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Enhancing Weld Quality Assessment Using a Comprehensive Study on Combined Thermal and Residual Stress Through Lenet pattern recognition 15

Enhancing Weld Quality Assessment Using a Comprehensive Stud...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Deepana, G. Raja, V.L. Priya, A. Parvathi Priya, J. Geetha Rajendiran, M. Babu, M. Dinesh S.A. Engineering College Department of Civil Engineering Thiruverkaadu Tamilnadu Chennai India Loyola Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India R. M. K. Engineering College Department of Chemistry Tamilnadu Kavaraipettai India R.M.D. Engineering College Department of Cse Tamilnadu Kavaraipettai India Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Raja Lakshmi Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

This study aims to conduct a thorough examination of improving the evaluation of weld quality. This will be achieved by utilizing a mix of thermal and residual stress measurement, along with a pattern recognition approach employing the LeNet neural network architecture. The primary aim of this study is to provide a reliable and precise approach for assessing the quality of welds. In order to enhance the evaluation of weld quality, we propose the utilization of the LeNet convolutional neural network, which is a deep learning framework specifically designed for problems involving image identification. Photographs of weld cross-sections are analyzed using the network, which lets visual patterns of both thermal and residual stress distributions be captured. The findings of our research indicate a notable enhancement in the precision of weld quality evaluation when compared to conventional approaches. We use thermal and residual stress data along with deep learning techniques to make our method much better at finding and classifying defects. The proposed methodology exhibits significant potential for enhancing the overall safety and performance of welded structures across many sectors. © 2024 IEEE.

关键词： Convolutional neural networks

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery 4

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Deng, Xinjie Khan, Burhan Lim, Chee Peng Liao, Ming-Yu Institute for Intelligent Systems Research and Innovation Deakin University Chongqing Jianzhu College Chongqing China Deakin University Institute for Intelligent Systems Research and Innovation Waurn PondsVIC Australia National Kaohsiung University of Science and Technology Department of International Business Taiwan

ISBN: (纸本)9798350324303

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. This paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification. © 2023 IEEE.

关键词： Antennas

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international conference on Sustainable computing and Smart Systems, ICSCSS 2023 - Proceedings

International Conference on Sustainable Computing and Smart ...

引用

2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

ISBN: (纸本)9798350333602

The proceedings contain 281 papers. The topics discussed include: applications of machine learning in cloud forensics;revolutionizing malaria diagnosis: a deep learning approach;age and gender prediction using gated residual attention network;artificial intelligence based scarecrow prevention from crop destruction;augmented reality application for sustainable development – a grounded theory approach;pattern recognition algorithm to detect suspicious activities;market analysis and forecasting using trending data analytical tool;an intelligent voice-recognition wheelchair system for disabled persons;temporal structural probability network for weather prediction;exploring bio signals for smart systems: an investigation into the acquisition and processing techniques;population density estimation using single shot detection algorithm;pituitary gland size estimation and lesion detection using segmentation-based color thresholding technique;design and development of smart house rental management system;and a novel approach for jewelry and fashion in mobile application development using augmented reality.

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Mean AR-Local Binary pattern For Facial Expressions Classification Based on KNN Classifier 10

Mean AR-Local Binary Pattern For Facial Expressions Classifi...

引用

17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Srilatha, Narubonia Reddy, Lokeswara Computer Science and Engineering Jntua Ananthapur India Computer Science and Engineering Kadapa India

ISBN: (纸本)9789380544472

According to its significance, facial expression recognition (FER) has reached an incredibly prominent position in the field of image processing, beginning in the 1990s and continuing to the present day. Face recognition, feature extraction, and face categorization are the three common steps in FER. In contrast to the various FER systems described for recognizing just part of face expressions, the system presented in this study can identify all eight fundamental facial emotions (neutral, pleased, angry, contempt, surprise, sad, fear, and disgust). We employ the Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK+) datasets to verify our results. The presented methods LBP patterns in this work namely, Asymmetric Region LBP(AR-LBP), Mean Asymmetric Region LBP (Mean AR-LBP), We used GLCM feature extractor technique for feature selection. For the given input image, The GLCM calculates different features factors named Contrast, Correlation, and Homogeneity, Entropy, Mean, variance, and dissimilarity which were results to a feature vector. The findings of the given approach, which employed K-Nearest Neighbor (KNN) to categories facial emotions, are then contrasted with those of the standard LBP pattern. The recognition rate utilizing the provided technique is 96% for the CK + data set and 97% for the JAFFE data set, as shown by the testing results. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Emotion recognition

