Boonsang, Siridech

Preferred name

Boonsang, Siridech

Alternative Name

Boonsang, S.

Main Affiliation

School of Engineering

siridech.bo@kmitl.ac.th

Now showing 1 - 10 of 42

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Cloud-based optical characterization of hard disk drive recording heads
(2018-01-01)
Khunrattanasiri, Weerayuth
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Yokpradit, Anchyza
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Tongloy, Teerawat
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Boonsang, Siridech
In the hard disk drive (HDD) industry, the measurement of flying height is crucial for the verification of the recording head air-bearing design. The flying performance of recording heads has to be measured and compared with the original design to assure the reliability of HDDs. Optical interferometry is normally used to measure the flying height of recording heads. The key parameter for setting up the measurement is the refractive index, whose values normally deviate depending on machine conditions during the manufacturing processes. The variation in refractive index cannot be identified during flying height measurement because there is no on-line technique for doing so. In this paper, we present a novel technique using inhouse designed micro-ellipsometry to measure the complex refractive index combined with data transfer to a private cloud network via the Internet of Things message queuing telemetry transport (IoT MQTT) protocol. Therefore, the measurement of the complex refractive index can be carried out for each recording head and we can improve the precision of the flying height measurement. In addition, the information about refractive index not only can be shared with a flying height tester to correct the variation in each recording head but also can be used to assist air-bearing designers in the instantaneous validation of their design.
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Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system
(2023-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Samung, Yudthana
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Jomtarak, Rangsan
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Naing, Kaung Myat
Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed—trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.
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Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: A cross-sectional diagnostic study
(2022-01-01)
Pongsakonpruttikul, N.
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Angthong, Chayanin
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Kittichai, V.
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Chuwongin, S.
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Puengpipattrakul, P.
OBJECTIVE: Radiographic interpretation suffers from an ever-increasing workload in orthopedic and radiology departments. The present study applied and assessed the performance of a convolutional neural network designed to assist orthopedists and radiologists in the detection and classification of knee osteoarthritis from early to severe degrees in accordance with the Kellgren-Lawrence (KL) classification system. MATERIALS AND METHODS: In total, 1650 knee joint radiographs (anteroposterior view) were collected from the Osteoarthritis Initiative public resource. Two models were developed: one distinguished normal (KL 0-I) from osteoarthritic knees (KL II-IV), and the other classified the severity as normal (KL 0-I), non-severe (KL II), or severe (KL III-IV). The regions of interest were labeled under the supervision of experts. Our artificial intelligence (AI) models were trained using the You Only Look Once version 3 (YOLOv3) detection algorithm. RESULTS: Our first AI model using YOLOv3 tiny could detect and classify normal and osteoarthritic knees on plain knee joint radiographs with 85% accuracy and 81% mean average precision. The second AI model for classifying severity achieved a total accuracy of 86.7% and mean average precision of 70.6%. CONCLUSIONS: Our proposed deep learning models provided high accuracy and satisfactory precision for the detection and classification of early to severe knee osteoarthritis on anteroposterior radiographs. These models may be used as diagnostic aids by interpreting knee radiographs and guiding the treatment options via each osteoarthritic stage for related physicians and specialists.
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Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning
(2026-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Soiphet, Naruchit
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Tongloy, Teerawat
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Chuwongin, Santhad
Background: Zoonotic diseases pose a major threat to both human and animal health, contributing significantly to global morbidity and mortality. Accurate diagnosis is crucial for effective control and treatment, with microscopic examination serving as the gold standard, supplemented by highly sensitive molecular biology techniques. However, these confirmatory methods require skilled personnel and are subject to inter- and intra-rater variability. An innovative solution lies in artificial intelligence (AI)-powered automated tools, which offer a promising alternative. This study aimed to develop a self-supervised learning (SSL) approach using the Distillation with No Labels (DiNOv2) algorithm to extract features of protozoa from Giemsa-stained blood samples. Methods: The development of self-supervised learning algorithms, including DiNOv2, was based on archived samples of clinically significant and significant veterinary microorganisms. These models were evaluated in comparison to a baseline Vision Transformer (ViT). Results: Among the tested SSL models, the DiNOv2-Small version achieved exceptional performance, surpassing 99% accuracy and specificity while maintaining the lowest misclassification rate (0.263). It also demonstrated a high area under the curve (AUC) value of 0.990, underscoring its robust classification capability. Remarkably, even when trained on only 20% of the dataset, the SSL models retained performance levels comparable to baseline models trained on the full dataset. However, further reducing the sample size below 20% led to notable declines in evaluation metrics: accuracy dropped by 5–7%, recall decreased from 37.9% to 35.1%, precision fell from 35% to 25.2%, and the F1 score declined from 31.1% to 21.5%. Additionally, the AUC decreased by 4–11%, while the misclassification rate increased, indicating reduced robustness. A key limitation of this study was the highly imbalanced fine-tuned dataset between classes. Nevertheless, the inconsistent performance observed may be mitigated by employing the larger DiNOv2 model, which improves the F1 score and enhances the model’s ability to handle imbalanced data while reducing reliance on labeled data. Conclusions: The proposed method can assist laboratory technicians, particularly in resource-limited healthcare settings. Furthermore, the findings support the potential deployment of this AI-based tool for automated screening in both medical and veterinary applications.
