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2024 Asia Pacific Conference on Computing Technologies, Communications and Networking, CTCNet 2024

作者： Zhang, Hongzheng National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9798400709609

In recent years, with the rapid development of deep learning technology, researchers in the field of network security have begun to explore the use of deep learning to solve the problem of encrypted traffic classification. However, existing publicly available encrypted traffic datasets suffer from severe class imbalance issues, which significantly impact the performance of deep learning classification methods. Constructing a complete encrypted traffic dataset from scratch is both time-consuming and costly. To address this issue, this paper proposes an innovative model named TransFlowRGAN, which is an encrypted traffic sequence feature generation model that combines Transformer and Relative Generative Adversarial Network (RGAN). Our model leverages the advantages of RGAN, effectively mitigating the instability and mode collapse issues that traditional GAN models often encounter during training. Additionally, it incorporates the Transformer architecture. The self-attention mechanism of the Transformer can better capture the complex features and long-term dependencies in traffic data, resulting in the generation of higher-quality data. We evaluated the performance of our model on the ISCXVPN2016 public dataset. The results demonstrate that TransFlowRGAN excels in handling imbalanced datasets, significantly outperforming other data augmentation models. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(information sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

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The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian National Engineering and Technology Center for Information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of ***,it’s essential to construct and estimate a yield-related phenotypic trait considering spike *** on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning *** results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area ***3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike *** spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,*** results provide new insights into the role of fruiting organs for the accurate spectral prediction of *** high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation

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Health monitoring by optical fiber sensing technology for rechargeable batteries

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escience 2024年第1期4卷 79-95页

作者： Yi Zhang Yanpeng Li Zezhou Guo Jianbo Li Xiaoyu Ge Qizhen Sun Zhijun Yan Zhen Li Yunhui Huang State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanHubei430074China School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanHubei430074China Henan Key Laboratory of Laser and Opto-electric Information Technology School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouHenan45001China

With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical ***,many traditional sensing technologies suffer from low sensitivity,large size,and electromagnetic interference problems,rendering them unusable in the harsh and complicated electrochemical environments of *** optical sensor is an alternative approach to realize multiple-parameter,multiple-point measurements ***,it has garnered significant *** analyzing these measured parameters,the state of interest can be decoded to monitor a battery's *** review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters,discussing the current limitations of optical fiber sensors as well as directions for their future development.

关键词： Optical fiber sensing Rechargeable battery Real-time sensing Health monitoring

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Feature Distillation GAN: An Adversarial Example Defense Method Based on Feature Reconstruction 2nd

Feature Distillation GAN: An Adversarial Example Defense Met...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： Liu, Ruiqi Li, Hu Wang, Dongxia Zhao, Chongyang Li, Boyu National Key Laboratory of Science and Technology On Information System Security Beijing China

ISBN: (纸本)9789819745180

Purifying and reconstructing adversarial examples into benign ones by generative models is a class of defense methods to eliminate adversarial perturbations. These methods do not make assumptions on adversarial attacks or model architectures, and thus can defend classifiers against unseen threats. In this work, we propose a new GAN-based adversarial purification method Feature Distillation GAN (FDGAN). We consider how to eliminate the feature differences between adversarial examples and benign examples which are difficult for humans to perceive. We extract and analyze the differences between the adversarial examples and the benign examples from multiple dimensionals. On the basis of comparing the differences among various features, we propose a new feature-level perspective on adversarial purification, design a new adversarial example purification method for image inputs, and implement our method based on GAN. Experimental results show that FDGAN is effective against unknown adversarial attacks and outperforms all the purification methods we used for comparison. Evaluating FDGAN against strong adaptive attacks, the robust accuracy of FDGAN under AutoAttack (L∞ = 8/255) exceeds the SOTA adversarial training methods on the Robustbench. Furthermore, FDGAN has lower training cost and can be combined with a variety of model-based defense strategies to further improve the defense effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distillation

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In-memory computing based on phase change memory for high energy efficiency

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science China(information sciences) 2023年第10期66卷 20-41页

作者： Luchang HE Xi LI Chenchen XIE Zhitang SONG National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Micro-System and Information Technology Chinese Academy of Sciences School of Microelectronics University of Science and Technology of China