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Research on Dynamic Fuzzy Neural Network Algorithm for LiFePO4 Battery Matching 1

Research on Dynamic Fuzzy Neural Network Algorithm for LiFeP...

引用

1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Liu, Xuepeng Chen, Jianping Lu, Guanming School of mechanical engineering Foshan Polytechnic Foshan China

ISBN: (纸本)9781643685779

The feature vector based on dynamic characteristic matching method is the charging and discharging curve of the battery, and this method is used to classify the battery. The process of classifying batteries is a pattern recognition process, and one of the most important steps is the design of the classifier. After comparing the advantages and disadvantages of various classifier methods, the generalized dynamic fuzzy neural network algorithm GDFNN was chosen to classify batteries. The results indicate that when the classification number is 5, The value of Vxb is the 8.3289e-5. The clustering effect is best when the number of clusters is 5. © 2025 The Authors.

关键词： charging classifier discharging Generalized Dynamic Fuzzy Neural Network Algorithm (GDFNN) LiFePO4 battery pattern recognition

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The Weakly Supervised Network of Hierarchical Attention Mechanism for Fine-Grained Classification 1

引用

20th international conference on Intelligent computing (ICIC)

作者： Long, Qian Wang, Gaihua Qu, Hongwei Yao, Jingxuan Zhu, Bolun Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Peoples R China

ISBN: (数字)9789819756001

ISBN: (纸本)9789819755998;9789819756001

Fine-grained classification is challenging task to discriminate subtle and local differences from sub-categories. Many works improve the accuracy by relying heavily upon the use of the object or part annotations of images whose label are costly. In the paper, a weakly supervised network is proposed for fine-grained image classification without using expensive ***, it learns object detector by hierarchical attention mechanism automatically and localizes the objects or its parts to extract salient feature. Then, based on the theory of prototypical networks, it learns a metric loss function by computing distances to prototype representations of each class which is unsupervised clustering methods. Both are jointed to remove useless information or noise patches and retain discriminative features. Finally, we apply the proposed method to complete the classification of disaster-scene images. Compared with other methods, experimental results demonstrate our method is generalized and robust to different datasets.

关键词： Fine-grained Classification Hierarchical Attention Mechanism Salient Feature Metric Loss Function Disaster-scene

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Smart Security: Unmasking Face Spoofers with Advanced Decision Tree Classifier 15

Smart Security: Unmasking Face Spoofers with Advanced Decisi...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Aparna Tiwari, Arvind Kumar Department of Computer Science and Engineering Dr. C. V. Raman University Kargi Road Chhattisgarh Kota India

ISBN: (纸本)9798350370249

Face spoofing, the act of supplying unreal face photos to deceive facial recognition systems, poses a significant chance to protection programs. This paper proposes an Advance Decision Tree (ADT) Classifier Algorithm for Face Spoof Detection with the usage of Local Binary pattern (LBP) functions. This method enhances conventional Decision Tree strategies by introducing advanced splitting criteria and optimization strategies to improve category performance. Firstly, in this work extract LBP features from face photos, converting every pixel right into a binary wide variety to capture important texture facts. The resulting LBP codes are then used to create a histogram function vector for the category. ADT Classifier set of rules proceeds with the development of the decision tree, beginning with dataset initialization and node splitting primarily based on the most suitable splitting standards. Work incorporates advanced strategies, which include Cost-Complexity Pruning, Random Feature Selection, and Optimal Split Search, to enhance decision-making and improve generalization. The study additionally outlines the model evaluation procedure, including classification function definition, performance metric calculation, and cross-validation. Our set of rules achieves robust face spoof detection by effectively leveraging LBP capabilities and ADT technique, ensuring excessive performance and generalizability in actual global situations. ©2024 IEEE.

