Chuwongin, Santhad
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Chuwongin, Santhad
Alternative Name
Chuwongin, S.
Chuwongin, Santad
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Email
santhad.ch@kmitl.ac.th
10 results
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Item type:Publication, Generative AI for Industrial Applications: Synthetic Dataset(2023-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Highly-transparent multi-layered spin-coated silk fibroin film(2017-01-01); ; In this study, the silk fibroin films with different numbers of layers were fabricated by the spin-coating method and their optical transmittances were observed. The process to synthesise the silk fibroin solution was explained - starting from the silk cocoon until the silk-fibroin solution, approximately 7.5% concentration wt/vol, was obtained. The solution was spin-coated onto clean glass substrates to fabricate samples. Totally 10 samples with different numbers of layers, from 1 to 5 layers, were obtained. All samples can be separated into two groups: those left dried at room temperature after spin-coating and those heated at 60°C. They were then measured for their transmittance over the visible-to-near-infrared region. All samples exhibited the high transmittance where the values were at 95% and 98%, for the samples at room temperature and those at 60°C, respectively. This was believed to be due to the heating effect that caused the silk fibroin to arrange itself after being heated, hence the higher transmittance. These high transmittances were maintained regardless of the number of layers and length of heating time. Results from this study could be used to fabricate a silk fibroin film with high optical transmittance and adjustable other properties. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of Self-Supervised Learning with Dinov2-Distilled Models for Parasite Classification in Screening(2023-01-01); ; Tongloy, TeerawatAt present, parasitic infections in humans, such as intestinal parasitic infections and soil-transmitted helminth (STH) infection, remain a public health concern, with screening methods that are simple but time-consuming and require parasitology experts. Microscopy images are increasingly being used to aid diagnosis but creating labels for supervised learning (SL) is a time-consuming, labor-intensive, and costly process. Self-supervised learning (SSL) is a deep learning approach that aims to train models to represent features in unlabeled datasets using automatically generated labels or annotations from the data itself, rather than explicitly labeled human-labeled labels. It is an appropriate method to address the challenges associated with the difficulty of labeling large datasets. A pretrained model that has learned useful data representations from an SSL task is fine-tuned using labeled data to perform well on a specific downstream task. DINOv2 is an SSL model based on the Vision Transformer (ViT) architecture. In this study, we aim to create a model for screening for helminth egg infection using a fine-tuned Dinov2 with a classification layer head to demonstrate that dataset sizes of 1% and 10% are sufficient when compared to SL model. Rather than SL, which requires a significant amount of human data labeling and is generally impractical, the model developed in this study is expected to be used in active surveillance in the future. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification model of optical character recognition failures in unrecovered slider serial numbers in hard disk drive manufacturing and image capture processes(2022-01-01); In hard disk drive (HDD) manufacturing processes, there are unrecovered serial number images about 0.01% from the standard optical character recognition (OCR) reading and deep learning approach. We found several failures from two main causes, i.e. manufacturing process and image capture process during standard OCR reading. We proposed classification model used for recognizing the serial number reading failures based on object detection You-Only-Look- Once (YOLO) algorithm and EfficientNet-B0 classification network as well as histogram analysis. The 1000 images captured by digital camera were used for training (600 images) and validation (400 images) the ROI detection model. The other 2100 captured images were used for training and testing classification OCR failure from manufacturing process model. The model testing was performed in 900 images contained 9 causes (classes) of failures. The proposed model reaches F1 score = 0.94. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Images Retrieval and Classification for Acute Myeloid Leukemia Blood Cell Using Deep Metric Learning(2023-01-01); ; Deep metric learning-based image retrieval systems have recently been used in medical applications because they provide clinically relevant information-based similar images based on prior knowledge. Although train examiners and deep learning models successfully analyze leukocyte cells, there are still numerous difficult challenges due to biological variation, time constraints, and a variety of image-related aspects. In this study, we propose a deep metric learning model-based image retrieval and classification system for acute myeloid leukemia blood cells to address these issues and assist physicians. The proposed model utilizes the pre-trained ResNet-34 model as the backbone network, embedding loss with multi similarity miner, and M-Per-Class sampling strategy to learn an embedding function. The five embedding losses were also applied to compare the four performances in order to determine the best loss-based model. Based on the best loss-based model, the