Purahong, Boonchana

Preferred name

Purahong, Boonchana

Alternative Name

Purahong, B.

Main Affiliation

School of Engineering

boonchana.pu@kmitl.ac.th

Now showing 1 - 10 of 20

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Student development towards innovation in the information engineering curriculum of faculty of engineering, KMITL
(2020-11-04)
Archevapanich, Tuanjai
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Sithiyopasakul, Jiran
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Purahong, Boonchana
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Sithiyopasakul, Jirayus
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Sithiyopasakul, Paisan
This article presents the student development towards creative works of innovation in the Information Engineering curriculum of the Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang by using the theory of cognition in wisdom, skill range, and mental range. Enlighten students have knowledge and skills in creating innovative inventions. The satisfaction survey results in the integration of teaching and learning. The criteria were at a high level (4.03), which is 80.76 percent and the achievement of students' creative innovation works is at a high level (4.16), representing 83.31 percent.
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Automated resource management system based on kubernetes technology
(2021-05-19)
Sithiyopasakul, Jirayus
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Archevapanich, Tuanjai
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Purahong, Boonchana
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Sithiyopasakul, Paisan
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Benjangkaprasert, Chawalit
The purpose of this research is intended to study and analyze the Kubernetes technology using the processes of performance evaluation by inspecting how many requests and responses could the server handle. According to the ability of Kubernetes technology, there is no systematic measure of performance despite the Kubernetes is currently in use broadly. Therefore, this paper proposed to test out the system by measuring the effectiveness according to a structured process by studying such measurement variables including the number of requests per second, number of responses to requests, and resource extension period with Kubernetes technology. Which from the testing and analysis of all three variables as mentioned, it is possible to know the efficiency of the Kubernetes technology in such a similar environment. Moreover, the testing experiment could display a piece of information on the dashboard for visualization and analytic purposes.
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INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
(2026-01-01)
Anuwongpinit, Thanavit
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Manthawornsiri, Chananont
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Archevapanich, Tuanjai
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Purahong, Boonchana
In this article, we propose developing digital innovation of smart monitoring systems in the poultry farm using the Internet of Things (IoT) technology. This work aimed to design and develop a monitoring system based on an IoT system that transforms a traditional farm that uses a manual management system to apply an IoT system for environmental monitoring in a poultry farm. The main components include a hardware component that was designed and implemented to gather data of the poultry houses. Temperature and humidity sensor nodes are applied to monitoring the environment of poultry houses. The LoRa communication module in the sensor node will forward data to the gateway. The second principal component is the cloud server for data acquisition from the gateway. The cloud will be responsible for back-end processing and a web-based dashboard displaying mechanism. This system can work as an alarm notification system using LINE notify API for the LINE application that is the most popular communication application in Thailand. The system was implemented practically in one of the poultry farms in Prachinburi province, Thailand. The results indicate that the proposed system provides significant advantages, including enhanced monitoring accuracy, reduced energy consumption, and improved real-time environmental tracking for poultry farms.
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Information Engineering Course Management in Faculty of Engineering, KMITL towards excellence
(2021-01-01)
Purahong, Punyaruethai
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Archevapanich, Tuanjai
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Chaowalittawin, Vasutorn
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Punthang, Donnaporn
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Millerteacher, Gregory P.
This article represents the management of Information Engineering course in faculty of Engineering, KMITL towards excellence. Sample group is the parental units of who registered since 2017 until 2020 for 15 people, by purposive sampling. The researcher later presenting the Information Engineering course management. Then make a parental satisfaction assessment form about the Information Engineering course management in faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, towards excellence of 7 subject. The conclude of assessment says that the parental units are having a good level of satisfaction and the arithmetic mean is 4.72 and the standard deviation is 0.56 (\overline{X} =4.27, S.D. = 0.56)
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Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials
(2022-01-01)
Chutchavong, Vanvisa
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Anuwongpinit, Thanavit
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Purahong, Boonchana
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Archevapanich, Tuanjai
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Janchitrapongvej, Kanok
The ultrawideband system operates a very short pulse with enormous bandwidth to provide high data rates for data transmission. To design the UWB pulse, considering the pulse shape is very necessary, and a spectral emission mask of the designed pulse should meet the FCC spectral mask requirement between frequency range 3.1 GHz to 10.6 GHz. The traditional UWB pulse design is based on the Gaussian derivative. However, the frequency spectrum is not satisfied the FCC spectral mask requirement. In this study, the Gaussian pulse can be designed from the mathematical characteristic of the generalized Bessel polynomial. The spectral efficiency of the proposed pulse can be improved by the combination of the derivative of Gaussian pulse with a weight coefficient optimization with particle swarm optimization (PSO). PSO is a population-based optimization algorithm inspired by animal behavior. PSO is applied with generalized Bessel polynomial transfer function to gain the best weight coefficient, we proposed to optimize its weight vector to design a pulse that exceeds to FCC spectral mask. The results were found in MATLAB software show that generalized Bessel polynomials can approximate the proposed pulse with combination method and PSO. The spectral efficiency is improved to 89.30% and the spectrum is greater close to the FCC spectral mask requirement. To confirm an improved spectral efficiency compared to the previous works.