关键词： Local binary pattern

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On Two-Dimensional Approximate pattern Matching Using Fuzzy Automata 12th

On Two-Dimensional Approximate Pattern Matching Using Fuzzy ...

引用

12th international conference on soft computing for Problem Solving, SocProS 2023

作者： John Kaspar, A. Sheena Christy, D.K. Department of mathematics Christ University Karnataka Bengaluru560029 India Department of mathematics SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819731794

pattern matching has been extensively studied in the last few decades, owing to its great contribution in various fields such as search engines, computational biology, etc. Several real-life situations require patterns that allow ambiguity in specified positions. In this paper, one-dimensional and two-dimensional approximate pattern matching models have been constructed using fuzzy automata. The similarity function used in fuzzy automata enables the occurrence of all exact and similar one-dimensional and two-dimensional patterns. This kind of searching approximate patterns is not possible with regular search models. The time complexity of the proposed algorithm has also been analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Search engines

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Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation 26

Weak-supervised Visual Geo-localization via Attention-based ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Xu, Yifan Shamsolmoali, Pourya Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Visual geo-localization aims to estimate the geographical location of a query image by identifying the best-matched reference image from a GPS-tagged database. It remains a challenging task because of image appearance changes such as lighting, scale and pose. The current approaches do not have satisfactory performance for large-scale environments owing to the lack of learning discriminative features for image matching. To address the above problem, we introduce a practical method to exploit a weak-supervised model with selective transfer for feature distillation. We propose an image matching method that uses image sub-regions to adequately analyze the potential of difficult positive images. For improving the network generations and performance, the model estimates image-to-region similarity labels at no additional parameters or manual annotations by use of soft-labeled loss. Moreover, to have optimal training we propose a novel knowledge distillation (KD) method to effectively capture and transfer knowledge of a teacher network to a student network. More specifically, our method uses an attention network to learn relative similarities within features and utilizes these similarities to enhance the distillation intensities by further exploring the potential of difficult positive images. Our model achieves significant localization performance over large variations of appearance on three challenging datasets with satisfactory efficiency. Our code is available at https://***/XuYifan98/WAKD.

关键词： Knowledge engineering Training Visualization Image matching Image retrieval Lighting Benchmark testing

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MultiPoseSeg: Feedback Knowledge Transfer for Multi-Person Pose Estimation and Instance Segmentation 26

MultiPoseSeg: Feedback Knowledge Transfer for Multi-Person P...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Ahmad, Niaz Khan, Jawad Kim, Jeremy Yuhyun Lee, Youngmoon Hanyang Univ Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-person pose estimation and instance segmentation suffer large performance loss when images are with an increasing number of people and their uncontrolled complex appearance. Yet, existing models cannot efficiently leverage unbalanced training images, i.e., few of them are with multi-person, and most are with single-person, making them ineffective for challenging multi-person scenarios. To tackle multi-person cases with a limited portion of them, we propose MultiPoseSeg, a data preparation and feedback knowledge transfer system designed for multi-person pose estimation and instance segmentation. First, MultiPoseSeg categorically performs random data reduction to reduce the single-person bias from the train dataset. Second, MultiPoseSeg employs the knowledge transfer from ancestor models to converge the model learning with a limited amount of data and time. This way, our model learns and train on human pose and instance segmentation to advance the training and testing accuracy. Finally, MultiPoseSeg proposes keypoint maps to identify the keypoint coordinates for soft and hard keypoints and segmentation maps to assign centroid to each human instance, which helps to cluster the pixels to a particular instance. We have evaluated MultiPoseSeg using COCO and OCHuman challenging datasets and demonstrated MultiPoseSeg outperforms state-of-the-art bottom-up models in terms of both accuracy and runtime performance, achieving 0.728 mAP for pose and 0.445 mAP for segmentation on COCO dataset. All the unbiased data and code has been made available at: https://***/RaiseLab/MultiPoseSeg

关键词： Training Image segmentation Runtime Codes Pose estimation Data models pattern recognition

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