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 15

Some of the metrics are blocked by your
consent settings
INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
(2026-01-01)
Anuwongpinit, Thanavit
;
Manthawornsiri, Chananont
;
Archevapanich, Tuanjai
;
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.
Some of the metrics are blocked by your
consent settings
DDoS Detection Using a Hybrid CNN–RNN Model Enhanced with Multi-Head Attention for Cloud Infrastructure
(2025-11-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Benjangkaprasert, Chawalit
;
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.
Some of the metrics are blocked by your
consent settings
Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials
(2022-01-01)
Chutchavong, Vanvisa
;
Anuwongpinit, Thanavit
;
Purahong, Boonchana
;
Archevapanich, Tuanjai
;
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.
Some of the metrics are blocked by your
consent settings
Hybrid facial features with application in person identification
(2020-08-14)
Purahong, Boonchana
;
Chutchavong, Vanvisa
;
Aoyama, Hisayuki
;
Pintavirooj, Chuchart
This paper presents the hybrid facial feature with identification and verification based on facial images. A query facial image had been taken under different conditions of the facial image of the same person (as the query). The query facial image database was constructed. We have used the technique of three-dimensional (3D) Dlib facial landmarks using a direct linear transform technique. A set of absolute affine invariance had been constructed from a series of the 3D landmark quadruplets, which make the facial identification robust to affine geometric transformation. These 3D facial features serve as a coarse feature depending on each individual facial structure. The construct of the 2D detail features represents the edge facial image confined between the 2D Dlib landmarks. The similarity of the 2D feature is achieved by aligning the 2D query edge image against that of the reference edge image. The geometric transformation matrix is estimated from the 2D Dlib landmarks, where correspondence is well established. An identification/verification cost function using a combination of local 2D facial features and global 3D facial features is utilized to verify and identify a query facial image against a candidate facial image(s). The performance of the algorithm yielding an area of 99.97% perfect classification is represented as a value under the receiver operating characteristic (ROC) curve.
Some of the metrics are blocked by your
consent settings
Automated Resource Management System Based upon Container Orchestration Tools Comparison
(2023-01-01)
Purahong, B.
;
Sithiyopasakul, J.
;
Sithiyopasakul, P.
;
Lasakul, A.
;
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.
Some of the metrics are blocked by your
consent settings
Rectangular slot antenna with Asymmetrical conductor strip for bandwidth enhancement coverage UWB standard
(2014-01-01)
Archevapanich, Tuanjai
;
Rakluea, Paitoon
;
Anantrasirichai, Noppin
;
Purahong, Boonchana
;
Chutchavong, Vanvisa
This paper presents the bandwidth enhancement of rectangular slot antenna fed by microstrip line on the flexible Mylar Polyester film substrate. Asymmetrical conductor strip is designed and inserted in the rectangular slot to extend the bandwidth from 3.06 GHz to 7.2 GHz frequency range. Besides, the rectangular conductor at the end of the feeding microstrip line is introduced for increasing the bandwidth at high frequency up to 12.32 GHz. This improved antenna to cover standard frequency range of UWB (3.1 GHz – 10.6 GHz). Finally, as to reject the frequency band of IEEE 802.11a (5.15 GHz – 5.35 GHz), the line strip at the side of tuning stub is added. In this case, the band-notched frequency from 5.15GHz to 5.38 GHz can be easily obtained.
Some of the metrics are blocked by your
consent settings
2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters
(2025-01-01)
Krungseanmuang, Woranidtha
;
Morita, Fuka
;
Chaowalittawin, Vasutorn
;
Sathaporn, Posathip
;
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.
Some of the metrics are blocked by your
consent settings
Banana quality classification using lightweight CNN model with microservice integration system
(2025-06-10)
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Sathaporn, Posathip
;
Morita, Fuka
;
Archevapanich, Tuanjai
Banana sorting has been performed manually, which often leads to human error due to the high volume and diverse characteristics involved. This paper presents a banana quality classification system using ConsolutechMobileNetV2 (CST-MobileNetV2) to classify banana ripeness into four categories unripe, ripe, overripe, and rotten. A lightweight deep learning model is proposed and integrated with a uniquely designed microservice system to optimize performance while minimizing computational demands. A publicly available dataset containing 13,478 images was used, and the data split into 56% for training, 14% for validation, and 30% for testing. Image normalization and augmentation techniques were applied to enhance the model's robustness. The model's performance was evaluated using a confusion matrix, achieving 98% precision, recall, and F1-score. The proposed model was compared with other deep learning models to benchmark its performance and deployed in different operating systems to evaluate its flexibility and capabilities. The LINE platform was employed as the user interface, enabling practical interaction with users. The system also demonstrated an average response time of 9.25 seconds per image, ensuring efficient processing, delivers high accuracy and scalability making it a practical and efficient solution for automated banana quality classification.
Some of the metrics are blocked by your
consent settings
Medical Drone Managing System for Automated External Defibrillator Delivery Service
(2022-04-01)
Purahong, Boonchana
;
Anuwongpinit, Thanavit
;
Juhong, Aniwat
;
Kanjanasurat, Isoon
;
Pintaviooj, Chuchart
One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 meters of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID‐19 pandemic.
Some of the metrics are blocked by your
consent settings
Personal identification using a delaunay triangle and optic disc retinal vascular pattern
(2020-01-01)
Kanjanasurat, Isoon
;
Purahong, Boonchana
;
Aoyama, Hisayuki
;
Benjangkaprasert, Chawalit
;
Pintavirooj, Chuchart
Retinal vascular patterns are unique and individual. They provide highly secure and correct identity authentication. In this study, we exploit an image alignment approach based on a geometric invariant, which is the area spanned by feature-point triplets for personal identification. First, we located the optic disc by using a projection of the vascular structure in vascular extraction and extracted feature points that are bifurca- tions of a retinal blood vessel in the vicinity of the optic disc as the landmarks. Delaunay triangulation is then applied to the extracted feature points. The absolute invariant is then derived by taking the ratio of successive triangular area patches. The alignment is achieved by establishing correspondences between feature points after a conformal sort- ing step based on a derived set of absolute affine invariants. The affine transformation parameters can then be calculated by the corresponding vertices of the most robust neigh- bouring triangle of both inquiry and reference images. The optic disc localization results successfully located 95.95% in six widely used retinal image databases. The algorithm of vascular extraction, applied on the DRIVE database, provided an average accuracy of ap- proximately 94.1%. The best accuracy and sensitivity for neighbouring triangle matching obtained were 99.90% and 87.66%, respectively.

Purahong, Boonchana

Filters

Settings

Sort By

Results per page