Chompoo_Inwai, Chow

Preferred name

Chompoo_Inwai, Chow

Alternative Name

Chompoo-Inwai, Chow

Chompoo-inwai, Chow

Chompooinwai, Chow

Chompoo-inwai, C.

Chompoo-Inwai, Chai Chow

Chompoo-Inwai, Chow Chai

Main Affiliation

School of Engineering

chow.ch@kmitl.ac.th

Now showing 1 - 10 of 10

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Research of Magnetic and Electric Fields to Design and Develop the Prototype of Magnetic Resonance Imaging Device
(2025-01-01)
Chompoo-Inwai, Chai
;
Chompoo-Inwai, Chow
;
Suksathit, Witchayaphorn
;
Kaewpromrat, Weerapat
;
Maneerat, Saranyu
This research presents a study and preliminary design of a main magnetic field generation system for an MRI machine using simulation software. The objective is to investigate the factors affecting magnetic field strength. Six coil models were preliminarily designed and simulated to analyze the magnetic field behavior. Simulation results were compared with experimental magnetic field measurements, showing that the software accurately reproduces the magnetic field. It was found that distributed winding improves field uniformity, and that both the number of coil turns and conductor size directly affect the strength of the main magnetic field in the air core.Based on the simulation outcomes, a prototype magnetic field generation system was designed. The proposed prototype consists of an aluminum core with a diameter of 110 mm and a height of 335 mm, suitable for arm imaging. The system uses a superconducting NbTi wire wound 6,516 turns in eight segments, with a current of 120 A. Cooling is achieved using a cryocooler model RDE-418D4 4K, which can reach temperatures as low as 4.2 K. This study provides essential guidance for the development of main magnetic field systems in future MRI devices.
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Implementation of a Face Detection Surveillance Security System in a Rice Farm Using Supervised Machine Learning Techniques
(2025-01-01)
Polset, Siriwan
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Chompoo-inwai, Chow
This paper presents an innovative approach to developing a surveillance security system tailored for the agricultural sector, specifically rice paddy fields in Thailand, by employing supervised machine-learning-based face detection techniques. The proposed system integrates real-time facial recognition with automated notifications sent to the owner via text messages and facial images through the LINE application. Video inputs from on-site CCTV cameras are processed into a sequence of images, from which key facial features are extracted using the Haar-like feature extraction method. Identification of individuals as either authorized owners or intruders is achieved through supervised machine learning utilizing the K-Nearest Neighbors (K-NN) algorithm. Upon detecting an intruder, the system promptly activates an alarm and sends notifications, enhancing real-time security monitoring. The system is implemented using a Raspberry Pi microprocessor and is powered by a stand-alone solar energy system, ensuring sustainability and operational efficiency. Performance evaluation includes extensive testing, verification, and comparison against conventional methods using the widely recognized Labeled Faces in the Wild (LFW) dataset. Experimental results demonstrate that the proposed system achieves an average accuracy of 86.86%, slightly surpassing traditional techniques. Additionally, it exhibits a significantly improved recognition speed, averaging 8.3 seconds per detection. Robustness and adaptability were further assessed by evaluating the system under varying brightness and distance conditions. The findings confirm the system's ability to provide real-time, precise facial recognition of intruders, thereby establishing its effectiveness as an advanced surveillance security solution. The system's operational boundaries for successful intruder recognition and warning alerts are set within a 1 to 3-meter range from the camera. Recognition accuracy was observed at 95% under optimal conditions and 83% in more challenging scenarios, while the warning alert system demonstrated a success rate between 86% and 97%. These results highlight the proposed system's superior performance and reliability, making it a valuable security solution for agricultural applications.
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Fast laser Quantity Measurement of Pest Box Imaging Using End-to-End Real Time in Thai Rice Farm
(2021-01-01)
Polset, Siriwan
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Chompoo-Inwai, Chow
In this paper, the researcher development a fast laser quantity measurement of pest box imaging using end-to-end real time in Thai rice farm. Usage of a small, light, low-cost, high precision laser raining sensor for detected pests in rice farm and send number data to ESP32-CAM then it will take a pest box picture send to user application LINE. The processing can save statistical data from laser raining sensor on memory card of ESP32-CAM for quantity pests analysis. The researchers had designed pest measure box two sets and install in rice farm an area of 3.2 acre. The experiment is quantity measurement of pest box in the real environment of rice grow season in Thailand. Measurement were for 3 different period of time: rice seedlings to tillering stage (30 - 45 days old), rice at full tillering stage, pregnancy-to-earth rice. The results show a lot of pest the before harvest season is pregnancy to earth rice.
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Novel Techniques for Critical Load Buses Identification and Load Bus's Available Capacity Calculation to Improve Power System Stability
(2020-07-01)
Wannoi, Narumon
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Jirsuwankul, Nirudh
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Chompoo-Inwai, Chow
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Chompoo-Inwai, Chai
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Wannoi, Chaisit
this paper presents two novel techniques for critical load buses identification and load bus's available capacity (LBAC) calculation to improve power system stability. The critical load bus identification is applied by contingency analysis considering system violation limits under load shedding impact. The violation limits consist of two limits including voltage and percent loading limits of power transfer equipment. As the LBAC calculation, the available transfer capability (ATC) of all multi-lines connected on each load bus are investigated with the total transfer capability (TTC). To validate the effectiveness of the proposed approach, the modified Thailand power system during summer peak load in areal (load center) is used for a system base case. As the results, it is found that the proposed techniques can effectively obtain and rank critical load buses as well as to calculate the LABC. The strong relation between these two indexes can be applied to improve power system stability.
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ANC System Modeling and Simulations in a Wide-Area Virtual Environment Using FDTD Approach with Perfectly Matched Layer Absorbing Boundaries
(2021-12-01)
Sookpuwong, Chadaporn
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Chompoo-inwai, Chow
