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Subject: search.filters.subject.convolutional neural network (CNN)

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    The Study of Image Quality Effect on Model Performance for Bacteria Classification
    (2025-01-31)
    Treebupachatsakul, Treesukon
    ;
    Chomkwah, Wanwalee
    ;
    Tanpatanan, Tananan
    ;
    Poomrittigul, Suvit
    One of the key requirements for supervised learning in deep learning model construction is the dataset for training and validation. For gathering the dataset, obtaining various image qualities from different resources is unavoidable, and this has been considered to affect the supervised model performance. This research proposes to demonstrate the effect of image quality involving high and standard datasets obtained from 2 different resources on the performance of models. The various cell characteristics with gram-positive and gram-negative bacteria datasets were challenged for trial. These different datasets were matched and contributed to 5 cases; case 1: train and test with high-quality images, case 2: train with high-quality images and test with standard quality images, case 3: train and test with images of standard quality, case 4: train with standard-quality images and test with high-quality images, and case 5: train and test with combining these two image qualities. Pre-trained CNN models were implemented to prove the purpose with and without stratified K-fold cross-validation. The results of retrained models showed that the high-performance models require high-quality datasets obtained from the same resource as the testing set, which yield more than 90% of all performance evaluation metrics when tested on challenging unseen datasets. This study provides valuable insights for building high-performance models that can be applied to automate microbiology diagnostics, impacting public health and clinical practice.
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    Optimizing Wafer Classification in Industrial Manufacturing Using Particle Swarm Optimization and Deep Learning
    (2025-01-01)
    Suwannoot, Pisit
    ;
    Konghuayrob, Poom
    In this study, we examine the application of convolutional neural networks (CNNs) for wafer pattern classification, with a focus on enhancing training efficiency and model performance. To achieve this, particle swarm optimization (PSO) is employed to improve the model performance while reducing its complexity, a critical factor in production environments. By minimizing the number of layers, the proposed method accelerates training, reduces resource consumption, and enhances defect detection accuracy. Wafer failure patterns are classified into four categories: vertical, rectangular, edge, and horizontal. The approach achieves an impressive F1-score of 0.988, significantly surpassing the traditional CNN’s score of 0.83. By integrating PSO, the method considerably improves the visual inspection process for hard disk drives, contributing to high-quality production. This optimization not only streamlines workflows but also enables manufacturers to address issues more rapidly, aligning with Industry 4.0’s objectives of automation and intelligent monitoring.
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    Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques
    (2024-09-01)
    Tuntiwong, Kuson
    ;
    Tungjitkusolmun, Supan
    ;
    Phasukkit, Pattarapong
    Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.
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    Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems
    (2023-01-01)
    Kankhunthod, Kittipon
    ;
    Warisarn, Chanon
    One of the problems that cause a decrease in the performance of the ultra-high bit-patterned magnetic recording (BPMR) system is track misregistration (TMR). Since the gap between data tracks is extremely narrow, it easily affects keeping the reader in the desired position. Therefore, this paper proposes the track misregistration mitigation included the estimation and correction techniques on single-reader/two-track reading (SRTR) BPMR using only a readback signal. The TMR estimation technique uses the convolutional neural network (CNN) to estimate the TMR level by the histograms of the readback signal enabling minimization of the complexity of the CNN structure and amount of training time. The estimated TMR levels obtained from the proposed CNN-histogram-based method will then be utilized to detect the estimated recorded bit by the CNN-based data detector. The simulation shows that our proposed system provides better TMR prediction accuracy even though the system has to face higher media noise. Furthermore, the CNN-based data detectors perform superior to the partial response maximum likelihood (PRML) based data detector, especially in strong electronic noise situations and the severe imperfection of recording media.
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    Ship Classification in Remote Sensing Images using FastAI
    (2021-01-01)
    Roungroongsom, Chittra
    ;
    Chitsobhuk, Orachat
    Specifying ship categories in waterways plays an important role in the field of marine surveillance, especially when classification is performed from satellite images due to the advancement in remote sensing technologies. In this paper, we presented an approach for ship classification of optical remote sensing images. Our approach was based on two aspects, modifying models and applying additional techniques to improve accuracy of classification. Two pretrained models, MobileNetV2 and DenseNet121, were modified in this work and all techniques were implemented using Fastai library. To illustrate the effectiveness of our approach, we compared the accuracy of the modified models to the original one. A public Dataset for Ship Classification in Remote sensing images (DSCR), containing six military ship types and a civilian ship type, was used for evaluation. The results showed that our modified DenseNet121 achieved the best accuracy at 99.52% and also outperformed the benchmark result of ResNet101 reported from the original dataset.
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    Robust and Unified VLC Decoding System for Square Wave Quadrature Amplitude Modulation Using Deep Learning Approach
    (2019-01-01)
    Alfarozi, Syukron Abu Ishaq
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    Pasupa, Kitsuchart
    ;
    Hashizume, Hiromichi
    ;
    Woraratpanya, Kuntpong
    ;
    Sugimoto, Masanori
    We have proposed a square wave quadrature amplitude modulation (SW-QAM) scheme for visible light communication (VLC) using an image sensor in our previous work. Here, we propose a robust and unified system by using a neural decoding method. This method offers essential SW-QAM decoding capabilities, such as LED localization, light interference elimination, and unknown parameter estimation, bundled into a single neural network model. This work makes use of a convolutional neural network (CNN) that has a capability in automatic learning of unknown parameters, especially when it deals with images as an input. The neural decoding method can provide good solutions for two difficult conditions that are not covered by our previous SW-QAM scheme: unfixed LED positions and multiple point spread functions (PSFs) of multiple LEDs. Responding to the above solutions, three recent CNN architectures - VGG, ResNet, and DenseNet - are modified to suit our scheme and other two small CNN architectures - VGG-like and MiniDenseNet - are proposed for low computing devices. Our experimental results show that the proposed neural decoding method performs better in terms of error rate than the theoretical decoding, an SW-QAM decoder with a $Wiener$ filter, in different scenarios. Furthermore, we experiment on the problem of moving camera, i.e., the unfixed position of LED points. For this case, a spatial transformer network (STN) layer is added to the neural decoding method for solving the moving camera problem, and the method with the new layer achieves a remarkable result.