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CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

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Journal of electronic science and technology 2024年第2期22卷 77-90页

作者： Xiao-Qian Lu Jun Tian Qiang Liao Zheng-Wu Xu Lu Gan Bidding and Purchasing Center University of Electronic Science and Technology of ChinaChengdu611731China Yibin Research Institute University of Electronic Science and Technology of ChinaYibin644000China school of Information and Communication engineering University of Electronic Science and Technology of ChinaChengdu611731China

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention ***,a traversal search is conducted through the traversal layer for finite parameters in the phase ***,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input ***,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction *** model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space ***,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction *** experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction ***,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space *** experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

关键词： Chaotic time series Incremental attention mechanism Phase-space reconstruction

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of computer science and technology Harbin Engineering UniversityHarbin 150001China school of computer science and technology Shandong UniversityQingdao 266237China Department of computer science Georgia State UniversityAtlantaGA 30303USA College of Information science and technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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Data augmentation method for insulators based on Cycle-GAN

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Journal of electronic science and technology 2024年第2期22卷 36-47页

作者： Run Ye Azzedine Boukerche Xiao-Song Yu Cheng Zhang Bin Yan Xiao-Jia Zhou school of Automation engineering University of Electronic Science and Technology of ChinaChengdu611731China school of Electrical engineering and computer science University of OttawaOttawaK1N6N5Canada Yangtze River Delta Research Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China

Data augmentation is an important task of using existing data to expand data *** generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated ***,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator *** solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator *** attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect *** experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.

关键词： Data expansion Deep learning Generate confrontation network Insulator

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Construction of LiCl/LiF/LiZn hybrid SEI interface achieving high-performance sulfide-based all-solid-state lithium metal batteries

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science china Chemistry 2024年第6期67卷 1990-2001页

作者： Chaochao Wei Yujie Xiao Zhongkai Wu Chen Liu Qiyue Luo Ziling Jiang Lin Li Liang Ming Jie Yang Shijie Cheng Chuang Yu State Key Laboratory of Advanced Electromagnetic technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan430074China school of Chemistry and Chemical engineering Huazhong University of Science and TechnologyWuhan430074China

Sulfide-based all-solid-state lithium metal batteries(ASSLMBs)have received extensive attention due to their high energy density and high safety,while the poor interface stability between sulfide electrolyte and lithium metal anode limits their ***,a hybrid SEI(LICl/Li F/Li Zn)was constructed at the interface between Li_(5.5)PS_(4.5)Cl_(1.5)sulfide electrolyte and lithium *** Li Cl and Li F interface phases with high interface energy effectively induce the uniform deposition of Li^(+)and reduce the overpotential of Li^(+)deposition,while the Li Zn alloy interface phase accelerates the diffusion of lithium *** synergistic effect of the above functional interface phases inhibits the growth of lithium dendrites and stabilizes the interface between the sulfide electrolyte and lithium *** hybrid SEI strategy exhibits excellent electrochemical performance on symmetric batteries and all-solid-state *** symmetrical cell exhibits stable cycling performance over long duration over 500 h at 1.0 mA cm^(-2).Moreover,the LiNbO_(3)@NCM712/Li_(5.5)PS_(4.5)Cl_(1.5)/Li-10%Zn F_(2)battery exhibits excellent cycle stability at a high rate of 0.5 C,with a capacity retention rate of 76.4%after 350 cycles.

