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Research on pattern recognition and Retrieval of "Reliance" Based on Deep Learning 3

Research on Pattern Recognition and Retrieval of "Reliance" ...

引用

3rd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Zhang, Chunxia Zhang, Guoyun Krirk University Bangkok10220 Thailand

ISBN: (纸本)9781510663473

With the rapid development of computer vision technology in the field of visual fashion, more and more people pay attention to the research on the "reliance" pattern of drama clothing. At present, in the field of clothing image display, research mainly focuses on clothing image recognition, key point detection, clothing recommendation, retrieval and matching. These studies can provide decision support for the design, production, display, sales and other links of drama costumes and bring a new display experience. However, in the realistic application scenarios of clothing "reliance" images, we still face challenges brought by changes in clothing style, materials, cutting, pattern composition and combination methods, which make the effects in recognition, positioning, recommendation and other applications continuously improved through experiments. The method based on depth learning in this paper focuses on clothing "reliance" pattern recognition, key point detection, clothing retrieval and other tasks. © 2023 SPIE.

关键词： Image recognition

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3D Human Pose Estimation with Bone Symmetry and Motion Locality 3

3D Human Pose Estimation with Bone Symmetry and Motion Local...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Xinyu Hua, Chunsheng Liaoning University Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning Shenyang110036 China

ISBN: (纸本)9798350369151

The bone symmetry and motion locality are two important priors for 3D human pose estimation. For bone symmetry, symmetric bones typically have equal length and higher motion correlation. In terms of motion locality, the motion of the human body conforms to its structure and is temporally continuous. These two priors align with human intuition, but are challenging for Transformer to directly abstract them from data. We propose novel modules, Bone Symmetry Temporal Transformer (BSTT) and Spatial-Temporal Locality Fusion (STLF), to utilize these two priors, and design a new Transformer-based BSMLFormer model. Specifically, BSTT considers the temporal sequences of two symmetric bones as a single new sequence and calculates its global relationships by self-attention mechanism. STLF performs local computations by GCN and convolutions to capture the local dependencies in adjacent joints and adjacent parts. The local-global alternating structure of BSMLFormer allows for consideration of the local information during global calculations. More remarkably, BSMLFormer achieves the state-of-the-art performance with 39.0mm MPJPE on Human3.6M dataset. © 2024 IEEE.

关键词： Motion estimation

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Self-supervised Image Classification Using Convolutional Neural Network 3

Self-supervised Image Classification Using Convolutional Neu...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Kong, Shiyun Shanghai United International School Shanghai China

ISBN: (纸本)9781665472784

Image classification has been a trendy research topic in the field of pattern recognition and computer vision, which extracts different features of images and predict the category of images. Thanks to the development of deep learning, powerful convolutional neural networks can be used in the field of image recognition. However, existing deep learning-based image recognition research mostly follows the framework of supervised learning, and model learning relies on a large number of accurate labels. Providing a large amount of label data will undoubtedly require laborious human effort and expensive costs. Therefore, image recognition based on unsupervised learning (without using any image category labels to achieve classification) has become a spotlight for research. In this paper, explorations on the image classification by self-supervised framework SimCLR on image classification successfully clusters a large number of images into an optimum amount categories. Qualitative results have show SimCLR is particularly effective in recognizing the colors of images;both qualitative and quantitative results shows SimCLR is great at identifying simple contours. However, when the colors are similar, and contour lines are complex, SimCLR does not obtain satisficing results. The accuracy on classifying Mnist dataset is 32%. © 2023 IEEE.

关键词： Image recognition

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network 3

Research on Intelligent Auxiliary Review of Power Grid 3D De...

引用

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

作者： Fan, Dongmei Yan, Wendou Liang, Yi Cheng, Zhi Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou102200 China

ISBN: (纸本)9798350319125

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid. © 2023 IEEE.

关键词： 3D model based on BP neural network computer Grid design power system

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User recognition based on Gait pattern via Smartwatch Accelerometer in Unrestricted Environment 3

User Recognition based on Gait Pattern via Smartwatch Accele...

引用

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

作者： Khan, Sheharyar Shah, Syed M Adnan Arsalan, Aamir Ahmed, Wakeel University of Engineering and Technology Taxila Department of Computer Science Taxila Pakistan Fatima Jinnah Women University Rawalpindi Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350332391

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature. © 2023 IEEE.

关键词： Machine learning

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Factors that Influence Reliability of Biometric Identification for On-Scene Smart Healthcare Applications 3

Factors that Influence Reliability of Biometric Identificati...

