Maneerat, Noppadol

Preferred name

Maneerat, Noppadol

Main Affiliation

School of Engineering

noppadol.ma@kmitl.ac.th

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Manufacturing Process Improvement by Barcode Reader using Image Processing
(2024-01-01)
Sukasem, Sutikamon
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Maneerat, Noppadol
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Thudthong, Jakkrit
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Wongsomboon, Chanathip
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Yajima, Kuniaki
Manufacturing industries get perspective on the efficiency of their production process. This study focuses on improving the cycle time of the work process at the station related to barcode reading. This operation should be of minimal duration to result in shorter cycle times and resulting in increased production volumes while still maintaining quality and reducing production costs. Image processing methodology is applied to reading barcodes instead of using a barcode reader which creates clear image quality along with developing an application using Android Bridge (ABD) and ZXing libraries that can read 1D barcodes in many formats. More essentially, application development by multithreading programming can process barcode reading simultaneously at a time that meets the target cycle time. The result of this study proves that the process of reading barcodes by human work has taken 60 times per job cycle and the reading time was about 29 seconds, becoming reduced to 19.85 seconds, manpower can be reduced by 1 person and production cost as well. Moreover, this implementation eliminates human errors that are likely to occur from scanning barcodes in the wrong position. Therefore, this study has benefited the manufacturing industry achieve significantly increased productivity and efficiency.
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Suitable Supervised Machine Learning Techniques for Malignant Mesothelioma Diagnosis
(2019-01-10)
Win, Khin Yadanar
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Maneerat, Noppadol
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Choomchuay, Somsak
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Sreng, Syna
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Hamamoto, Kazuhiko
Malignant Mesothelioma (MM) is a rare, aggressive cancer that grows in the lining of the internal organs such as lung, abdomen or heart. Fousing on MM diagnosis, in this paper, we investigate multiple machine learning methods and compare for accurate MM diagnosis results. Seven machine learning algorithms namely (i) Linear Discriminant Analysis (LDA), (ii) Naïve Bayes, (iii) K Nearest Neighborhood (KNN), (iv) Support Vector Machine (SVM), (v) Decision Tree (DT), (vi) Logistic Regression (LogR) and (vii) Random forest (RF) algorithms are exploited. The experiments dataset containing 324 cases with 34 features and six performance measures are used to assess the accuracy of evaluated classifiers. The average accuracy of LDA, NB, KNN, SVM, DT, LogR and RF are 61.73%, 67.90%, 91.36%, 100%, 100%, 100% and 100%, respectively. In addition, the computational complexity of each method is also analyzed. Each algoritm is judged based on its classification accuracy and computational complexity. It is found that SVM, DT, LogR and RF outperform the others and even previous studies.
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Detection of hard exudate for diabetic retinopathy using unsupervised classification method
(2020-07-01)
Maneerat, Noppadol
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Thongpasri, Teerapon
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Narkthewan, Athasart
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Kimpan, Chom
Diabetic retinopathy (DR) causes retinal disorders such as blood vessel blockage, the leaks of blood, and the proteins in water bleeding into the tissues of retina. All of the symptoms lead to the destruction of retina resulting in reduced visibility or finally lose vision. Therefore, this study presents an image processing method to extract hard exudates in the retinal image, which is a serious symptom of diabetic retinopathy using an unsupervised classification method. The proposed hard exudates extraction method composes of 3 steps. Firstly, the optic disc similar to hard exudate is eliminated from the retinal image. Subsequently, the green channel of the RGB color model is selected for data analysis because it represents all hard exudates better than the red and blue channels. The features of hard exudates in the retinal image are then extracted by various methods such as dilation, erosion, entropy analysis, and standard deviation analysis and it also appeared in many dimensions. Finally, the proposed method uses k-mean, which is an unsupervised classification technique for hard exudates clustering. The determination of hard exudates from the retinal image is achieved using two datasets (DIARETDB0 and DIARETDB1). These datasets are usually used for algorithm efficiency analysis to retinal image evaluation. The results show that the maximum specificity is approximate 97%. It indicates that the proposed method can be applied for the automatic detection of diabetic retinopathy.
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Hybrid learning of hand-crafted and deep-activated features using particle swarm optimization and optimized support vector machine for tuberculosis screening
(2020-09-01)
Win, Khin Yadanar
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Maneerat, Noppadol
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Hamamoto, Kazuhiko
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Sreng, Syna
Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.
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AC Breakdown and Resistivity of Natural Ester Based Nanofluids
(2020-10-25)
Maneerat, Noppadol
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Makmork, Kawee
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Kittikhuntharadol, Yannaphol
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Suksai, Nattapat
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Chusang, Thanapat
