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Anomaly Detection Algorithm of Power System Based on graph structure and Anomaly Attention

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Computers, Materials & Continua 2024年第4期79卷 493-507页

作者： Yifan Gao Jieming Zhang Zhanchen Chen Xianchao Chen Zhaoqing Power Supply Bureau of Guangdong Power Grid Co. Ltd.Zhaoqing526060China

In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.

关键词： Anomaly detection transformer graph structure

Clustering Reference Images Based on Covisibility for Visual Localization

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Computers, Materials & Continua 2023年第5期75卷 2705-2725页

作者： Sangyun Lee Junekoo Kang Hyunki Hong School of Integrative Engineering Chung-Ang UniversityHeukseok-ro 84Dongjak-kuSeoul06973Korea School of Software Chung-Ang UniversityHeukseok-ro 84Dongjak-kuSeoul06973Korea

In feature-based visual localization for small-scale scenes,local descriptors are used to estimate the camera pose of a query image.For large and ambiguous environments,learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced.However,since global descriptors are generated using visual features,reference images with some of these features may be erroneously selected.In order to address this limitation,this paper proposes two clustering methods based on how often features appear as well as their covisibility.For both approaches,the scene is represented by voxels whose size and number are computed according to the size of the scene and the number of available 3Dpoints.In the first approach,a voxel-based histogram representing highly reoccurring scene regions is generated from reference images.A meanshift is then employed to group the most highly reoccurring voxels into place clusters based on their spatial proximity.In the second approach,a graph representing the covisibility-based relationship of voxels is built.Local matching is performed within the reference image clusters,and a perspective-n-point is employed to estimate the camera pose.The experimental results showed that camera pose estimation using the proposed approaches was more accurate than that of previous methods.

关键词： Visual localization deep learning voxel representation clustering covisibility meanshift graph structure

New approaches to quantify progressive damage and associated dynamic rock mass blockiness

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第2期15卷 285-295页

作者： Ladan Karimi Sharif Davide Elmo Doug Stead NBK Institute of Mining Engineering The University of British ColumbiaVancouverCanada Department of Earth Science Simon Fraser UniversityBurnabyCanada

In the past decade, numerical modelling has been increasingly used for simulating the mechanical behaviour of naturally fractured rock masses. In this paper, we introduce new algorithms for spatial and temporal analyses of newly generated fractures and blocks using an integrated discrete fracture network (DFN)-finite-discrete element method (FDEM) (DFN-FDEM) modelling approach. A fracture line calculator and analysis technique (i.e. discrete element method (DEM) fracture analysis, DEMFA) calculates the geometrical aspects of induced fractures using a dilation criterion. The resultant two-dimensional (2D) blocks are then identified and characterised using a graph structure. Block tracking trees allow track of newly generated blocks across timesteps and to analyse progressive breakage of these blocks into smaller blocks. Fracture statistics (number and total length of initial and induced fractures) are then related to the block forming processes to investigate damage evolution. The combination of various proposed methodologies together across various stages of modelling processes provides new insights to investigate the dependency of structure's resistance on the initial fracture configuration.

关键词： Numerical modelling Spatial analysis Temporal analysis Discrete fracture network(DFN) Finite-discrete element method(FDEM)modelling Block calculations graph structure

Smoother manifold for graph meta-learning

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High Technology Letters 2022年第1期28卷 48-55页

作者：赵文仓 WANG Chunxin XU Changkai College of Automation and Electronic Engineering Qingdao University of Science and TechnologyQingdao 266061P.R.China

Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation.

关键词： meta-learning smoother manifold similarity parameter graph structure

Advancing graph Convolution Network with Revised Laplacian Matrix

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Chinese Journal of Electronics 2020年第6期29卷 1134-1140页

作者： WANG Jiahui GUO Yi WANG Zhihong TANG Qifeng WEN Xinxiu East China University of Science and Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies

graph convolution networks are extremely efficient on the graph-structure data,which both consider the graph and feature information.Most existing models mainly focus on redefining the complicated network structure,while ignoring the negative impact of lowquality input data during the aggregation process.This paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing stage.The comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity,which gains relatively higher accuracy and stability.

关键词： graph convolution network Clustering Label propagation Laplacian matrix graph structure Fraud detection

Robust interactive image segmentation via graph-based manifold ranking

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Computational Visual Media 2015年第3期1卷 183-195页

作者： Hong Li Wen Wu Enhua Wu Department of Computer and Information Science University of Macao Chinese Academy of Sciences

Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.

关键词： interactive image segmentation graph structure graph edge weights manifold ranking relevance inference