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NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Computer Modeling in Engineering & Sciences 2024年第7期140卷 929-955页

作者： Tao Yi Xingshu Chen Mingdong Yang Qindong Li Yi Zhu School of Cyber Science and Engineering Sichuan UniversityChengdu610065China CyberScience Research institute Sichuan UniversityChengdu610065China Chengdu Fengwei technology Co. Ltd.Chengdu610041China

Due to the rapid evolution of advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective *** the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has ***,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract *** address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is ***,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in *** addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background ***,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack *** results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail *** state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

关键词： APT attacks spatial pyramid pooling NFHP(network flow holo-graphic picture) ResNet self-attention mechanism meta-learning

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Migratable Power System Transient Stability AssessmentMethod Based on Improved XGBoost

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Energy Engineering 2024年第7期121卷 1847-1863页

作者： Ying Qu Jinhao Wang Xueting Cheng Jie Hao Weiru Wang Zhewen Niu Yuxiang Wu Electric Power Science Research institute State Grid Shanxi Electric Power CompanyTaiyuan030000China Department of Electrical and Power Engineering Taiyuan University of TechnologyTaiyuan030024China

The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after ***,the accuracy of the assessment is limited by the quality of the data and has weak *** on this,this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting(XGBoost)***,the gradient detection method is employed to remove noise interference while maintaining the original time series *** this basis,a focal loss function is introduced to guide the training of theXGBoostmodel,enhancing the deep exploration of minority class samples to improve the accuracy of the model ***,to improve the generalization ability of the evaluation model,a transfer learning method based on model parameters and sample augmentation is *** simulation analysis on the IEEE 39-bus system demonstrates that the proposed method,compared to the traditional machine learning-based transient stability assessment approach,achieves an average improvement of 2.16%in evaluation ***,under scenarios involving changes in topology structure and operating conditions,the accuracy is enhanced by 3.65%and 3.11%,***,the model updating efficiency is enhanced by 14–15 times,indicating the model’s transferable and adaptive capabilities across multiple scenarios.

关键词： Transient stability assessment data-driven segmented focusing approximation portability

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HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

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Computers, Materials & Continua 2024年第9期80卷 4543-4560页

作者： Licheng Sun Heping Li Liang Wang College of Information Science and technology Beijing University of TechnologyBeijing100124China Chinese institute of Coal Science Beijing100013China State Key Laboratory for Intelligent Coal Mining and Strata Control Beijing100013China Engineering Research Center of Digital Community of Ministry of Education Beijing100124China

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine *** existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective ***,a new safety helmet-wearing detection method based on deep learning is ***,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural ***,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction *** block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small ***,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance *** experiments on open dataset validate the proposed *** reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art *** be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version *** generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

关键词： Object detection deep learning safety helmet wearing detection feature extraction attention mechanism

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Neural partially linear additive model

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Frontiers of Computer Science 2024年第6期18卷 186334-186334页

作者： ZHU Liangxuan LI Han ZHANG Xuelin WU Lingjuan CHEN Hong College of Informatics Huazhong Agricultural University Engineering Research Center of Intelligent technology for Agriculture (Ministry of Education) Huazhong Agricultural University Shenzhen institute of Nutrition and Health Huazhong Agricultural University Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics institute at Shenzhen Chinese Academy of Agricultural Sciences

Interpretability has drawn increasing attention in machine learning. Most works focus on post-hoc explanations rather than building a self-explaining model. So, we propose a Neural Partially Linear Additive Model (NPLAM), which automatically distinguishes insignificant, linear, and nonlinear features in neural networks. On the one hand, neural network construction fits data better than spline function under the same parameter amount; on the other hand, learnable gate design and sparsity regular-term maintain the ability of feature selection and structure discovery. We theoretically establish the generalization error bounds of the proposed method with Rademacher complexity. Experiments based on both simulations and real-world datasets verify its good performance and interpretability.