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Learning Process during the COVID-19 Crisis, Selected Topics in Telecommunication Engineering subject, Department of Electronics and Telecommunication Engineering, RUS, Thailand
(2021-01-01)
Archevapanich, Tuanjai
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Khunthawiwone, Park Poom
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Sithiyopasakul, Jiran
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Anuwongpinit, Thanavit
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Purahong, Boonchana
This article presents a learning process during the COVID-19 crisis, used in selected topics of telecommunication engineering subject, department of Electronics and Telecommunication Engineering, Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi. The sample consisted of 19 students, enrolled in the selected topics of telecommunication engineering course by selecting a specific sample group. The researcher presented a learning process during the COVID-19 crisis with the orientation of students for the same understanding and then teaching according to the given process. Finally, there are three aspects of satisfaction with learning processs during the COVID-19crisis assessed. The results showed that in all of the three aspects, having a very good level of students' satisfaction with an arithmetic mean of 4.614 and a standard deviation of 0.46 (\overline{X}=4.614, S.D. = 0.46)
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Case study: To identify the students that participating in the cheer activity of the faculty of engineering, KMITL by using near field wireless communication
(2020-11-04)
Sithiyopasakul, Paisan
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Purahong, Boonchana
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Sithiyopasakul, Saran
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Archevapanich, Tuanjai
This article presents wireless communication technology with NFC. In this case study, it is about using the NFC to identify the freshmen one by one that participating in the Cheer activity of the Faculty of Engineering, KMITL of the first-year students of 800 students, duration ten days. From the experiment results, when using the application, it can be used to check the names of students who have participated in the activity with 100 percent accuracy.
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2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters
(2025-01-01)
Krungseanmuang, Woranidtha
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Morita, Fuka
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Chaowalittawin, Vasutorn
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Sathaporn, Posathip
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Kanamori, Chisato
Global climate change impacts all regions and leads to natural disasters such as typhoons, which cause destruction, debris, and flooding. Postdisaster restoration is a very important activity that is mostly done manually and can be time-consuming and challenging, especially in subterranean environments owing to accumulated objects such as pipes, pillars, and mud distributed in confined underground areas. Therefore, in this study, we aim to utilize emerging AI technologies by comparing deep learning algorithms and evaluating four models for 2D object detection and four for 3D point cloud segmentation for detecting sediment accumulation and navigating around obstacles in underground areas after a disaster. Additionally, a custom dataset was developed to simulate underground disaster scenarios. As a result, the You Only Look Once version 11 (YOLOv11) model achieved the highest mean average precision 50 (mAP50: 91.1%) for general detection within the pillar-pipe dataset, whereas the YOLOv12 model performed the best in detecting pipes (mAP50: 87.7%). In the mud dataset, the YOLOv8 segmentation (YOLOv8-seg) model demonstrated superior performance with mAP50 scores of 93.0% (detection) and 86.4% (segmentation). For 3D point cloud segmentation, PointNet achieved the highest accuracy (98.61%), whereas RandLA-Net was optimal for pipe segmentation, achieving an intersection over union score of 37.1%. These findings highlight AI’s potential to accelerate disaster recovery, reduce manual labor, and ensure faster cleanup. Integrating deep learning models into post-typhoon restoration efforts can enable communities to recover more quickly and efficiently after climate change impacts or disaster events.
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Hybrid learning during the COVID-19 pandemic of engineering students at KMITL, Thailand
(2021-01-01)
Purahong, Boonchana
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Sithiyopasakul, Saran
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Sithiyopasakul, Paisan
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Anuwongpinit, Thanavit
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Archevapanich, Tuanjai
This article presents the hybrid learning model during the COVID-19 for students of Agro-Industrial Systems Engineering who enrolled in the course embedded system, course code 01386304, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang for 29 students by selecting a specific sample group. The researcher presented a hybrid learning model during the COVID-19, where students were orientated for the same understanding and followed by teaching as active learning formed project-based learning. Finally, we assessed five aspects of hybrid learning satisfaction during the COVID-19. The evaluation results revealed that the students' satisfaction was at a good level and their arithmetic mean was 4.206 and the standard deviation is equal to 0.74 (X = 4.206, S.D. = 0.74)
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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
(2024-01-01)
Purahong, Boonchana
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Krungseanmuang, Woranidtha
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Tenghongsakul, Kasi
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Archevapanich, Tuanjai
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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.

Purahong, Boonchana

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