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检索条件"任意字段=7th International Conference on Advances in Pattern Recognition"

共 1113 条记录，以下是1-10 订阅

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2024 7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

2024 7th International Conference on Pattern Recognition and...

引用

7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

ISBN: (纸本)9798350350890

the proceedings contain 177 papers. the topics discussed include: short-term power load forecasting model based on EEMD-SE-ERCNN;dynamic low-rank adaptation based pruning algorithm for large language models;dual-input dual-output underwater image enhancement with branch interaction;object detection model of YOLOv8-CSD for UAV images;SE-ResNet: recognition of ECG anomalies based on convolutional neural network with fused attention;predicting accuracy for quantized neural networks: an attention-based approach with single-image;improved self-attention for Spodoptera frugiperda larval instar stages identification;an improved YOLOv8 algorithm for rotating object detection in unmanned aerial vehicle remote sensing images;and a comprehensive data augmentation method for modern cigarette packaging defect detection.

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advances in Few-Shot Action recognition: A Comprehensive Review 7

Advances in Few-Shot Action Recognition: A Comprehensive Rev...

引用

7th international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Ruan, Zanxi Wei, Yingmei Yuan, Yifei Li, Yu Guo, Yanming Xie, Yuxiang Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9798350385113;9798350385106

Action recognition holds a key position in the field of video understanding. While methods based on deep learning have made significant strides in big data backgrounds, their application in few-shot scenarios for action recognition still faces numerous challenges. this review delves deeply into over 20 methods in the few-shot action recognition (FSAR) area from recent years. Initially, following the research's developmental trajectory, we systematically categorize the current mainstream methods for few-shot action recognition into three perspectives: memory networks, metric learning, and data augmentation. In order to ensure a fair comparison, we examine the performance of several approaches on multiple datasets. Lastly, we offer insights into the future development directions of this field. this review is the first review in the FSAR domain, summarizing a wealth of research outcomes, techniques, and theories. It offers researchers a cohesive perspective and illuminates potential new avenues for innovative studies.

关键词： few-shot action recognition few-shot learning action recognition

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Design and Implementation of Anti-Peeping Screen Algorithm Based on Soft-NMS YOLOv7 7

Design and Implementation of Anti-Peeping Screen Algorithm B...

引用

7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Baining Chen, Hao Chen, Junlong Dai, Huawei School of Integrated Circuit Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (纸本)9798350350890

the occurrence of privacy breaches through screen peeping has highlighted the increasing importance of anti-peeping screen algorithms. In the traditional YOLOv7 algorithm, it may not accurately detect faces when individuals are mutually obscured. In order to improve the success rate of face detection, we propose a new anti-peeping screen algorithm, Soft-NMS-YOLOv7. Compared to the traditional NMS-YOLOv7, Soft-NMS-YOLOv7 prevents face leakage by specially handling the scores of adjacent detection boxes. therefore, Soft-NMS-YOLOv7 exhibits better detection performance and robustness, increasing the accuracy of face detection in anti-peeping screen algorithms from 90.05% to 94.71%. Additionally, we have developed a physical model of Soft-NMS-YOLOv7 based on Raspberry Pi 4B. © 2024 IEEE.

关键词： Face recognition

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Overview of Bionic Hand 7

Overview of Bionic Hand

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Yadav, Amol P. Patil, S.R. Aissmsioit E& Tc Dept. Bvcoew India

ISBN: (纸本)9798350304268

Several approaches for operating bionic limbs are outlined in this paper. While pattern recognition and gesture classification have shown promise, they also foreshadow a number of difficulties due to inaccurate findings from algorithms. However, every once in a while, new methods of gesture classification and pattern recognition emerge. this paper reviews the current state of the art in gesture classification algorithms and their related results. © 2023 IEEE.

关键词： ANN Fl score...... gesture recognition IF LDA pattern recognition RNN sEMG. DOF SVM transradial XGBoost

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An EEG Signal Analysis Method Based on Common Spatial pattern and Support Vector Machine Algorithm 7

An EEG Signal Analysis Method Based on Common Spatial Patter...

引用

7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Wang, Yuxin Liu, Bao Tang, Yuqi Qingdao China The 22nd Research Institute of China Electronics Technology Group Corporation Qingdao China

ISBN: (纸本)9798350350890

To solve the problem of EEG signal on feature extraction and low recognition rate, we analyze the characteristics of EEG signal and propose an EEG signal analysis method based on common spatial pattern (CSP) and support vector machine (SVM) algorithms. In this method, we first preprocess the EEG signal by wavelet packet transform (WPT) and extract the specific frequency band signal in the EEG signal. then the features of the EEG signal are extracted by constructing pair-wise CSP (PW-CSP) filters. Finally, the recognition of the EEG signal was realized by the support vector machine algorithm which optimizes parameter selection by particle swarm optimization (PSO), and the 2008 BCI competition 2A data set was used to verify the classification effect of the algorithm. the experimental results indicated that the classification accuracy of the method may reach up to 93.07%, and the average accuracy is higher than the results of the top two in the 2008 brain-computer interface (BCI) competition. the EEG signal features extracted by this method can reflect the signal characteristics of EEG well, and improve the accuracy of classification, which may assist in the development and application of BCI. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Rhythmic pattern recognition - A Review 7

Rhythmic Pattern Recognition - A Review

引用

7th IEEE Pune Section international conference, PuneCon 2024

作者： Garware, Chaitanya P. Shinde, Gitanjali R. Mahalle, Parikshit N. Velankar, Makarand R. Sable, Nilesh P. Vishwakarma Institute of Information Technology Pune India Cummins College of Engineering for Women Pune India

ISBN: (纸本)9798331527822

Rhythmic pattern recognition is crucial in music analysis and information retrieval, involving the identification and classification of rhythmic structures. this paper offers a detailed overview of advanced methods in rhythmic pattern recognition, including time-domain, frequency-domain, and symbolic representations. We examine various machine learning techniques, such as statistical models, neural networks, and deep learning. Additionally, we address the challenges and limitations of current methods and propose directions for future research. Understanding rhythmic patterns is vital for performing, composing and analysing music. © 2024 IEEE.

