Satayarak, Nitjaree

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Satayarak, Nitjaree
Alternative Name
Satayarak, N.
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nitjaree.sa@kmitl.ac.th

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    Item type:Publication,
    Blood Vessel Extraction and Optic Disk Localization for Diabetic Retinopathy
    (2020-09-15)
    Kanjanasurat, Isoon
    ;
    Purahong, Boonchana
    ;
    Pintavirooj, Chuchart
    ;
    Satayarak, Nitjaree
    ;
    Benjangkaprasert, Chawalit
    This paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%.
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    Comparison of logistic regression and artificial neural network model for apron allocation assignment
    (2023-01-01)
    Purahong, B.
    ;
    Teerapanpong, S.
    ;
    Satayarak, N.
    ;
    Benjangkaprasert, C.
    Management of the parking apron is one of the most essential airport ground service operations for flight operations to run smoothly. Effective airport ground service management will have a direct effect on the cost and duration of flights. Therefore, in this paper, we address the issue of using machine learning techniques, such as logistic regression analysis and artificial neural network (ANNs) models, for classified targets of stand locations assignment of an arriving flight. Also, this could assist ground controllers to assign apron allocation and improve the efficiency and predictability of airport operations which reduce the time required for airport ground processing to increase flight capacity. In order to evaluate the performance of the proposed method, simulation results reveal that ANN has the lowest error rate and the highest accuracy. Therefore, ANN is the effective classification technique for this data set.
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    Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
    (2024-01-01)
    Purahong, Boonchana
    ;
    Krungseanmuang, Woranidtha
    ;
    Tenghongsakul, Kasi
    ;
    Archevapanich, Tuanjai
    ;
    Khunthawiwone, Parkpoom
    This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.