Tuntiwong, Kuson
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Item type:Publication, Crack Localization Detection in Monolithic Zirconia Dental Crowns via 1D-Convolutional Neural Networks Algorithm-Based Acoustic Emission Analysis(2024-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Continuous Wavelet Transform based SqueezeNet for Damage Classification of Monolithic Zirconia Dental Crowns Using Acoustic Emission Analysis(2024-01-01); ; This work presents a novel approach utilizing continuous wavelet transform and deep learning for pattern recognition and classification of generated cracked signals. The research employs a smart sensing data acquisition system in a non-destructive manner, utilizing dual acoustic emission (AE) sensor to determine the specific cracked position at the crown surface. An AE data acquisition module is used to transform the AE signals captured by the AE sensor into.csv data, which is then subjected to denoising utilizing band stop and Bayesian filters to eliminate background noise. Subsequently, the denoised data are processed and classified using an algorithmic model called SqueezeNet, which facilitates the accurate localization of cracks in the Monolithic crown. For the purpose of training and validating the algorithmic model, the study uses AE signals produced by pencil lead breaking (PLB) at the incisal, labial, palatal, left, and right sides of the crown. In order to make it easier to identify the sources of AE signals resulting from PLB-induced AE events, wavelet transform (WT) is used to assess the location of the crown in respect to AE signals. AE signals are divided into two categories and the algorithm's accuracy in classifying them is assessed during the training and testing stages. With a total accuracy of 9 6. 5%, the deep learning-powered AE approach successfully detects flaws on the dental crown surfaces. The integration of a dual AE sensor with a SqueezeNet algorithm based on AE signals sets this study apart, offering an automated and efficient solution for early cracked detection in dental crowns for prototype clinical dental restorative crack identification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Monolith Zirconia Dental crown under Load(2023-01-01); ; This study introduces an innovation non-destructive approach employing dual-sensor acoustic emission (AE) techniques to detect and accurately locate cracks within monolith dental crowns, even when subjected to various load directions. Throughout the process, the AE sensor recorded AE signals that were subsequently converted into.csv data using an AE data acquisition module. Afterward, these digital records underwent denoising to eliminate background and contact-related disturbances. Subsequently, the noise-filtered data underwent processing and classification via a deep learning algorithmic model, enabling accurate localization of cracks within the dental crown. The study utilized AE signals generated by pencil lead breakage (PLB) at the labial, left, palatal, and right surfaces of dental crown for both training and testing of the algorithmic model. Initially, wavelet transform (WT) was employed to analyze and examine the dental crown's location in relation to AE signals. The approach was introduced to precisely identify the sources of AE signals originating from PLB-induced AE events. During the algorithm's training and testing phases, the AE signals were categorized into two groups, and the accuracy of their classification was evaluated. The deep learning-powered AE strategy successfully identified cracks within the dental crown. The total accuracy was 90.625%. This study's innovation resides in its utilization of a dual AE sensor combined with a deep learning algorithm based on AE signals. This combination effectively detects and precisely locates cracks within dental crowns. In contrast to current AE crack-localization methods that depend on human interpretation in radiographic examination, this approach offers an automated and more efficient solution. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Image Analysis for Gender Identification on Panoramic Dental X-Rays(2024-01-01);