引用

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

作者： Hong, Guanyue Fong, A. C. M. Western Michigan Univ Dept Comp Sci Kalamazoo MI 49008 USA

ISBN: (纸本)9798350372977;9798350372984

In a medical emergency situation, such as a traffic accident, effective and efficient identification of patients can improve the chances of survival for critically injured patients. In some cases, the patients may be unconscious when paramedics arrive at the scene. Timely and successful identification of patient in varying states of injury and/or consciousness can provide the attending paramedics with vital information about the patients' medical records, thereby allowing them to provide the patients with the most appropriate life-saving on-scene medical interventions. Major improvements in miniaturized biometric hardware, low-power telecommunication devices, efficient biometric identification platforms, and energy-efficient artificial intelligent-enhanced data processing techniques all contribute towards the next generation of smart on-scene patient identification, even for paramedics attending at emergency sites in remote locations. This paper focuses specifically on fingerprint acquisition and processing, as fingerprints are among the most popular biometrics used for identification. In addition to fingerprint identification for patients, it is also useful for quick and effective identification of medical personnel for access to resources like ambulances. Specifically, the purpose of this research is to investigate factors that can enhance the reliability of such identification systems in practical scenarios involving some form of telecommunications backbone, which are typically much more unforgiving than comparable laboratory settings.

关键词： Biometric identification fingerprint identification pattern recognition mission-critical applications smart healthcare smart cities prognostics telehealth telecommunications in harsh operating environments

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

引用

3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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PM2.5-O3 Compound Pollution pattern recognition Based on Hybrid Model of tensor Decomposition and Spectral Clustering 3

PM2.5-O3 Compound Pollution Pattern Recognition Based on Hyb...

引用

3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Li, Wanhua Hu, Dongmei Guo, Yafei Dong, Jiaqi The North China Electric Power University MOE Key Laboratory of Resources and Environmental System Optimization College of Environmental Science and Engineering Beijing10044 China Beijing Jiaotong University Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation Ministry of Education School of Environment Beijing10044 China

ISBN: (纸本)9798350331387

para>Clustering intra-urban areas with the same pollution characteristics can help to accurately manage intra-urban air pollution. In this study, a new classification model for PM2.5-O3 composite pollution features is established based on tensor decomposition and spectral clustering methods. The tensor decomposition was utilized to extract pollution features from massive air pollutant concentration data, and the pollution concentration data were decomposed into day-by-day, intra-day, and spatial modes, which provided a low-dimensional space for the subsequent spectral clustering and fine clustering classification process of the urban spatial-scale PM2.5-O3 composite pollution conditions. The model is explored by taking the annual PM2.5 and O3 pollution conditions in Beijing in 2017 and 2020 as an example, discussing the differences in geographic locations and pollution characteristics between the different models, and explaining the changes in the city's pollution characteristics from 2017 to 2020. Through this new hybrid method, scientific support can be provided for urban refinement and precise management. © 2023 IEEE.

关键词： pattern recognition

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The technique of parsing vehicle wiring harness tooling diagram based on pattern recognition 3

The technique of parsing vehicle wiring harness tooling diag...

引用

3rd international conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Guo, Zhibo Jiang, Wei Song, Yanhai Sun, Chenglin Wang, Nan Qiao, Xiaoli Changchun University of Science and Technology Changchun China Changchun CRRC Railway Vehicles Co. Ltd. Changchun China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

This paper analyzes the main elements of vehicle wire harness diagrams and summarizes the rules that a correct wire harness diagram should adhere to. Building upon this foundation, with the aid of Teigha, successful extraction of entities within vehicle wire harness diagrams has been achieved. Node recognition Algorithm and Wire harness recognition algorithm have been designed, which can correctly analyze elements in the harness diagram and extract data from them. A comparative experiment was conducted with the traditional electronic wiring board, and the experimental results showed that the intelligent electronic wiring board using the algorithm shortened the average path resolution time from 4.3 hours to 7.6 seconds, and no errors occurred during the processes. © 2024 SPIE.

关键词： Vehicles

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Novel Power Load pattern recognition Algorithm Based on Unbalanced Data Clustering 3

Novel Power Load Pattern Recognition Algorithm Based on Unba...

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3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Zhu, Dongyuan Sun, Yifei Cui, Jia Hu, Zhen Huang, Jingbo Zhang, Jiahui Shenyang Institute of Engineering School of Electric Power Liaoning Shenyang110136 China State Grid Yingkou Electric Power Supply Liaoning Yingkou115000 China Shenyang University of Technology School of Electrical Engineering Liaoning Shenyang China

ISBN: (纸本)9798350393224

An improved fuzzy C-means clustering algorithm is proposed for the imbalance between different date types in the power load dataset and the uniformity effect of the cluster size faced by the fuzzy C-means clustering algorithm when dealing with the unbalanced dataset. Firstly, in the improved algorithm, the sum of the membership values of all samples belonging to a certain class is taken as the volume of the class, and the objective function of the traditional algorithm is normalized by its inverse ratio to overcome the problem of uniformity. Secondly, through the Lagrange multiplier method, a new membership matrix update formula is obtained. Finally, Dynamic Time Warping (DTV) was selected as the distance measure in order to solve the Euclidean distance cannot measure the shape similarity of time series. Experimental results show that the proposed algorithm can effectively identify the load pattern of holidays from the historical load data set. © 2023 IEEE.

关键词： clustering algorithms imbalance data load pattern recognition

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