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Author: search.filters.author.Satthamsakul, SuthamStart date: 2020

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    2D-CNN Based Classification on Water Leakage Identification in Automatic Pump
    (2026-01-01)
    Satthamsakul, Sutham
    ;
    Rattanakun, Kritsana
    ;
    Khummongkol, Rojanee
    ;
    Tangsrirat, Worapong
    This study proposes an approach for classification of unusual in automatic water pump systems, especially on a water leakage, one of the key issues that can significantly affect system efficiency and cause serious damage. The method utilizes motor current signals, which are transformed into 2D spectrograms using the Short-Time Fourier Transform (STFT). These spectrograms are then classified by a 2D Convolutional Neural Network (2D-CNN) Designed to determine between usual and unusual operating conditions with high accuracy. Experimental results indicate that the model can effectively detect unusual and leakage events when trained with appropriate parameters, such as a learning rate of 0.001 and 60 training epochs. This model serves as an efficient tool for preventing system failures and reducing maintenance costs in automatic water pump systems.
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    Development of a Capacitive Sensing Module to Detect the Concentration of Isopropyl Alcohol for Covid-19 Disinfection Using Small Capacitance to DC Converter
    (2024-01-01)
    Sriratana, Witsarut
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    Faijaree, Manasnit
    ;
    Sriratana, Lerdlekha
    ;
    Satthamsakul, Sutham
    This paper presents the development of a sensing module for analyzing the concentration of disinfectant alcohol commercially available in Thailand during the Covid-19 epidemic situation to ensure the ingredients and percentage concentration by focusing on simplicity, low cost with reliable performance. Under the basic physical structure of the capacitance change [1]-[5], it consists of a New Sensor Module developed to fully interface with the small capacitance to DC converter for capacitive sensor [2] model. Inside the sensing structure, there were 2 modules of metal conductors. Each conductor was 3 mm. thick, placed in parallel with a fixed distance between the module of 1 mm. The cross-sectional areas of the plates of those two modules were different in order to compare the efficiency of the detector developed. The cross-sectional areas of the plates of Module A and B were 4 cm<sup>2</sup> and 1 cm<sup>2</sup>, respectively. Experiments were performed with 3 concentrations of alcohol, 65%, 70% and 75%, to enhance the concentration detecting performance of the sensing module developed. The signal conditioning was generated from a small capacitance to DC converter for capacitive detection with a frequency of 100 kHz, which was the most suitable frequency for exciting the capacitance change which was the capacitive reactance of the alcohol used in this study. From experiments, it was found that the sensing module with a small cross-sectional conductive material would provide more accurate results in terms of linearity and repeatability. The errors were less than 2.72% compared to the module with a large cross-sectional conductor material which would have an error up to 4.73%.
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    Contamination Measurement Techniques in Hydraulic Fluid Based on Hall Effect Sensor
    (2024-01-01)
    Satthamsakul, Sutham
    ;
    Khummongkol, Rojanee
    This paper proposed two methods to examine metal particles in fluid using Hall Effect Sensor under the condition of nondestructive testing based on two conditions: test of particles in oil samples, and test of particles in industrial machinery simulation. Hall Effect Sensor was a low-cost device had ability to detect magnetic fields in objects, so this work applied Hall Probe to measure the density of metal in liquid. Hereby, hydraulic liquid was selected to examine because it was one of the most use in industrial machines. By using the sensor, two types of experiment were designed: test of oil sample, and test of simulation process, where the results showed that Hall Effect Sensor had ability to measure particles in both two types of experiment. For oil sample test, the sensor was able to detect metallic powder when the number of contaminants were greater than or equal to 10 milligrams (equivalented to NAS 15 standard). On the other hand, to measure particle in simulation process, magnetic field were generated in two ways, i.e. use of electromagnet, and use of permanent magnet. For using electromagnet as magnetic field source, the result showed that temperature had an influence on metal particle evaluation. Therefore, to get the most accurate value, system's temperature had to employed in calculation process. While, the result of using permanent magnet to generate magnetic field showed that the relationship between output voltage and amount of metal particles were probably in linear form. To apply this method in industrial applications could be an inexpensive way to help officer in detecting metal particle in oil which was able to extend service time of hydraulic liquid properly.
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    Fish Recognition Optimization in Various Backgrounds Using Landmarking Technique and YOLOv4
    (2022-01-01)
    Kuswantori, Ari
    ;
    Suesut, Taweepol
    ;
    Tangsrirat, Worapong
    ;
    Satthamsakul, Sutham
    The identification and categorization of fish is a popular and fascinating research topic. Many researchers have developed expertise in fish detection, both underwater and outside the water, which is particularly beneficial for population management and aquaculture. This paper proposes a fish recognition approach using the landmarking methodology with YOLO version 4 to identify and categorize fish with different backdrop circumstances. The approach can be used both underwater and on land. The proposed approach was evaluated using four distinct types of fish from the BYU dataset. The final test result determined that the accuracy reached 96.60%, with an average classification score of 99.67% at the 60% threshold. The result is 4.94 % better than the most frequent traditional labelling approach.
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    Mathematical Model for an Autonomous Vehicle to Fulfill the Closed Area
    (2022-01-01)
    Namngam, Kampanat
    ;
    Chitsakul, Pakkinee
    ;
    Satthamsakul, Sutham
    This research presents the mathematical model to roam an autonomous vehicle in a closed region with minor coincident areas. This model is Blind Algorithm, no literature that concern on mathematical model. It is essential for agricultural technology, for example, shoveling soil for planting, spraying liquid fertilizer, harvesting agricultural products, to control the movement of the vehicle with the redundant path is as less as possible is benefited to decrease the working hours and also the burning oil and more for cost-saving without any expensive hardware. The almost wholly filled farmland depends on the shape, the size of the area, and the vehicle dimension.
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    Analysis of electric current by using MySQL database on web server for machine performance evaluation: A case study of air conditioning system
    (2020-10-13)
    Sriratana, Witsarut
    ;
    Khagwian, Vittaya
    ;
    Satthamsakul, Sutham
    This study developed a performance analyzing system for industrial machines by using an air conditioning system as a case study. Electricity consumption of the air conditioner compressor operating normally under controlled temperature in the room was gathered by applying the principle of quantitative data collection. Work efficiency of the system was analyzed by using the big data pattern. Due to sufficient information, it can further contribute to the application of AI (Artificial Intelligence) in analysis of the failures or causes of machine malfunctions. Data collecting system in this study was designed to analyze electric current and various parameters including power voltage, temperature and humidity, real power and power factor in order to recheck as well as increase the reliability of the system. All collected data were stored in the MySQL database on a web server and managed by phpMyAdmin that could allow users to access the information chronologically on daily, monthly, or yearly basis via a computer screen. The test results showed that the designed system was accurate with a small variation about 0.07 A or 1.78% compared to the standard digital ammeter. For power voltage, an average difference compared to the standard digital voltmeter was approximately 0.4 V or about 0.17%.