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In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： Classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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Learning Fine-Grained information with Capsule-Wise Attention for Salient Object Detection

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IEEE Transactions on Multimedia 2023年 1-14页

作者： Zhao, Sanyuan Wen, Zongzheng Qi, Qi Lam, Kin-Man Shen, Jianbing School of Computer Science & Technology Beijing Institute of Technology China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macau China

With the popularity of convolutional neural networks being used for salient object detection (SOD), the performance has been significantly improved. However, how to integrate crucial features for modeling salient objects needs further exploration. In this work, we propose an effective feature selection scheme to solve this task. Firstly, we provide a Simplified Atrous Spatial Pyramid Pooling (SASPP) module to lightweight the multi-scale features. Dealing with the SASSP features, we design a pixel-level local feature selection scheme named Multi-Scale Capsule-wise Attention (MSCA). It aggregates features from multi-scales by dynamic routing and helps the network to generate fine-grained prediction maps. In addition, we exploit holistic features by the Spatial-wise Attention and Channel-wise Attention (SA/CA) mechanisms, which adaptively extracts spatial or channel information. We also propose a Multi-crossed Layer Connections (MLC) structure in the upsampling stage, to fuse features from not only different levels but also different scales. The salient object prediction is performed in a coarse-to-fine manner. By conducting comprehensive experiments on five benchmark datasets, our method achieves the best performance when compared to existing state-of-the-art approaches. IEEE

关键词： Object detection

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Learning to Cluster Person in the Video via Graph Convolution Network 13

Learning to Cluster Person in the Video via Graph Convolutio...

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13th International Conference on information technology in Medicine and Education, ITME 2023

作者： Li, Wei Liang, Sixin Bian, Ang Du, Guodong College of Computer Science Sichuan University Chengdu China School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350319156

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module;(ii) a pose-guided embedding module;(iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior. © 2023 IEEE.

关键词： Convolution

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A polarization sensitive interferometer:Delta interferometer

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Chinese Physics B 2024年第3期33卷 369-377页

作者：卫超奇刘建彬董翼飞孙雨农周宇郑淮斌刘严严闫秀生李福利徐卓 Key Laboratory of Multifunctional Materials and Structures Ministry of Education & International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Department of Applied PhysicsXi'an Jiaotong UniversityXi'an 710049China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China

A new type of polarization sensitive interferometer is proposed,named the Delta interferometer,inspired by its geometry resembling the Greek letter *** main difference between the Delta interferometer and other existing interferometers,such as Michelson,Mach-Zehnder and Young's double-slit interferometers,is that the two interfering paths are asymmetrical in the Delta *** visibility of the first-order interference pattern observed in the Delta interferometer is dependent on the polarization of the incidental *** coherence theory is employed to interpret this phenomenon and single-mode continuous-wave laser light is employed to verify the theoretical *** theoretical and experimental results are *** Delta interferometer is a perfect tool to study the reflection of electromagnetic fields in different polarizations and may find applications in polarization-sensitive scenarios.

关键词： phase sensitive interferometer Fresnel formula first-order interference

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Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

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Acta Oceanologica Sinica 2024年第7期43卷 93-111页

作者： Tianyu Zhang Mohammed Shokr Zhida Zhang Fengming Hui Xiao Cheng Zhilun Zhang Jiechen Zhao Chunlei Mi School of Geospatial Engineering and Science Sun Yat-Sen University/Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-Sen University) Ministry of EducationZhuhai 519082China State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Science and Technology Branch Environment and Climate Change CanadaToronto M3H5T4Canada Qingdao Innovation and Development Base(Centre) Harbin Engineering UniversityQingdao 266500China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input *** article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice ***,it estimates the uncertainty of the output in response to the uncertainties of the input *** parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow *** input parameters include air temperature,ice surface temperature,and wind *** the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice *** sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and *** retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved *** in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be *** resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement *** results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

关键词： Arctic sea ice 1-D thermodynamic ice model thin-ice thickness sea ice parameterization

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FCOS-LSC:A Novel Model for Green Fruit Detection in a Complex Orchard Environment

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Plant Phenomics 2023年第3期5卷 546-563页

作者： Ruina Zhao Yujie Guan Yuqi Lu Ze Ji Xiang Yin Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Engineering Cardiff UniversityCardiff CF243AAUK School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China

