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Research on Traffic Classification Algorithm of Power Communication Network based on Cluster Analysis 3

Research on Traffic Classification Algorithm of Power Commun...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Wang, Lijun Lu, Yi Zhu, Guopeng Wang, Qi Ma, Kai Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9798350319125

Power communication network is an important infrastructure in power system, and its security and stability are very important to ensure the normal operation of power system. With the development and application of power system, the traffic of power communication network is increasing, and the classification of traffic is of great significance for network management and optimization. In this paper, a classification algorithm based on clustering analysis is proposed to solve the problem of traffic classification in power communication network. The algorithm can effectively classify the traffic data and provide powerful support for the management and optimization of power communication network © 2023 IEEE.

关键词： clustering algorithms pattern recognition theory power communication networks Traffic classification

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A Graph-based Multi-view Clustering Approach for Building Group patterns recognition 3

A Graph-based Multi-view Clustering Approach for Building Gr...

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3rd international conference on Internet of Things and Smart City, IoTSC 2023

作者： Yang, Ruolin Yang, Lin Zuo, Zejun Wei, Jing National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9781510666375

Building pattern informs urban spatial structure understanding and modeling. However, previous studies showed limitations on the identification of building groups which has complex spatial distribution. Specifically, they usually use original spatial or non-spatial characteristics but omit certain complementary among multi-faceted features of buildings. In this paper, we give a novel method based on a multi-view clustering framework, which establishes a centroid distance view and a building attribute view to recognize building patterns accurately. The two views are based on both the spatial structure and non-spatial attributes. The similarity graphs on these views, which are obtained by the Gaussian kernel function, are executed graph diffusion and fusion process to obtain a unified graph. Then, the clustering results are obtained via the Normalized cut algorithm. We conduct experiments to recognize building patterns by four real-world community building footprint datasets of two cities in China: Wuhan and Chengdu. The experimental results show that our proposed method identifies building groups with grid, grid-like and unstructured patterns more effectively. Our method performs better in seven evaluation indexes compared with the baseline model. © 2023 SPIE.

关键词： pattern recognition

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric pattern recognition System Using Multi Resolution Approach 3

FPGA Implementation of Textural Feature Extractor and Distan...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Hariprasath, S. Santhi, M. Prabakar, T.N. Koushick, V. Aravind, P. Gunasekaran, T. Saranathan College of Engineering Department of Ece Trichy India Sri Sairam College of Engineering Department of Ece Bangalore Anekal India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Mattu University Department of Electrical and Computer Engineering Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798350322842

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Accelerating Semantic Segmentation Labeling of Time Sequence Data via Assistive Label Generation with Conditional GANs 3

Accelerating Semantic Segmentation Labeling of Time Sequence...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Akagic, Amila Buza, Emir Kecman, Stefani Saric, Rijad Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia SIA 2 Res Ave Bundoora Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

This paper presents a robust exploration of the capabilities of conditional Generative Adversarial Networks (GANs) in harnessing labeled data to produce high-quality labels for unlabeled samples. By leveraging conditional information, our approach guides the network to generate contextually relevant labels for specific time series data, accelerating the labeling process. A comprehensive evaluation of our model's performance, incorporating diverse metrics, visual representations, and histograms, illuminates the effectiveness of conditional GANs for the Assistive Label Generation (ALG) of time series Arabidopsis thaliana images. The Structural Similarity Index (SSIM) highlights an average similarity of 98.89% between the generated and manually labeled images. This innovative methodology holds the promise of significantly reducing labeling efforts.

关键词： Plant Phenotyping Generative Adversarial Networks Image Processing Computer Vision Deep Learning pattern recognition

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Using UDP to Realize Flexible and Portable Human Activity recognition 3

Using UDP to Realize Flexible and Portable Human Activity Re...

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3rd international conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Zhou, Jian Huang, Binke Yan, Sen Xi'An Jiaotong University School of Information and Communications Engineering Xi'an China

ISBN: (纸本)9798350357707

With the development of smart home, the research of human activity recognition (HAR) in home scene has been attracting more and more attentions in both the academic and industrial fields. Benefited from the widespread deployment of Wi-Fi routers in every household, research on Wi-Fi-based human activity recognition is cost-effective and feasible. Human activity recognition can be realized by obtaining the changes in the Wi-Fi channel state caused by human activities in the environment, and then using machine learning models for corresponding training. In this paper, a low-cost HAR system based on ESP32 is proposed, which can collect and transmit channel state information (CSI) data flexibly and efficiently by using a user datagram protocol (UDP) communication transceiver server. The proposed system avoids the use of serial port communication or SD card to collect CSI data, which reduces the difficulty of CSI data collection. An actual verification of the method is built and the performances of several common training models are compared. Our initial results show that the convolutional neural network (CNN) provide the best performances, i.e., reaching an accuracy rate of 98.6%. © 2023 IEEE.