This paper presents a model and simulation results of the active noise control (ANC) system in a wide-area virtual environment using the acoustics finite-difference time-domain (FDTD) propagation approach defined by particle velocities and sound pressure level (SPL) within a certain absorbing boundary conditions called a perfectlymatched layer (PML) technique. The ANC excitation applies a single-frequency noise source with an adaptive feedforward configuration. The FDTD algorithm is used to model the area of interest acoustically, including a desired quiet zone, considering the effects from a primary path, a secondary path, and a feedback path. A processing unit of the ANC system based on the least mean-squared (LMS) algorithm is utilized to synthesize a cancelling noise using a secondary loudspeaker. A single-channel feedforward ANC system used in this paper is modified from the proposed multi-channel models and forms. Acoustics FDTD propagation results can be used to determine the optimum placements for ANC sensors and actuators. The SPL numerical and graphical results are plotted to demonstrate the performance of the proposed ANC system. All of the simulation results confirm that the proposed modeling approach can be combined with wide-area acoustic simulations and a feedforward LMS adaptive algorithm. The proposed model also provides a way for the optimum placement of the ANC sensors and actuators before being used in a more-complex practical environment
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Optimum Generated Power for a Hybrid DG/PV/Battery Radial Network Using Meta-Heuristic Algorithms Based DG Allocation
(2023-07-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-inwai, Chow
This paper presents four optimization outcomes for a diesel generator (DG), photovoltaic (PV), and battery hybrid generating radial system, to reduce the network losses and achieve optimum generated power with minimum costs. The effectiveness of the four utilized meta-heuristic algorithms in this paper (firefly algorithm, particle swarm optimization, genetic algorithm, and surrogate optimization) was compared, considering factors such as Cost of Energy (COE), the Loss of Power Supply Probability (LPSP), and the coefficient of determination (R<sup>2</sup>). The multi-objective function approach was adopted to find the optimal DG allocation sizing and location using the four utilized algorithms separately to achieve the optimal solution. The forward-backward sweep method (FBSM) was employed in this research to compute the network’s power flow. Based on the computed outcomes of the algorithms, the inclusion of an additional 300 kW DG in bus 2 was concluded to be an effective strategy for optimizing the system, resulting in maximizing the generated power with minimum network losses and costs. Results reveal that DG allocation using the firefly algorithm outperforms the other three algorithms, reducing the burden on the main DG and batteries by 30.48% and 19.24%, respectively. This research presents an optimization of an existing electricity network case study located on Tomia Island, Southeast Sulawesi, Indonesia.
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Advanced Design and Implementation of a 2-Channel, Multi-Functional Therapeutic Electrical Stimulator
(2024-10-01)
Lakatem, Rujira
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Boontaklang, Suttipong
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Chompoo-inwai, Chow
This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable of producing twelve essential current waveforms with fully adjustable parameters, including pulse amplitude, pulse duration, and pulse repetitive frequency. The proposed driving stage unit ensures precise voltage-to-current conversion, delivering stable and accurate output currents even under varying load conditions, which effectively simulate the diverse impedance characteristics of human tissue. Extensive testing confirmed the compliance with international medical standards, notably IEC 60601-1, IEC 60601-1-2, and IEC 60601-2-10. The experimental results underscore the device’s consistent operation within prescribed safety and performance thresholds, with all deviations in pulse parameters remaining well below the permissible limits. Furthermore, the proposed electrical stimulator demonstrated exceptional stability across variable load conditions, as evidenced by minimal amplitude errors and high correlation between waveform characteristics. These findings highlight the proposed device’s robustness and its potential as a versatile tool for a wide range of therapeutic applications, including pain management, muscle stimulation, and nerve rehabilitation, thus marking a significant advancement in the field of therapeutic electrical stimulation.
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Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Genetic Algorithm and Particle Swarm Optimization
(2022-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
Hybrid generation systems took the attention of many researchers searching for the best energy source that can be optimum, reliable, and scalable instead of dependency on the traditional fossil fuels sources; researchers are developing Artificial Intelligence (AI) algorithms to optimize those systems. This paper will study the optimum generating power from a Wind turbine, PV, and Battery linked to the radial network in Tomia Island, considering the optimum cost using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
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Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Firefly Algorithm and Surrogate Optimization
(2022-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
Exploiting renewable energy resources can help decrease carbon emissions by providing a reliable solution to generate electricity and tackle the climate change dilemma. Artificial Intelligence algorithms have been used in the last two decades to optimize power system networks. In this paper, we discussed the effect of replacing the existing Diesel Generator (DG) with a wind turbine to satisfy the load in the standalone hybrid (DG, PV, Battery) radial network in Tomia Island, south-east Sulawesi, Indonesia. Loss of Power Supply Probability (LPSP) and the Coefficient of determination parameters were used as technical performance indicators. Firefly Algorithm (FF) and Surrogate Optimization technique were used to optimize the system considering the minimum costs.
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Optimum Generated Power with the Minimum Cost of a Radial Network using Firefly and Genetic Algorithms
(2021-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
This paper presented two artificial intelligence methods to find the optimum output power from Diesel generator (DG), Photovoltaic system (PV) and batteries to satisfy the load with the minimum cost considering the minimum losses. Our case study was a micro-gird 20 kV radial network consists of 21 busses located in Tomia island, south-east Sulawesi Island, Indonesia. Firefly algorithm (FA) and Genetic algorithm (GA) used in this study to do the optimization and chose the optimum operation. Forward-Backward sweep method used for the power flow calculations.

Chompoo_Inwai, Chow

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