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 31

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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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IoT-based Water Quality Monitoring Station and Forecasting System with Machine Learning
(2025-01-01)
Jomjaiekachorn, Thanart
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Anuwongpinit, Thanavit
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Purahong, Boonchana
This paper presents an IoT-based water quality monitoring and forecasting system designed for real-time and continuous assessment of water resources. The system integrates Siemens SIMATIC IOT2050 as an Industrial IoT Gateway, which collects data from sensors measuring conductivity, pH, dissolved oxygen, and temperature using RS485 Modbus RTU communication. Data processing occurs at the edge using Node-RED and is transmitted to AWS Cloud via MQTT for storage and visualization on a dashboard. Predictive analysis employs machine learning models, including XGBoost with Optuna parameter tuning and Long Short-Term Memory (LSTM) networks, for water quality forecasting. Results indicate superior performance of LSTM for most parameters, while XGBoost excels in pH prediction. This system demonstrates scalability, reliability, and potential for enhanced water quality management in diverse environments.
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DDoS Detection Using a Hybrid CNN–RNN Model Enhanced with Multi-Head Attention for Cloud Infrastructure
(2025-11-01)
Sathaporn, Posathip
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Benjangkaprasert, Chawalit
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Purahong, Boonchana
Cloud infrastructure supports modern services across different sectors, such as business, education, lifestyle, government and so on. With the high demand for cloud computing, the security of network communication is also an important consideration. Distributed denial-of-service (DDoS) attacks pose a significant threat. Therefore, detection and mitigation are critically important for reliable operation of cloud-based systems. Intrusion detection systems (IDS) play a vital role in detecting and preventing attacks to avoid damage to reliability. This article presents DDoS detection using a convolutional neural network (CNN) and recurrent neural network (RNN) model enhancement with a multi-head attention mechanism for cloud infrastructure protection enhances the contextual relevance and accuracy of the DDoS detection. Preprocessing techniques were applied to optimize model performance, such as information gained to identify important features, normalization, and synthetic minority oversampling technique (SMOTE) to address class imbalance issues. The results were evaluated using confusion metrics. Based on the performance indicators, our proposed method achieves an accuracy of 97.78%, precision of 98.66%, recall of 94.53%, and F1-score of 96.49%. The hybrid model with multi-head attention achieved the best results among the other deep learning models. The model parameter size was moderately lightweight at 413,057 parameters with an inference time in a cloud environment of less than 6 milliseconds, making it suitable for application to cloud infrastructure.
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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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Comparison of Support Vector Machine for Apron Allocation
(2022-05-27)
Kanjanasurat1, Isoon
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Purahong, Boonchana
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Teerapanpong, Saowaluk
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Benjangkaprasert, Chawalit
This paper presents machine learning techniques for classifying parking stand locations in the apron allocation management service that affects total airport ground service processing time at airports where arriving aircraft land. SVM and Kernel SVM algorithms will be used, as well as Polynomial, Gaussian RBF, and Sigmoid, based on five input factors: aircraft identification, estimated time of arrival (ETOA), area of apron, type of aircraft, and target of stands. Then, we compared classification accuracy and performance using the Mean Absolute Error (MAE) and the squared mean error (Root Mean Square Error: RMSE), and discovered that the Gaussian RBF kernel of the SVM algorithm model is more accurate than the other model. This work may be beneficial in assisting airport's decision-makers and enhancing airport operations efficiency and predictability.
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Inventory Management System based on IoT and Microservices Architecture Design
(2023-01-01)
Sithiyopasakul, Paisan
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Piyatananugoon, Chavinkorn
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Chaowalittawin, Vasutorn
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Krungseanmuang, Woranidtha
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Sathaporn, Posathip
This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second.
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Landing Runway Assignment by Airport Traffic using Machine Learning
(2022-05-27)
Kanjanasurat1, Isoon
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Jungsuwadee, Wasarut
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Purahong, Boonchana
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Benjangkaprasert, Chawalit
This paper presents the solutions to the overwhelming burden of air traffic controllers by reducing workload and optimizing runway capacity using machine learning tools to assign runways for incoming aircraft based on critical information such as aerodrome traffic information of aircraft taking off and landing on the runway at Suvarnabhumi Airport, THAILAND. The model is composed of four layers and three hidden layers. ReLU and Adam are the activation and optimization functions used in this model, respectively. The model was trained using assigned landing runway and traffic runway factors. Predicting the assigned runway is 82.77 percent accurate.
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Automated Resource Management System Based upon Container Orchestration Tools Comparison
(2023-01-01)
Purahong, B.
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Sithiyopasakul, J.
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Sithiyopasakul, P.
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Lasakul, A.
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Benjangkaprasert, C.
The goal of this article is to study and analyze the container orchestration technology Kubernetes, Docker Swarm, and Apache Mesos by performing performance evaluations and inspecting how many requests and responses the server can handle. Due to the fact that managing information system resources is a challenge in terms of performance, usability, reliability, and the cost of information resources. Some orchestration tools cannot automatically allocate resources depending on the scope of the information system resource management. This leads to allocating resources more than the needs of system requirements, resulting in excessive costs. Therefore, this article proposed testing the system by measuring its effectiveness using a structured process by examining measurement variables such as the number of requests per second, number of responses to requests, and resource extension period using all three-orchestration technology. 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 and compared it with other orchestration tools like Docker Swarm and Apache Mesos orchestrator. For Kubernetes, Docker Swarm, and Apache Mesos, the mean value of its handling average request per minute is 30,677.25/min, 33,688.67/min, and 29,682.6/min, respectively. Swarm performed better in aspects of handling requests per minute by 9.35% of the difference when compared to Kubernetes and by 12.64% when compared to Apache Mesos. However, there are several things which should be taken into consideration because each orchestration tool has its own strong and weak points. The testing experiment could display a piece of information on the dashboard for visualization and analytic purposes and there is an elaboration at the end of when to use which container orchestration tool to suit the business proposes the most.
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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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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application
(2024-01-01)
Archevapanich, Tuaniai
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Sathaporn, Posathip
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Chaowalittawin, Punyisa
This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

Purahong, Boonchana

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