Maneerat, Noppadol

Preferred name

Maneerat, Noppadol

Main Affiliation

School of Engineering

noppadol.ma@kmitl.ac.th

Now showing 1 - 10 of 24

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Banana Plant Nutrient Deficiencies Identification using Deep Learning
(2023-01-01)
Han, Kadipa Aung Myo
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Maneerat, Noppadol
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Sepsirisuk, Kasemsuk
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Hamamoto, Kazuhiko
This paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.
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The Polarization and Depolarization Current Characteristics of Natural Ester Stressed by Partial Discharge
(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
This paper represents the polarization and depolarization current (PDC) of the natural ester stressed by partial discharge (PD). The needle-plane electrode was used to simulate corona discharge in natural ester. Besides, the needle electrode as a high voltage electrode placed on the 8 hours natural ester impregnated pressboards and the plane electrode as a grounded electrode were used to simulate the surface discharge. To simulate the internal discharge, the plane-plane electrode inserted with various condition impregnated pressboards were experimented. PD phenomena were simulated in the natural ester container for 1, 3, and 6 months. Then the PD-stressed natural ester was used as the liquid specimen for polarization and depolarization current measurement. It was found that the PDC characteristics of natural ester undergone with various PD types were obviously different.
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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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Apply PLC to Control 3-Axis Machines
(2025-01-01)
Cheypoca, Thepjit
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Thaiyai, In
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Maneerat, Noppadol
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Narkthewan, Athasart
This report presents a method of using PLC (Programable Logic Control) to control the work instead of the 3-Axis machine position controller. PLC is popularly used in various types of control systems. It has a lot of control capabilities, is efficient, stable, durable, easy to use, and cheap. It is the use of commands to control the work of all 3 axes, 2 axes, and 1 axle. The commands used for moving in both straight lines and curves, and there are other devices that need to be controlled together with the machine that is being controlled.
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Fully Digital Cosine Signal Driven Pulse Oximeter Without Bandpass Filter Based on Microcontroller
(2022-04-20)
Kainan, Pattana
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Wiriyatanachit, Supatsara
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Rungkrae, Manussawee
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Wardkein, Paramote
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Maneerat, Noppadol
In this research, almost fully digital microcontroller pulse oximeter processing by two frequencies of cosine wave-driven red and infrared light sources as well as direct recovering photoplethysmography (PPG) signals from FDM signal without bandpass filter with coherent amplitude demodulation is proposed. An ESP32 microcontroller is employed to generate two cosine waves with two difference equation algorithms instead of using the function of cosine from Arduino or C library language. Two frequency digital cosine wave signals are generated and converted to analog signals with D/A to drive two light sources while two digital cosine signals as mentioned above are recognized in order to use to be as two local oscillator cosine wave signals for recovery of two PPG signals with synchronous demodulation. The RED and IR PPG signals are later used to estimate blood oxygen saturation (SpO2). The experimental result shows that it works well with accuracy and precision of 99.4448% and 0.5551, respectively.
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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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Brain Tumor Classification using Supervised Support Vector Machine
(2023-01-01)
Pilaoon, Pongsak
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Maneerat, Noppadol
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Yajima, Kuniaki
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Varakulsiripunth, Ruttikorn
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Hamamoto, Kazuhiko
A Glioblastoma (GBM) is a malignant brain tumor earlier detection and diagnosis will increase survival opportunities. This research has developed for binary classification of GBM brain tumors by supervised machine learning from magnetic resonance imaging (MRI). DICOM medical images have been converted into JPEG files and morphological operation has been implemented to separate the brain region from the skull image for preparation and easier for tumor segmentation in preprocessing stage. The global thresholding segmentation has been proposed to segment the brain tumor from the artifact and then the features have been extracted by gray level coefficient matrix feature extraction (GLCM). In this research, a support vector machine has been conducted for binary classification and finally, GBM grade-4 brain tumor is distinguished from normal brain images. The dataset comprises 155 MRI images 80% has been assigned for training and another 20% will be the testing dataset. The experimental output prediction result is 96.875 % accuracy, 95 % sensitivity, and 100% specificity. The performance of classification has been improved and shown better results when compared with previous research work.
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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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Linear Interpolation Using Siemens S7-1200
(2025-01-01)
Maneerat, Noppadol
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Wongworrada, Tharathorn
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Cheypoca, Thepjit
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Sepsirisuk, Kasemsuk
This research is a continuation of the research on circular interpolation use siemens s7-1200 [1] It presents the movement of 3 axes, $x y z$, as a method of moving in a curve that is controlled by programmable logic control (PLC). In the future research, PLC will be used to control linear movement straightly. The work will be in the original format, but change the equation used to move in a straight line instead.
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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
;
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.

Maneerat, Noppadol

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