关键词： feature selection structure discovery partially linear additive model neural network

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Physics-informed deep learning for fringe pattern analysis

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Opto-Electronic Advances 2024年第1期7卷 4-15页

作者： Wei Yin Yuxuan Che Xinsheng Li Mingyu Li Yan Hu Shijie Feng Edmund Y.Lam Qian Chen Chao Zuo Smart Computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjing 210094China Smart Computational Imaging Research institute(SCIRI)of Nanjing University of Science and technology Nanjing 210019China Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense Nanjing 210094China Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong Kong SAR 999077China

Recently,deep learning has yielded transformative success across optics and photonics,especially in optical *** neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe ***,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning *** this end,we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (Le FTP) *** parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning *** by the initial phase from Le FTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end *** results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during *** proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

关键词： optical metrology deep learning physics-informed neural networks fringe analysis phase retrieval

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A Novel Method for Cross-Subject Human Activity Recognition with Wearable Sensors

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Journal of Sensor technology 2024年第2期14卷 17-34页

作者： Qi Zhang Feng Jiang Xun Wang Jinnan Duan Xiulai Wang Ningling Ma Yutao Zhang Faculty of Computing Harbin Institute of Technology Harbin China School of Future technology Nanjing University of Information Science & Technology Nanjing China School of Medicine and Health Harbin Institute of Technology Harbin China Nanjing Jinling Hospital Affiliated Hospital of Medical School Nanjing University Nanjing China

Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.

关键词： Human Activity Recognition Cross-Subject Adaptation Semi-Supervised Learning Wearable Sensors

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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting Data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of Big Data technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer *** data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth ***,data augmentation is crucial for this *** data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge *** this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth *** method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge *** validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation ***,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed *** results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data *** the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to *** the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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Spacelike Framed Curves with Lightlike Components and Singularities of Their Evolutes and Focal Surfaces in Minkowski 3-space

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Acta Mathematica Sinica,English Series 2024年第6期40卷 1521-1532页

作者： Peng Cheng LI Dong He PEI Xin ZHAO School of Mathematics and Statistics Northeast Normal UniversityChangchun 130024P.R.China Department of Mathematics Harbin Institute of TechnologyWeihai 264209P.R.China School of Mathematics and Statistics Qingdao UniversityQingdao 266071P.R.China

In this paper,we define the evolute and focal surface of a spacelike framed curve with lightlike components in Minkowski *** is a generalization of the previous results of regular spacelike curves,since singularities are allowed in the original spacelike curves studied by spacelike framed curves with lightlike ***,we show a new geometric invariant to characterise singularities of the focal ***,the classification theorem and recognition theorem for the singularities of the focal surface in generic are also given.

关键词： Evolutes focal surfaces spacelike framed curves singularities

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Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

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Energy Engineering 2024年第5期121卷 1329-1346页

作者： Lifeng Li Zaimin Yang Xiongping Yang Jiaming Li Qianyufan Zhou Ping Yang Energy Development Research institute China Southern Power GridGuangzhou510000China Corporate Headquarters China Southern Power GridGuangzhou510000China Guangdong Green Energy Key Laboratory South China University of TechnologyGuangzhou510000China

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy *** assessment of solar energy resources is crucial for the siting and design of photovoltaic power *** study proposes an integrated deep learning-based photovoltaic resource assessment *** learning and deep learning methods are fused for photovoltaic resource assessment for the first *** proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource *** proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and *** experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

关键词： Photovoltaic resource assessment deep learning ensemble learning random forest gated recurrent unit long short-term memory

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Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

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Computers, Materials & Continua 2024年第4期79卷 279-306页

作者： Zenglun Guan Murad S.Alfarzaeai Eryi Hu Taqiaden Alshmeri Wang Peng School of Mechatronic Engineering China University of Mining and TechnologyXuzhou221116China China National Coal Group Corp. Beijing100029China Information institute Ministry of Emergency Management of the ChinaBeijing100029China School of Mechanical Engineering Sana’a UniversitySana’a31220Yemen

In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and ***,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for *** such method exploits the difference in substancedensity,leading to excellent coal/gangue ***,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the *** training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the *** prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the *** classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,*** test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was *** useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained ***,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),*** obtaining a classification with hi

关键词： Coal coal gangue convolutional neural network CNN object classification volume estimation separation system

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