检索结果-南通市图书馆

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第4期79卷 1005-1023页

作者： Yan Xiang Daofeng Li Xinyi Meng Chengfeng Dong Guanglin Qin School of Computer and Electronics information Guangxi UniversityNanning530004China guangxi Colleges and Universities key laboratory of Multimedia Communications and information Processing Guangxi UniversityNanning530004China

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

关键词： Internet of Things cyberattack intrusion detection internet security

An improved typhoon monitoring model based on precipitable water vapor and pressure

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Geodesy and Geodynamics 2024年第3期15卷 276-290页

作者： Junyu Li Haojie Li Lilong Liu Jiaqing Chen Yibin Yao Mingyun Hu Liangke Huang Fade Chen Tengxu Zhang Lv Zhou College of geomatics and Geoinformation Guilin University of TechnologyGuilin 541006China guangxi key laboratory of spatial information and geomatics Guilin 541006China Qinzhou Institute of Surveying and Mapping for Housing and Urban-Rural Development Co. Ltd.Qinzhou 535000China School of Geodesy and geomatics Wuhan UniversityWuhan 430079China School of Resources and Environmental Science and Engineering Hubei University of Science and TechnologyXianning 437100China

The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

关键词： Typhoon GNSS/ERA5 PWV Pressure Monitoring Improved model

Analysis of phase preservation and interferometric offset test in sparse SAR imaging

维普期刊数据库

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

Science China(information Sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU key laboratory of spatial information Processing and Application System Technology Chinese Academy of Sciences Aerospace information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military Sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

A time-dependent luminescent phosphor of Na_(2)Ba_(2)Si_(2)O_(7):Eu for multi-level encryption and dynamic information display

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Advanced Ceramics 2024年第4期13卷 429-436页

作者： Jie Shao Xiyang Li Meng Liu Haiqin Sun Qiwei Zhang Dengfeng Peng Fuchi Liu College of Physics and Technology Guangxi Normal UniversityGuilin 541004China guangxi key laboratory of Nuclear Physics and Technology Guangxi Normal UniversityGuilin 541004China key laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China

Photo-stimuli responsive materials show great potential in the fields of information encryption and storage due to their distinctive spatial or temporal color changes.However,the conventional single or multi-color static outputs by light stimulus difficulty meet practical requirements for high-security optical storage technologies.Here,a novel dynamic irradiation-responsive phosphor of Na_(2)Ba_(2)Si_(2)O_(7):Eu is demonstrated,exhibiting high storage stability and convenient readout behaviors.The inherent Eu^(2+)luminescence can be dynamically tuned,instantly read out,and conveniently erased by controlling irradiation duration of a portable diode laser(365 nm).The modulation mechanism is unraveled by optically induced oxidation reactions of Eu^(2+)→Eu^(3+)and defects as killer centers.The excellent luminescence modulation degree(ΔRt=89.5%)and the accompanying larger color contrast enable the creation of invisible optical codes with multi-level encryption in bright or dark field.These results indicate potential applications of Na_(2)Ba_(2)Si_(2)O_(7):-based materials in information encryption and invisible optical storage,and are expected to expand more investigations on optically induced PL modulation behaviors based on mixed valences and defects.

关键词： light irradiation luminescent modulation invisible optical storage

A Numerical Calculation Algorithm of the Inclination Function Based on the Representation of SO(3) Group

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 2024年第1期40卷 137-148页

作者： Yu-xiao Wei Wen-lin Tang Department of Geophysics School of Geology Engineering and GeomaticsChang’An UniversityXi’an710064China key laboratory of Electronics and information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing100190China

In the determination of the Earth gravity field in satellite geodesy, the inclination functions represent the projection of data observed along the orbital plane of a satellite orbit into the sphere in the terrestial reference frame. The inclination functions in this work is studied from a group theoretical perspective. The inclination functions are proved to generate a representation of the SO(3) group. An orthogonal relation of the inclination functions is derived and some recurrence relations for the inclination functions are given, based on which an algorithm to calculate the inclination functions is proposed.

关键词： SO(3)group inclination function satellite geodesy representation of SO(3)

维普期刊数据库

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

在线全文

学校读者我要写书评

暂无评论

Science China(information Sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information Science School of Computer and Information TechnologyBeijing Jiaotong University Beijing key laboratory of Advanced information Science and Network Technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades.However, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2111-2136页

作者： Lieping Zhang Xiaoxu Shi Liu Tang Yilin Wang Jiansheng Peng Jianchu Zou key laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology) Education Department of Guangxi Zhuang Autonomous RegionGuilin541006China College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China Guilin Mingfu Robot Technology Company Limited Guilin541199China key laboratory of AI and information Processing(Hechi University) Education Department of Guangxi Zhuang Autonomous RegionYizhou546300China

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

关键词： Autonomous detection RRT algorithm mobile robot ROS Karto SLAM algorithm

Hybrid ITO Algorithm for Large-Scale Colored Traveling Salesman Problem

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing key laboratory of Urban spatial information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid ITO(HITO) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITO. In the iteration process of HITO, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITOis superior to the known comparison algorithms in term of the quality solution.

关键词：

spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第4期139卷 279-296页

作者： Hui Zhang Huaji Zhou Li Wang Feng Zhou The Ministry key laboratory of Electronic information Countermeasure and Simulation Xidian UniversityXi’an710071China Science and Technology on Communication information Security Control laboratory Jiaxing314033China

This paper proposes a novel open set recognition method,the spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

关键词： Electromagnetic signal recognition deep learning feature extraction open set recognition