To better address the difficulties in designing green fruit recognition techniques in machine vision systems,a new fruit detection model is *** model is an optimization of the FCOS(full convolution one-stage object detection)algorithm,incorporating LSC(level scales,spaces,channels)attention blocks in the network structure,and named *** method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions,lighting conditions,and capture ***,the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature *** feature pyramid network(FPN)is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected ***,the attention mechanisms are added to each of the 3 dimensions of scale,space(including the height and width of the feature map),and channel of the generated multiscale feature map to improve the feature perception capability of the ***,the classification and regression subnetworks of the model are applied to predict the fruit category and bounding *** the classification branch,a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit *** proposed FCOS-LSC model has 38.65M parameters,38.72G floating point operations,and mean average precision of 63.0%and 75.2%for detecting green apples and green persimmons,*** summary,FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural ***,FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

关键词： connected designing weights

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STUDY ON THE ADAPTABILITY OF DYNAMIC LOAD IDENTIFICATION BASED ON LEAST SQUARES SUPPORT VECTOR MACHINE

STUDY ON THE ADAPTABILITY OF DYNAMIC LOAD IDENTIFICATION BAS...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Yang, Hongji Xia, Tianxiang He, Jincheng AVIC Nanjing Engineering Institute of Aircraft System Nanjing China Aviation Science and Technology Key Laboratory of Aviation Mechanical and Electrical System Nanjing China Nanjing Vocational-University of lndustry-Technology Nanjing China

Dynamic load identification is a crucial aspect in the field of structural dynamics. In recent years, the identification method based on the Least Squares Support Vector Machine (LSSVM) has garnered significant attention. The LSSVM method offers high accuracy, does not require extensive response information, and exhibits strong algorithmic stability, meaning the load identification results remain consistent across different training sessions with the same training and prediction sets, without cumulative error. However, research on identification using this method is still in its infancy, with many issues yet to be addressed. This paper investigates the adaptability of this method, exploring the effects of factors such as the position of sinusoidal excitation, amplitude, frequency, and load type on the identification results. The computational results indicate that the dynamic load identification method based on the least squares approach has strong adaptability and holds certain value for engineering applications. © The Institution of engineering & technology 2024.

关键词： Dynamic loads

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Plasmon coupling-driven enhanced high-order multiphoton excited fluorescent performance of metal-organic frameworks

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Nano Research 2024年第7期17卷 6502-6508页

作者： Tong Meng Bo Li Linlin Zeng Xianshun Sun Yupeng Tian Hongping Zhou Meng Zhou Dandan Li Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid MaterialsMinistry of EducationAnhui UniversityHefei 230601China School of Chemistry and Chemical Engineering Anhui UniversityHefei 230601China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 230026China

Accessing high-order multiphoton excited fluorescence(H-MPEF)materials is challenging yet and needs complicated synthesis *** this study,we successfully assembled plasmonic Au nanorods(Au NRs)with multiphoton responsive metalorganic frameworks(MOFs),resulting in a significant several-fold enhancement of *** extent of multiphoton enhancement was found to be strongly dependent on the degree of overlap between the multiphoton excitation wavelength of MOFs and the localized surface plasmon resonance absorbance of Au NRs,indicating the importance of plasmon-induced resonance energy ***,plasmon-induced hot electron transfer played a vital role in enhanced multiphoton activity as ***,the optimum H-MPEF enhancement occurs at the second near-infrared(NIR-II)region,which provides a promising platform for fluorescent *** findings provide a feasible and practical method to fabricate optimized H-MPEF materials for biological imaging using tissue-penetrating NIR-II light.

关键词： metal-organic framework(MOF) plasmon resonance multiphoton activity fluorescence imaging

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A Novel Pipelining Encryption Hardware system with High Throughput and High Integration for 5G

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China Communications 2022年第6期19卷 1-10页

作者： Yuntao Liu Zesheng Shen Shuo Fang Yun Wang College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information TechnologyHarbin 150001China Guangdong Greater Bay Area Institute of Integrated Circuit and System Guangzhou 510535China

This paper presents a ZUC-256 stream cipher algorithm hardware system in order to prevent the advanced security threats for 5 G wireless *** main innovation of the hardware system is that a six-stage pipeline scheme comprised of initialization and work stage is employed to enhance the solving speed of the critical logical ***,the pipeline scheme adopts a novel optimized hardware structure to fast complete the Mod(231-1)*** function of the hardware system has been validated experimentally in *** hardware system shows great *** with the same type system in recent literatures,the logic delay reduces by 47%with an additional hardware resources of only 4 multiplexers,the throughput rate reaches 5.26 Gbps and yields at least 45%better performance,the throughput rate per unit area increases 14.8%.The hardware system provides a faster and safer encryption module for the 5G wireless network.

关键词： encryption hardware system for 5G ZUC-256 stream cipher algorithm pipeline scheme throughput rate integration rate

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