Phasukkit, Pattarapong
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Phasukkit, Pattarapong
Alternative Name
Phasukkit, P.
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Email
pattarapong.ph@kmitl.ac.th
7 results
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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, Cross-Sensor and Cross-Population Generalization of Deep Learning Models for Digital Mammography: A Controlled Four-Country Benchmark of Five Backbone Architectures with Statistical Significance Testing(2026-06-01); Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques(2024-09-01); ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures(2026-05-01); ; Sritart, HiranyaMonolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry. - 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, Optimal antenna slot design for hepatocellular carcinoma microwave ablation using multi-objective fuzzy decision making(2020-10-01); ; Chayakulkheeree, KeeratiThis paper presents the optimal antenna slot position and sizing (OASPS), for microwave (MW) hepatocellular carcinoma (HCC) treatment, using multi-objective fuzzy decision making (MOFDM). In the optimal design problem formulation, the multi-objective for, (1) achievement for a near-spherical zone for ablation defined by axial ratio (AR), (2) the minimum reflection coefficient (S11), and (3) maximum volume of destroying (VD), are desired. The finite element method (FEM) is used for MW distribution simulation for coordination analysis with the proposed MOFDM based OASPS. The investigation on the design results shown that the temperature distribution of the proposed MOFDM for OASPS is in the more controllable shape comparing to the existing design, due to the sharp beam of temperature distribution to the target at the front side of slot with the less temperature distribution at the rare side of slot. The results also show that the proposed MOFDM for OASPS for OASPS design is easier to manage with the less effect to the rare side of the slot. Moreover, the proposed MOFDM for OASPS resulted in the temperature distribution shape closer to round shape than the existing design. The MOFDM for OASPS is tested on designing of the antenna build up from the coaxial cable. The coaxial cable used is Semi-rigid 141 (RG402 M17/130-RG402 Copper Jacket) including Inner conductor, Dielectric, and Outer conductor. In the simulation, the slot distance from conductor end (Lts), representing the slot position, is varied as 2.3mm, 3.3mm, 4.3mm, 5.3mm, 6.3mm, 7.3mm, and 8.3mm. Meanwhile, the slot size (Wd), is varied as 1mm, 2mm, 3mm, 4mm, 5mm, and 6mm. The input power (Pi) used is 50 W with the duration of 300 seconds. The comparison of obtained by different L<inf>ts</inf> and W<inf>d</inf>, is investigated. The simulation results show that the proposed MOFDM based OASPS can efficiently and effectively provide the near-spherical zone of ablation result with simultaneously trade-off between S11 minimization and VD maximization. - 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.