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Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks
(2021-12-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Thanee, Suchansa
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Jomtarak, Rangsan
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Klanboot, Kamonpob
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.
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Tailoring silk fibroin hydrophilicity and physicochemical properties using sugar alcohols for medical device coatings
(2024-12-01)
Kaewpirom, Supranee
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Piboonnithikasem, Sarayoot
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Sroisroemsap, Pongsathorn
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Uttoom, Sittichai
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Boonsang, Siridech
This study explores the modification of silk fibroin films for hydrophilic coating applications using various sugar alcohols. Films, prepared via solvent casting, incorporated glycerol, sorbitol, and maltitol, revealing distinctive transparency and UV absorption characteristics based on sugar alcohol chemical structures. X-ray diffraction confirmed a silk I to silk II transition influenced by sugar alcohols. Glycerol proved most effective in enhancing the β-sheet structure. The study also elucidated a conformational shift towards a β-sheet structure induced by sugar alcohols. Silk fibroin–sugar alcohol blind docking and sugar alcohol-sugar alcohol blind docking investigations were conducted utilizing the HDOCK Server. The computer simulation unveiled the significance of size and hydrogen bonding characteristics inherent in sugar alcohols, emphasizing their pivotal role in influencing interactions within silk fibroin matrices. Hydrophilicity of ozonized silicone surfaces improved through successful coating with silk fibroin films, particularly glycerol-containing ones, resulting in reduced contact angles. Strong adhesion between silk fibroin films and ozonized silicone surfaces was evident, indicating robust hydrogen bonding interactions. This comprehensive research provides crucial insights into sugar alcohols’ potential to modify silk fibroin film crystalline structures, offering valuable guidance for optimizing their design and functionality, especially in silicone coating applications.
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Deep learning approaches for challenging species and gender identification of mosquito vectors
(2021-12-01)
Kittichai, Veerayuth
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Pengsakul, Theerakamol
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Chumchuen, Kemmapon
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Samung, Yudthana
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Sriwichai, Patchara
Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.
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A numerical model for ultrasonic measurements of swelling and mechanical properties of a swollen PVA hydrogel
(2010-08-01)
Lohakan, M.
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Jamnongkan, T.
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Pintavirooj, C.
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Kaewpirom, S.
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Boonsang, S.
This paper presents a numerical model for the evaluation of mechanical properties of a relatively thin hydrogel. The model utilizes a system identification method to evaluate the acoustical parameters from ultrasonic measurement data. The model involves the calculation of the forward model based on an ultrasonic wave propagation incorporating diffraction effect. Ultrasonic measurements of a hydrogel are also performed in a reflection mode. A Nonlinear Least Square (NLS) algorithm is employed to minimize difference between the results from the model and the experimental data. The acoustical parameters associated with the model are effectively modified to achieve the minimum error. As a result, the parameters of PVA hydrogels namely thickness, density, an ultrasonic attenuation coefficient and dispersion velocity are effectively determined. In order to validate the model, the conventional density measurements of hydrogels were also performed. © 2010 Elsevier B.V. All rights reserved.
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Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
(2023-10-01)
Kittichai, Veerayuth
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Kaewthamasorn, Morakot
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Thanee, Suchansa
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Sasisaowapak, Thanyathep
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Naing, Kaung Myat
Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.
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Analysis of multi-hop wireless sensor networks using probability propagation models
(2019-01-01)
Jaksukam, Komgrit
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Tongloy, Teerawat
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Chuwongin, Santad
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Boonsang, Siridech
This paper presents a formula for estimating the probability of collecting a given amount of data from a propagation model and multi-hop wireless sensor networks (WSNs) based on Monte Carlo simulation with cluster-tree topology. The probabilistic model is based on an analytical model of the IEEE 802.15.4 MAC protocol. The probability of successful node transmission is extended to the probabilities of successful collection at the cluster P(X=k) and sink node P(X ≥ k). A numerical example has been provided for comparing the probabilities. We propose a model to calculate the probability from the ratio of the collection rate to the total number of nodes and therefore provide the likeliness of complete data collection. Finally, the results from our analysis provide an estimation of the probability of achieving successful transmission in WSNs.

Boonsang, Siridech

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