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Advancing PCB Quality Control:Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection

computers, Materials & Continua 2024年第10期81卷 345-367页

作者： Rehman Ullah Khan Fazal Shah Ahmad Ali Khan Hamza Tahir Faculty of Cognitive Sciences and Human Development Universiti Malaysia SarawakKota Samarahan94300Malaysia School of Computer Science and Engineering Anhui University of Science and TechnologyHuainan232001China Department Electronic Quaid-i-Azam UniversityIslamabad45320Pakistan

Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working *** play a crucial role in modern electronics by connecting various *** trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control *** the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system *** address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in ***,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 *** rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing *** results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB *** model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of *** findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.

关键词： Printed circuit boards(PCB) YOLOv8 YOLOv8 Nano YOLOv8 Small deep learning object detection

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Analysis on Improved Gaussian-Wiener filtering technique and GLCM based Feature Extraction for Breast Cancer Diagnosis 2

Analysis on Improved Gaussian-Wiener filtering technique and...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Ranjitha, K.V. Pushphavathi, T.P. Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bangalore India

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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Domination based graph neural networks

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International Journal of computers and Applications 2024年第11期46卷 998-1005页

作者： Meybodi, Mohsen Alambardar Safari, Mahdi Davoodijam, Ensieh Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan Iran Department of Software Engineering University of Kashan Kashan Iran

Graph Neural Networks (GNNs) have emerged as a widely used and effective method across various domains for learning from graph data. Despite the abundance of GNN variants, many struggle with effectively propagating messages over long distances. This paper introduces a novel hierarchical message passing framework for graph learning, specifically designed to address the challenge of long-distance message propagation in graphs. By constructing smaller graphs from the main graph using the concept of domination, a fundamental principle in graph theory, we facilitate more efficient message passing within each subgraph. Subsequently, we employ a Graph Attention Network (GAT) to aggregate these features and propagate them to distant nodes across the graph. Experimental results on standard node classification datasets validate that the proposed architecture achieves performance comparable to or better than conventional GNNs. Additionally, our model consistently performs better on graphs with missing edges. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Neural network models

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Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

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computers, Materials & Continua 2024年第1期78卷 1461-1479页

作者： Sarah Mustafa Eljack Mahdi Jemmali Mohsen Denden Mutasim Al Sadig Abdullah M.Algashami Sadok Turki Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Higher Institute of Computer Science and Mathematics University of MonastirMonastir5000Tunisia Mars Laboratory University of SousseSousseTunisia Department of Logistic and Maintenance UFR MIM at MetzUniversity of LorraineMetzFrance Department of Computer and Information Technologies College of Telecommunicationand Information Riyadh CTITechnical and Vocational Training Corporation TVTCRiyadh12464Saudi Arabia Department of Computer Science Higher Institute of Applied Sciences of SousseSousse UniversitySousse4000Tunisia

In the cloud environment,ensuring a high level of data security is in high *** planning storage optimization is part of the whole security process in the cloud *** enables data security by avoiding the risk of data loss and data *** development of data flow scheduling approaches in the cloud environment taking security parameters into account is *** our work,we propose a data scheduling model for the cloud *** is made up of three parts that together help dispatch user data flow to the appropriate *** first component is the Collector Agent whichmust periodically collect information on the state of the network *** second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the *** third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable *** should be noted that each part of the proposedmodel requires the development of its *** this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link *** algorithms are based on the grouping of transmitted files and the iterative *** proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)*** experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

关键词： Cyber-security cloud computing cloud security algorithms heuristics

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Arbitrary style transfer system with split-and-transform scheme

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Multimedia Tools and Applications 2024年第23期83卷 62497-62517页

作者： Tu, Ching-Ting Lin, Hwei Jen Tsai, Yihjia Lin, Zi-Jun Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

For the subject of arbitrary image style transfer, there have been some proposed architectures that directly compute the transformation matrix of the whitening and coloring transformation (WCT) to obtain more satisfactory transformation results. However, calculating the transformation matrix of WCT is time-consuming. Li et al. trained a linear transformation module to generate a WCT transformation matrix for any pair of images, i.e., content image and style image, to avoid complex calculations and improves time efficiency. In this work, we introduce a flexible arbitrary image style transfer framework based on the LST, which uses deep neural networks to train a linear transformation matrix as the standard matrix for WCT. For the first part, inverse relationship between the Whitening matrix and the Coloring matrix w.r.t. the same image is enforced during the training of the linear transformation matrix, so that the resulting matrix will be more accurate and closer to the standard matrix of WCT. For the second part, a split-and-transform scheme is proposed. Unlike LST, which transforms the block of feature maps as a whole, the split-and-transform scheme divides the feature block into several smaller blocks and transforms them individually, so that the transformation is more localized, and the more the number of divided blocks, the more localized. In addition, the proposed split-and-transform scheme allows users to determine the number of divided blocks to flexibly control the locality of the transformations. Experimental results demonstrate the effectiveness and flexibility of the proposed framework by the high-quality stylized images and adjustable balance between globality and locality of transformations. The use of the split-and-transform scheme can reduce the computational time while preserving or even improving the stylization results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Linear transformations

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Disease Diagnosis from Facial Alternations Using Ensemble CNNs

Disease Diagnosis from Facial Alternations Using Ensemble CN...