class-wise precision and sensitivity using a neighborhood size are also presented. The results show that the contrastive loss-based deep metric learning model achieved the highest precision of 94.90%, sensitivity of 94.85%, specificity of 99.64%, and accuracy of 99.32% in model comparison. Except for a few failures in small classes, the class-wise precision and sensitivity scores looked to be impressive in all classes. Therefore, this proposed system can highly be effective in screening and diagnosing of AML-related white blood cell stages that cause serious cancer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Three-stage deep learning system for recognizing contaminated serial numbers in hard disk drive: A comparison study with two-stage deep learning model(2022-01-01); The previous two-stage deep learning model for detecting and classifying misidentified serial numbers on the defect hard disk drive (HDD) slider was proposed by authors. We found that the threshold level adjusted during preprocessing process could limit the robustness of the two-stage model in large-scale manufacturing. Thus, we proposed a three-stage deep learning model comprised of 1) region of interest (ROI) detection and cropping, 2) character detection and cropping, and 3) character classification. Object detection algorithm and classification network used in this model are based on YOLO v.4 and EfficientNet-B0. The 1000 images captured by the digital camera were used for training (600 images) and validation (400 images) of the ROI detection model. The other 1000 captured images were used for testing the performance of the proposed three-stage model, then we compared them with those obtained from the previous two-stage model. The proposed three-stage model reaches F1 score = 0.997 and recovery rate up to 95.9%, while the two-stage model yields only 0.948 and 73%, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CiRA CORE: A Low Code Platform that Makes AI Work for Industry 4.0(2024-01-01); ; Tongloy, TeerawatCiRA CORE is a central hub designed to connect AI technology creation with practical application, making it easier to work with ROS (Robot Operating System) and link different systems through a user-friendly drag-and-drop interface. This approach removes the need for extensive coding, making the platform accessible to those with minimal programming experience. CiRA CORE offers a comprehensive suite of features for AI development and robot control, including algorithm creation, AI model training, and device integration commonly used in industrial settings. It supports tasks like image recognition and facilitates data storage, labeling, and integration with other systems for data-driven AI development. Overall, CiRA CORE aims to democratize AI development and robot control, simplifying AI development for Industry 4.0 applications, and leading to increased efficiency, reduced costs, and improved safety in industrial processes. This paper reports the progress of the CiRA CORE training modules funded by the SMCS TEAM Program Award. The project has completed the design of a 6-axis robot 3D training kit and simulation models for CiRA CORE training modules. The next steps involve developing 3D-printed robots and training materials. The main goal is to democratize advanced robotics and AI by simplifying integration through a visual, node-based programming interface. This approach reduces the need for complex coding, making these technologies accessible to users with limited programming experience. This initiative aims to foster widespread adoption in business and industrial settings, aligning with IEEE SMC's mission to promote professional growth and innovation in robotics and AI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Superior Automatic Screening for Human Helminthic Ova by Using Self-supervised Learning Approach-Based Object Classification(2023-01-01); ; Tongloy, TeerawatHuman parasitic infections remain one of public health concerns for 1.5 billion people worldwide including Thailand. Conventional microscopic examination is a gold standard method and often used to identify the helminth ova and filariform larvae and also protozoa cyst in stool-dependent simple smear. The benefits of traditional techniques are diminished by time-consuming, complicated procedures, massive labor, and skilled and trained parasitologists. An automatically rapid screening of the most in need of treatment is considered to replace the conventional technique. Here, we aim to develop a deep convolutional residual network based self-supervised learning model to identify mostly common parasite ova in Thailand. Although small amounts of training data was used to train the proposed model, the result shows superior performance over 95% accuracy. As a result, low values of false positive and false negative based confusion matrix table found revealed the robustness of the proposed models. General accuracy of self-supervised learning based the area under a ROC curve proposed with greater than 94% is also support an outstanding model studied. Therefore, rank of 1% to 10% of fine-tuning data labelled used bring us about a comparable model to that of using a 100% labelled training data. These findings emphasize the transformative potential of the BYOL method for screening of parasitic infection, particularly in resource-limited settings where is a lack of supportive lab equipment and skilled parasitologists to manage a large amount of challenging data in the future. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach(2023-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Asynchronous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks(2017-07-02); ;