The energy efficiency issue caused by the memory wall in traditional von Neumann architecture is difficult to reconcile. In-memory computing(CIM) based on emerging nonvolatile memory(NVM) is a promising solution to avoid data movement between storage and processors and realize highly energy-efficient computing. Compared with other NVM technologies, phase change random access memory(PCM) exhibits comprehensive performance for analog computing. In this paper, we review advanced PCM techniques,including phase-change materials, mechanisms, and unique properties, as a foundation and inspiration for implementing CIM architecture. Meanwhile, state-of-the-art PCM-based CIM systems are well discussed for high energy efficiency in artificial neural networks, spiking neural networks, and other artificial intelligence(AI) applications. Finally, we present the remaining challenges and potential solutions of CIM for further investigation.

关键词： in-memory computing phase change memory energy efficiency neural network memory wall

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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Van der Waals epitaxial growth of single-crystal molecular film

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national science Review 2024年第11期11卷 194-202页

作者： Lixin Liu Penglai Gong Kailang Liu Bingrong Huang Zhihao Zhang Yingshuang Fu Yu Wu Yinghe Zhao Meihui Wang Yongshan Xu Huiqiao Li Tianyou Zhai State Key Laboratory of Materials Processing and Die &Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology Key Laboratory of Optic-Electronic Information and Materials of Hebei Province College of Physics Science and Technology Hebei University Wuhan National High Magnetic Field Center School of Physics Huazhong University of Science and Technology

Epitaxy is the cornerstone of semiconductor technology, enabling the fabrication of single-crystal *** advancements in van der Waals（vd W） epitaxy have opened new avenues for producing wafer-scale single-crystal 2D atomic crystals. However, when it comes to molecular crystals, the overall weak vd W force means that it is a significant challenge for small molecules to form a well-ordered structure during *** we demonstrate that the vd W epitaxy of Sb2O3molecular crystal, where the whole growth process is governed by vd W interactions, can be precisely controlled. The nucleation is deterministically modulated by epilayer–substrate interactions and unidirectional nuclei are realized through designing the lattice and symmetry matching between epilayer and substrate. Moreover, the growth and coalescence of nuclei as well as the layer-by-layer growth mode are kinetically realized via tackling the Schwoebel-Ehrlich barrier. Such precise control of vd W epitaxy enables the growth of single-crystal Sb2O3molecular film with desirable thickness. Using the ultrathin highly oriented Sb2O3film as a gate dielectric, we fabricated MoS2-based field-effect transistors that exhibit superior device performance. The results substantiate the viability of precisely managing molecule alignment in vd W epitaxy, paving the way for large-scale synthesis of single-crystal 2D molecular crystals.

关键词： van der Waals epitaxy inorganic molecular crystal layer-by-layer growth single-crystal film dielectric

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On-orbit attitude planning for Earth observation imaging with star-based geometric calibration

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science China(Technological sciences) 2025年第2期68卷 243-256页

作者： Di HUANG Zhifu WANG Jianya GONG Haoran JI Institute of Aerospace Science and Technology School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingSchool of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China National Key Laboratory of Air-based Information Perception and Fusion Luoyang 471009China Intelligent Game and Decision Laboratory Beijing 100071China

To improve the quality of remote sensing images,a novel on-orbit attitude planning method for Earth observation imaging with star-based geometric calibration is presented in this *** traditional imaging processes,the proposed method includes both pre-calibration and post-calibration stages to enhance the accuracy of satellite geometric positioning.A multiple-constraint attitude planning algorithm was developed to ensure star-based calibration,wherein the satellite camera and star sensors simultaneously capture the images of *** integration of cameras and multiple star sensors enables effective attitude *** the Earth imaging stage,a fast on-orbit attitude planning algorithm was developed to determine the satellite attitude and the optimal imaging times for ground *** with existing methods,the fast on-orbit attitude planning algorithm can significantly reduce computational time and resource consumption via initial value selection and *** demonstrate the effectiveness of the proposed attitude planning method,which was successfully applied to Wuhan 1 satellite.

关键词： attitude planning Earth observation imaging geometric calibration attitude constraints

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