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2024 International Research Conference on Smart Computing and Systems Engineering, SCSE 2024

作者： Surasinghe, Pabasara Krishnapillai, Keerthiha Sabapathippillai, Papiththira Thanikasalam, Kokul University of Vavuniya Faculty of Applied Science Department of Physical Science Vavuniya Sri Lanka University of Peradeniya Faculty of Science Department of Statistics and Computer Science Kandy Sri Lanka University of Jaffna Faculty of Science Department of Computer Science Jaffna Sri Lanka

ISBN: (纸本)9798350375688

Identifying health conditions from facial images is crucial for the early detection of certain diseases and provides crucial information for timely intervention. This study introduces a novel ensemble convolutional neural network (CNN) classifier with a visualization technique for diagnosing Bell's palsy, Parry-Romberg syndrome, and Moebius syndrome. As the first step of this study, a dataset was constructed using publicly available images due to the unavailability of benchmark datasets to detect these diseases. The proposed ensemble CNN classifier combines the strengths of ResNet-50, VGG-16, and DenseNet-121 to classify diseases with high accuracy. In addition, a visualization technique was developed to identify the most influential facial regions for detecting these diseases. The proposed ensemble CNN classifier achieved a classification accuracy of 91.96% on the test set of the constructed dataset. © 2024 IEEE.

关键词： Visualization

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A New Notion of Reserve for Power Systems with High Penetration of Storage and Flexible Demand

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第2期1卷 131-144页

作者： Tsaousoglou, Georgios Technical University of Denmark Department of Applied Mathematics and Computer Science Lyngby2800 Denmark

Modern power systems face important demand uncertainties due to increasing penetration of behind-the-meter renewable generation. System operators need to account for such uncertainties when solving the unit commitment and economic dispatch problem. The research literature has proposed advanced methods for decision making under uncertainty but, in practice, actual system operators put more trust in the tried-and-true approach of dealing with future uncertainty by committing reserves. In this paper, the unit commitment and economic dispatch problem is formulated for a system with high penetration of storage and the inadequacy of methods based on the traditional notion of reserves is exposed. Namely, in contrast to a generator, a storage unit can provide reserve capacity in a number of timeslots but it cannot provide an analogous reserve activation in all of those timeslots due to the battery's energy being depleted. After discussing two plausible but inadequate approaches, a new, generalized notion of reserves is proposed, which addresses these issues while not abandoning the practical, reserve-based approach for the operator's problem, thus making the best of both worlds. The proposed scheme enables storage units to provide reserves, without putting the system at risk of energy scarcity, which is shown to result in substantial cost savings. © 2023 IEEE.

关键词： Decision making

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition 28

Enhance Efficient of Support Vector Machine with CNN and PSO...

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28th International computer science and Engineering Conference, ICSEC 2024

作者： Janprasit, Siwach Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Thammasat University Data Science and Innovation College of Interdisciplinary Studies Thailand

ISBN: (纸本)9798350366860

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: The MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Load-Independent Class-Φ3 Resonant Inverter

Design of Load-Independent Class-Φ3 Resonant Inverter

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Komiyama, Yutaro Zhu, Wenqi Konishi, Akihiro Nguyen, Kien Sekiya, Hiroo Chiba University Department of Applied and Cognitive Informatics Chiba Japan Tokyo University of Science Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350376067

This paper proposes the design of a load-independent class-Φ3 resonant inverter. The feasibility of achieving the load-independent operation in the class-Φ3 inverter is investigated through an analysis. The analysis reveals the load-independent design conditions of the class-Φ3 inverter. The load-independent class-Φ3 inverter inherently achieves zero-voltage switching (ZVS) and a constant output-voltage amplitude against load variations without any controls. Experimental verifications were carried out at 6.78 MHz with a prototype inverter. The load-independent class-Φ3 inverter achieved 87.5 %-95.9 % power-conversion efficiency within a tenfold change in the load resistance, showing the effectiveness of the proposed inverter. © 2024 IEEE.

关键词： Zero voltage switching

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