关键词： pattern recognition

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2023 3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

2023 3rd International Conference on Communication, Computin...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

ISBN: (纸本)9798350332391

The proceedings contain 31 papers. The topics discussed include: efficient virtual network embedding with hierarchical and cooperative multi-agent reinforcement learning;testing neurofeedback system for studying cognitive tests;a survey on human factors in cyberspace: a new dimension of privacy threats;user recognition based on gait pattern via smartwatch accelerometer in unrestricted environment;performing walk-through energy audits in smart homes;exploration of machine learning algorithms for development of intelligent intrusion detection systems;chaos-based secured modified strong recovery conditions for least support orthogonal matching pursuit in the noisy case;machine learning technique to monitor heartbeat using amalgamated data of multi-sensor stream;a metadata approach to classify domain-specific documents for event-based surveillance systems;a novel font color and compression text steganography technique;hybrid deep learning approach for nonfunctional software requirements classifications;and consensus MPC-based hierarchical control of islanded AC microgrid.

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Third international conference on Image Processing, Object Detection, and Tracking, IPODT 2024

Third International Conference on Image Processing, Object D...

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2024 3rd international conference on Image Processing, Object Detection, and Tracking, IPODT 2024

ISBN: (纸本)9781510685512

The proceedings contain 35 papers. The topics discussed include: design of a book misplacement detection system based on image recognition;automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt;deep intrinsic image popularity assessment by learning to rank;a late fusion framework for multi-vehicle collaborative perception;subpixel precision BGA chip localization method based on Lagrange interpolation;signal-to-noise ratio analysis and experimental verification of imaging laser detection system;comparison of cell image segmentation results based on U-Net and transformer;and deep learning based algorithm for classifying pedestrian behavior at crosswalks.

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Gait recognition Algorithm GCGait based on Skeleton 3

Gait Recognition Algorithm GCGait based on Skeleton

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3rd IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Ma, Ru Zhang, Jikai Jia, Mengyu Lv, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Inner Mongolia University of Technology School of Information Engineering Hohhot China

ISBN: (纸本)9781665490788

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method. © 2023 IEEE.

关键词： Complex networks

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Bangla License Plate Detection and recognition Approach Based on Computer Vision for Authentic Vehicle Identification 3rd

Bangla License Plate Detection and Recognition Approach Base...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dhar, Rajib Kumar Khanom, Fahmida Uddin, Khandaker Mohammad Mohi Imam, Md. Hasan Mostafiz, Rafid Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Mathematics American International University—Bangladesh 408/1 Kuratoli Khilkhet Dhaka1229 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh

ISBN: (纸本)9789819739363

Vehicle license plates (LPs) detection and recognition is a fundamental technique in computer vision that plays a crucial role to extract, localize, and authenticate the information of a vehicle. This paper presents an efficient method for Bangla LPs recognition and vehicle authentication using edge analysis and pattern matching algorithm in real-time nature. This method performs four modules as: preprocessing, license plate identification, character recognition, vehicle authentication. A multi-step preprocessing technique has been applied to extract and understand the potential information in case of any uneven images. To identify edge connectivity and uniformity, Canny edge detector provides more promising in contour analysis and character localization. The segmented characters are converted into texts and verified using the pattern matching algorithm by a predefined database. For this research, a database consisting of 3000 automobile license plate photos captured under different climatic circumstances has been compiled. Additionally, we have curated two additional databases consisting of templates for recognition and registered automobiles for the purpose of authentication. The proposed model can achieve accuracy of 96% in LPs detection, 99% in character recognition, and 100% in vehicle authentication by extracted texts from LPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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3rd CAAI international conference on Artificial Intelligence, CICAI 2023

3rd CAAI International Conference on Artificial Intelligence...

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3rd CAAI international conference on Artificial Intelligence, CICAI 2023

ISBN: (纸本)9789819991181

The proceedings contain 100 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Equivariant Indoor Illumination Map Estimation from a Single Image;Weakly-Supervised Grounding for VQA with Dual Visual-Linguistic Interaction;STU3: Multi-organ CT Medical Image Segmentation Model Based on Transformer and UNet;integrating Human Parsing and Pose Network for Human Action recognition;lightweight Rolling Shutter Image Restoration Network Based on Undistorted Flow;an Efficient Graph Transformer Network for Video-Based Human Mesh Reconstruction;multi-scale Transformer with Decoder for Image Quality Assessment;low-Light Image Enhancement via Unsupervised Learning;MARS: An Instance-Aware, Modular and Realistic Simulator for Autonomous Driving;GLCANet: Context Attention for Infrared Small Target Detection;fast Point Cloud Registration for Urban Scenes via Pillar-Point Representation;PMPI: Patch-Based Multiplane Images for Real-Time Rendering of Neural Radiance Fields;EFPNet: Effective Fusion Pyramid Network for Tiny Person Detection in UAV Images;end-to-End Object-Level Contrastive Pretraining for Detection via Semantic-Aware Localization;pointerNet with Local and Global Contexts for Natural Language Moment Localization;self-supervised Meta Auxiliary Learning for Actor and Action Video Segmentation from Natural Language;rsMmFormer: Multimodal Transformer Using Multiscale Self-attention for Remote Sensing Image Classification;fashion Label Relation Networks for Attribute recognition;a Modified Fuzzy Markov Random Field Incorporating Multiple Features for Liver Tumor Segmentation;concealed Object Segmentation with Hierarchical Coherence Modeling;weakly Supervised Optical Remote Sensing Salient Object Detection Based on Adaptive Discriminative Region Suppression;SPCTNet: A Series-Parallel CNN and Transformer Network for 3D Medical Image Segmentation;LANet: A Single Stage Lane Detector with Lightweight Attention;Visible and NIR Image Fusion

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