e.,the overall process and the specific ***,input and output data were generated based on stochastic *** to these data,FNN was adopted for function approximation,and MOAFSA was adopted for iterative ***,the rationality of the model,and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.

关键词： Stochastic uncertainty aviation swarm stochastic simulation course of action feedforward neural network multi-objective artificial fish school algorithm

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the college of Computer Science and engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of information Science and engineering Ocean University of ChinaQingdao 266000China the college of Computer Science and engineering Shandong University of Science and TechnologyQingdao 266590China the college of Computer Science and engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2..We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Radiation build-up and dissipation in Raman random fiber laser

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Science China(information Sciences) 2024年第1期67卷 254-260页

作者： Shengtao LIN Zinan WANG Jiaojiao ZHANG Pan WANG Han WU Yifei QI Key Laboratory of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China college of Electronics and information {2. Sichuan University

Raman random fiber laser(RRFL) is a complex physical system that arises from the distributed Raman amplification and the intrinsic stochasticity of fiber *** analysis of the underlying lightwave kinetics at steady state has been the focus of considerable ***,the transient state,such as the RRFL build-up and dissipation,is particularly important for revealing the lightwave interaction ***,we investigate for the first time the RRFL dynamics at the transient state and track the RRFL temporal and spectral evolution theoretically and ***,with the contribution of random distributed feedback,RRFL build-up exhibits continuous Verhulst logistic growth curves without cavity-related features,which is significantly different from the step-like growth curve of conventional fiber ***,the radiation build-up duration is inversely related to the pump power,and the spectral evolution of the RRFL undergoes two phases,i.e.,from spectral density increase to spectral *** the steady state to the pump switch-off state,the RRFL output power dissipates immediately,and the remaining Stokes lightwave from the Rayleigh scattering will gradually disappear after one round *** work provides new insights into the transient dynamic features of the RRFL.

关键词： random fiber laser stimulated Raman scattering transient phenomenon nonlinear optics biological growth dynamics

All-day perception for intelligent vehicles: switching perception algorithms based on WBCNet

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Science China(information Sciences) 2024年第11期67卷 269-284页

作者： Hongbin XIE Haiyan ZHAO Chengcheng XU Hong CHEN college of Communication engineering Jilin University college of Electronic and information {2. Tongji University

A weather-and brightness-based classification network(WBCNet) is proposed for driving scene classification to address the decreased accuracy in perception caused by weather and environment *** facilitate its applicability in vehicles and minimize computational demands on vehicle chips, WBCNet has been designed with special modules, including attention mechanisms and dilated convolutions. Dilated convolutions combined with residual connections empower WBCNet to concurrently handle information at various scales. This aids in simplifying the training and optimization of deep networks, consequently enhancing the model's performance and mitigating the risk of overfitting. The outstanding feature association capability originating from the fusion of channel attention and spatial attention enables WBCNet to focus more on the sky, lanes, and other traffic information features within the image. This design enables WBCNet to use only images as input, making it highly suitable for {2. applications. The output of WBCNet provides the basis for the downstream perception model selection algorithm, allowing it to choose the appropriate perception model for different scenes accurately. A dataset with complex scenes based on Carla is constructed for comparison to verify WBCNet's performance. Finally, a real-world driving dataset is used to validate the effectiveness and real-time performance of WBCNet.

关键词： driving scene classification intelligent vehicle safe driving convolutional neural network attention mechanism

Low-Frequency Stability Analysis of Power Electronic Traction Transformer Based Train-Network System

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1608-1617页

作者： Yi Hong Yang Li Zhikang Shuai Hanyu Yang college of Electrical and information {2. Hunan University and the Advanced Energy Technology Center of State Grid Shanghai Energy Interconnection Research Institute Co.Ltd.Shanghai 201203China college of Electrical and information {2. Hunan UniversityChangsha 410082China college of Electrical engineering and Controal Science in Nanjing Tech University Nanjing 211816China

Power electronic traction transformers(PETTs)will be increasingly applied to locomotives in the future for their small volume and light ***,similar to conventional trains,PETTs behave as constant power loads and may cause low-frequency oscillation(LFO)to the train-network *** solve this issue,a mathematical model of the PETT is firstly proposed and verified based on the extended describing function(EDF)method in this *** the proposed model,the LLC converter is simplified to an equivalent circuit consisting of a capacitor and a resistor in *** is further demonstrated that the model can apply to various LLC converters with different topologies and ***,when the parameter differences between cells are not obvious,the PETT can be simplified to a single-phase rectifier(i.e.,conventional train)by equivalent *** on the model of PETT,the system low-frequency stability and influential factors are analyzed by using the generalized Nyquist ***,the correctness and accuracy of theoretical analyses are validated by off-line and hardware-in-the-loop simulation results.

关键词： Extended describing function method impedance model low-frequency oscillation power electronic traction transformer train-network system

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical engineering The University of LahoreLahore54000Pakistan Faculty of Computer and information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

Full-domain collaborative deployment method of multiple interference sources and evaluation of its deployment effect

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Defence Technology（防务技术） 2024年第2期32卷 572-595页

作者： Yue Wang Fuping Sun Xian Wang Jinming Hao Kai Xiao School of Geospatial information The PLA Information Engineering UniversityZhengzhou 450001HenanChina

This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation *** evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment *** principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively ***,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to *** evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.

关键词： Jamming effect Multiple interference sources Collaborative deployment Effect evaluation Defense capability

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Global optimization of double protograph LDPC codes for JSCC scheme

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Science China(information Sciences) 2024年第6期67卷 455-456页

作者： Qiwang CHEN Chen CHEN Yu-Cheng HE Zhiping XU Xiamen Key Laboratory of Mobile Multimedia Communications College of Information Science and EngineeringHuaqiao University Key Laboratory of Underwater Acoustic Communication and Marine information Technology (Xiamen University) Ministry of Education School of Ocean information engineering Jimei University

A double low-density parity-check (D-LDPC) coding system was proposed in[1]as a typical joint source-channel coding(JSCC) scheme[1–5].In this system,two LDPC coding matrices perform source and channel coding,*** important concept in a D-LDPC coding system is the introduction of a linking matrix between the check nodes (CNs)of the source coding matrix and the variable nodes (VNs) of the channel LDPC coding matrix.

关键词： JOINT SOURCE DESIGN

On the size generalizibility of graph neural networks for learning resource allocation

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Science China(information Sciences) 2024年第4期67卷 239-254页

作者： Jiajun WU Chengjian SUN Chenyang YANG Beihang University School of Electronics and information engineering

Size generalization is important for learning resource allocation policies in wireless systems with time-varying scales. If a neural network for learning a wireless policy is not generalizable to the size of its input, it has to be re-trained whenever the system scale changes, which hinders its practical use due to the unaffordable training costs. Graph neural networks(GNNs) have been shown with size generalization ability empirically when optimizing resource allocation. Yet, are GNNs naturally size generalizable? In this paper,we argue that GNNs are not always size generalizable for resource allocation. We find that the aggregation and activation functions of the GNNs for learning a class of wireless policies play a key role in their size generalization ability. We take the GNN with the mean aggregator, called mean-GNN, as an example to reveal a size generalization condition. To demonstrate how to satisfy the condition, we learn power and bandwidth allocation policies for ultra-reliable low-latency communications and show that selecting or pretraining the activation function in the output layer of mean-GNN can make the GNN size *** results validate our analysis and evaluate the performance of the learned policies.

关键词： size generalization graph neural networks resource allocation permutation equivariance