关键词： argyrodite electrolytes ASSLMBs electrochemical stability Li-ZnF_2 anode electrochemical performance

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Fabrication of YAG:Ce^(3+) and YAG:Ce^(3+),Sc^(3+) Phosphors by Spark Plasma Sintering Technique

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Journal of Wuhan university of technology(Materials science) 2024年第2期39卷 255-260页

作者：周卫新娄朝刚 school of electronic science and engineering Southeast UniversityNanjing 210096China

In this study,a single-doped phosphors yttrium aluminum garnet(Y_(3)Al_(5)O_(12),YAG):Ce^(3+),single-doped YAG:Sc^(3+),and double-doped phosphors YAG:Ce^(3+),Sc^(3+) were prepared by spark plasma sintering(SPS)(lower than 1 200℃).The characteristics of synthesized phosphors were determined using scanning electron microscopy(SEM),X-ray diffraction(XRD),and fluorescence *** SPS,the lattice structure of YAG was maintained by the added Ce^(3+) and Sc^(3+).The emission wavelength of YAG:Ce^(3+) prepared from SPS(425-700 nm) was wider compared to that of YAG:Ce^(3+) prepared from high-temperature solid-state reaction(HSSR)(500-700 nm).The incorporation of low-dose Sc^(3+) in YAG:Ce^(3+) moved the emission peak towards the short wavelength.

关键词： high-temperature solid-state reaction spark plasma sintering yttrium aluminum garnet phosphors

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Electromagnetic scattering and imaging simulation of extremely large-scale sea-ship scene based on GPU parallel technology

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Journal of electronic science and technology 2024年第2期22卷 16-23页

作者： Cheng-Wei Zhang Zhi-Qin Zhao Wei Yang Li-Lai Zhou Hai-Yu Zhu Southwest china Institute of electronic technology Chengdu610036China school of electronic science and engineering University of Electronic Science and Technology of ChinaChengdu611731China Tencent Company Shenzhen518054China

Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships,a high-frequency method by using graphics processing unit(GPU)parallel acceleration technique is *** the implementation of different electromagnetic methods of physical optics(PO),shooting and bouncing ray(SBR),and physical theory of diffraction(PTD),a parallel computing scheme based on the CPU-GPU parallel computing scheme is realized to balance computing ***,a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing *** using the established simulation platform,signals of ships at different seas are simulated and their images are achieved as *** is shown that the higher sea states degrade the averaged peak signal-to-noise ratio(PSNR)of radar image.

关键词： Multi graphics processing unit Radar imaging Sea-ship Shooting and bouncing rays

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Online Pareto optimal control of mean-field stochastic multi-player systems using policy iteration

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science china(Information sciences) 2024年第4期67卷 21-37页

作者： Xiushan JIANG Yanshuang WANG Dongya ZHAO Ling SHI College of New Energy China University of Petroleum(East China) Department of electronic and computer engineering Hong Kong University of Science and Technology

In this study, the Pareto optimal strategy problem was investigated for multi-player mean-field stochastic systems governed by It? differential equations using the reinforcement learning(RL) method.A partially model-free solution for Pareto-optimal control was derived. First, by applying the convexity of cost functions, the Pareto optimal control problem was solved using a weighted-sum optimal control problem. Subsequently, using on-policy RL, we present a novel policy iteration(PI) algorithm based on the Hrepresentation technique. In particular, by alternating between the policy evaluation and policy update steps,the Pareto optimal control policy is obtained when no further improvement occurs in system performance,which eliminates directly solving complicated cross-coupled generalized algebraic Riccati equations(GAREs).Practical numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

关键词： mean-field stochastic systems Pareto optimal control policy iteration scheme ℌ-representation

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Sparse antenna array design methodologies:A review

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Journal of electronic science and technology 2024年第3期22卷 1-15页

作者： Pan Wu Yan-Hui Liu Zhi-Qin Zhao Qing-Huo Liu school of electronic science and engineering University of Electronic Science and Technology of ChinaChengdu611731China Eastern Institute of technology Ningbo315200China

Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are *** arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered *** numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is *** work aims to fill this gap by thoroughly summarizing these *** study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and ***,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.

关键词： Clustered array Nonuniformly spaced array Sparse antenna array Synthesis method Thinned array

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Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua science and technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang school of computer science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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science china(Information sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU school of computer science and engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology school of computer science and technology Wuhan University of Science and Technology school of Future technology South China University of Technology Shenzhen Research Institute of Hunan university school of computer science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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