In this paper, AC breakdown including resistivity of natural ester and natural ester mixed with nanoparticles were studied. Two types of nanoparticles i.e. Barium titanate (BaTiO3) and Titanium dioxide (TiO2) which have their diameter less than 100 nm were used in experiment. For the nanofluid samples, three concentration of nanoparticles including 0.01%, 0.03% and 0.05% were prepared in five processes. Firstly, the natural ester mixed was with 0.01%, 0.03% and 0.05% of volume fraction of nanoparticles. Second, a magnetic stirrer was used to diffuse them evenly over the liquid later, ultrasonic dispersion was also used to ensure that the prepared nanofluids were homogeneous. Then, the nanofluid liquid was heated at 80 degree Celsius in vacuum oven for 15 hours. Finally, this liquid was dispersed by using magnetic stirrer again. Besides, the mixed liquid sample was heated at 110, and 130 degree Celsius for 72 hours to simulated thermal stress. To measure the AC breakdown voltage, a dielectric breakdown testing device was used. The test circuit was set up according to ASTM D1816. The gap distance between electrode was set at 2.0 mm. Furthermore, the test cell for liquid insulation testing (Model DAC-OBE-2: SOKEN) was used for polarization and depolarization current (PDC) experiment. The PDC test result was then used to calculated the resistivity of the liquid samples. The test results show that the AC breakdown voltages of the natural ester mixed with nanoparticles is higher than unmodified natural ester. Additionally, Barium titanate nanoparticles and Titanium dioxide nanoparticles enhance the breakdown strength and the resistivity of the ester nanofluid tested. According to the results, it concluded that nanoparticles were an good alternative to enhance the dielectric properties of natural ester for dielectric application.
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A proposal of global engineering PBL education using by developed sequence control kit
(2017-07-01)
Yajima, Kuniaki
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Nitta, Akihiro
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Takeichi, Yoshihiro
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Maneerat, Noppadol
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Sato, Jun
In recent years, a learning method called problem-solving learning (PBL), which is expected to acquire 'various facilitation' through learning, attracts attention. In this research, we aim to construct a learning environment that supports 'training human resources with various facilitation skills'. Therefore, we developed sequence control PBL learning kit which can carry out 1 Day PBL with overseas students and evaluate and improve the usefulness of PBL learning kit by actually implementing 1 Day PBL overseas we done.
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Application of protégé, SWRL and SQWRL in fuzzy ontology-based menu recommendation
(2009-12-01)
Fudholi, Dhomas Hatta
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Maneerat, Noppadol
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Varakulsiripunth, Ruttikorn
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Kato, Yasushi
We develop a daily menu assistance system which is built to give a suggested daily menu based on daily calories need. In this paper, we design a recommendation feature using fuzzy ontology to give menu recommendation by few factors, such as price, rate, vote and taste, since so many menus are suggested. Protégé, SWRL (Semantic Web Rule Language) and SQWRL (Semantic Query-Enhanced Web Rule Language) are used to show that this feature provides user the more specific, recommended and preferable menus. Protégé, an open-source ontology design software, is used to develop menu recommendation ontology. SWRL is used to build fuzzy ontology classes and properties mapping, and also calculations. SQWRL performs a query in menu recommendation results. ©2009 IEEE.
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Proposal of global PBL education for engineers using sequence learning kit
(2018-08-13)
Yajima, Kuniaki
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Maneerat, Noppadol
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Sugaya, Junichi
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Nitta, Akihiro
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Takeichi, Yoshihiro
Recently, in the field of education, learning methods called active learning and Global Project Based Learning are paying attention. We suggest education material and theme as the Global Engineering PBL education. So we developed the learning kit of sequence control. That is simple positioning control system which consist of PLC, sensors and some balls. It looks like game board, but that can change difficulty of the problem that the angle is changed and the number of the sensor is changed. Students cooperate to solve the problem that many balls are caught. This will be active learning and be PBL of the Problem Based Learning. We investigate how student's Generic Skill grows by implementation of Global PBL.
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Provision of Thai herbal recommendation based on an ontology
(2010-08-23)
Kato, Takumi
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Maneerat, Noppadol
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Varakulsiripunth, Ruttikorn
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Izumi, Satoru
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Takahashi, Hideyuki
In Thailand, most of people like to use Thai herbs for their traditional medical treatments. However, since there are various sorts of Thai herbs, and the Thai herbal knowledge is complicated, it is difficult to find an appropriate one for each health condition. In order to help people to find suitable Thai herbs to cure the diseases, we have developed a system to provide herbal recommendations to users regarding their symptoms. In the development, we represented the Thai herbal knowledge in an ontology, and extracted new facts based on the ontology. Finally, the system provides appropriate herbal recommendations to users based on extracted facts and the ontology. This paper shows how we represented Thai herbal knowledge in an ontology, and how we extract the new facts based on the ontology, as well as how the system provides herbal recommendations to users. © 2010 IEEE.
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Primary screening of diabetic retinopathy based on integrating morphological operation and support vector machine
(2017-07-02)
Sreng, Syna
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Maneerat, Noppadol
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Isarakorn, Don
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Hamamoto, Kazuhiko
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Panjaphongse, Ronakorn
Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.

Maneerat, Noppadol

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