关键词： Adversarial machine learning

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Study on Identification of Partial Discharge Type of Oil-Paper Insulation Based on Deep Separable Convolutional Neural Network 7

Study on Identification of Partial Discharge Type of Oil-Pap...

引用

7th international conference on Automation Electronics and Electrical Engineering

作者： Yang, Xu Zhan, Hao Liu, Mengna Zhou, Zhengqin Zhou, Wen Wu, Yihua NARI Grp Corp State Grid Elect Power Res Inst Wuhan NARI Ltd Liabil Co State Grid Elect Power Res Inst Wuhan Peoples R China

ISBN: (纸本)9798350377040;9798350377033

the identification of partial discharge types is of great significance for the diagnosis and evaluation of transformer insulation. With the rapid advancement of deep learning in fields like machine vision and image processing, applying deep learning algorithms to partial discharge type recognition has become a new trend in power transformer fault diagnosis. Firstly, this article established a simulation test platform for partial discharge of oil paper insulation in the laboratory, conducted long-term partial discharge tests using the constant voltage method, and obtained ultrasonic signals under typical discharge defect models. then a two-dimensional time-frequency spectrum was constructed, and a depthwise separable convolutional neural network model was used to classify the spectrum, thereby achieving identification of partial discharge types. the results indicate that there are differences in the time-frequency spectra of ultrasound signals among different defect models;the partial discharge identification model used in this article can ensure high accuracy while effectively reducing the size of the model and improving training efficiency;Compared with traditional machine learning models, the model used in this paper has a higher recognition accuracy, reflecting the superiority of convolutional neural networks in image recognition.

关键词： power transformer partial discharge convolutional neural network discharge pattern recognition

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An Improved YOLOv7 Based Concrete Defect Detection Method 7

An Improved YOLOv7 Based Concrete Defect Detection Method

引用

7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Wang, Zhiyuan Chen, Zhili Abba, Adamu Abubakar School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350350890

Concrete is widely used in numerous buildings, and timely detection of concrete defects is significant. Currently, the detection of concrete defects mainly relies on manual visual inspection methods. However, manual inspection has many work efficiency, accuracy, and safety issues. In recent years, with the rapid development of computer vision technology, many studies have begun to attempt concrete defect detection methods based on deep learning networks. this paper proposes a concrete defect detection method based on an improved YOLOv7 network, which enhances the detection performance of minor target defects such as cracks by integrating the ACmix attention mechanism and separation-merge operations. To increase the difficulty of defect detection, this paper constructs a concrete defect dataset containing various defect in various environments. the effectiveness of the improved method is verified through ablation experiments, and comparative experiments with Faster R-CNN, YOLOv4, SSD, and other target detection networks which demonstrated the advancement of the enhanced network. © 2024 IEEE.

关键词： ACmix concrete defects object detection various environments YOLOv7

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Human Activity recognition with Dual-Stream Residual Network based on Wearable Devices 7

Human Activity Recognition with Dual-Stream Residual Network...

引用

7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Qingquan Hu, Dinghan Jiang, Tiejia Gao, Feng Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang310018 China Machine Learning and I-health International Cooperation Base of Zhejiang Province Zhejiang310018 China The Children's Hospital Zhejiang University School of Medicine The Department of Neurology Zhejiang310003 China Editorial National Clinical Research Center for Child Health Zhejiang310003 China

ISBN: (纸本)9798350350890

Human activity recognition (HAR) is utilized to monitor human postures and actions in various fields such as human-computer interaction, motion detection and analysis, and medical rehabilitation. Currently, there is a growing interest in human activity recognition using wearable devices. Traditional convolutional neural networks (CNNs) typically use one-dimensional convolution for time-series signals, which makes it difficult to fully exploit high-dimensional features. this paper proposes a dual-stream residual network model for human activity recognition based on Gramian Angular Field (GAF). GAF converts a one-dimensional time-series signal into a two-dimensional image, enabling better observation of weak amplitude changes in the signal and providing better interpretability than traditional conversion algorithms. the experimental com-parison between the proposed model and several mainstream depth models on a local dataset shows that the proposed model outperforms all other models in all evaluation indicators. © 2024 IEEE.

关键词： Convolutional neural networks

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Emerging Cutting-Edge Developments in Intelligent Traffic and Transportation Systems - Proceedings of the 7th international conference, ICITT 2023 - incorporating the 7th international conference on Communication and Network Technology, ICCNT 2023

Emerging Cutting-Edge Developments in Intelligent Traffic an...

引用

7th international conference on Intelligent Traffic and Transportation, ICITT 2023

ISBN: (纸本)9781643685045

the proceedings contain 27 papers. the topics discussed include: analysis and characterization of vehicular traffic in a low emission zone;an augmented reality-based proving ground vehicle-in-the-loop test platform;next place prediction model: a literature review;driver model using fuzzy logic for virtual validation;risky driving behaviors among motorcyclists in Ecuador: a study of associated factors;high-speed localization estimation method using lighting recognition in tunnels;a machine learning approach for efficient assignment of batch delivery tasks;real-time idling vehicles detection using combined audio-visual deep learning;assessment of methods to estimate the level of service on third-class roads;urban traffic flow predictions with impacts of weather and holidays;and data formats for communication between freight tram operator and forwarder.

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