Konghuayrob, Poom

Preferred name

Konghuayrob, Poom

Main Affiliation

School of Engineering

poom.ko@kmitl.ac.th

Now showing 1 - 10 of 25

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Comparison of Reduced-Length FFT-Based Feature for Induction Motor Fault Classification
(2025-01-01)
Taweewat, Pat
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Suwan-Ngam, Warachart
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Songsuwankit, Kanoknuch
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Konghuayrob, Poom
This research presents a comparison of FFT-based features which can be used for classifying induction motor faults via neural network. In this paper, the misalignment and rotor bar damage faults are investigated by using stator current as input data only. As the length of the full FFT can include both informative data corresponding to the faults and uninformative data such as noise from environment or electrical supply, only relevant magnitude from FFT bins should be selected and used instead. This paper proposed to use threshold level determined from the magnitude of FFT bins in dataset as a criterion for the selection. From experimental results, an input feature vector created by proposed method can create short input feature vector length to be used by neural network efficiently. The trained neural network performs classification task at 99.98% in accuracy. Comparing to using dimension reduction by PCA, thresholding method needs basic computation, and yields result close to PCA method.
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Mitigating Racial Bias in Skin Lesion Classification With a Novel Deep Learning-Driven Dataset
(2025-01-01)
Joshi, Priyanka
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Ruangrit, Chanet
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Konghuayrob, Poom
(1) Background: A critical issue in the application of Machine Learning (ML) in dermatology is the presence of racial bias in training datasets, which can lead to disparities in diagnostic performance across different skin tones. This paper proposes a mitigation strategy for the BCN20000 dataset, addressing its limitations regarding darker skin lesions by augmenting it with a custom dataset. (2) Method: This was achieved by augmenting the BCN20000 dataset with a custom-developed dataset of dark skin lesion images, generated by combining serial style transfer and latent diffusion-based upscaling. The impact of this augmented dataset was assessed on the accuracy of several established Convolutional Neural Network (CNN) architectures, including DenseNet, ConvNeXt, EfficientNet, RegNet and ResNet. (3) Results: Our findings underscore the necessity of diverse datasets for AI-driven dermatology tools with the best performing model achieving 92% accuracy when trained on the augmented dataset containing additional dark-skin images compared to baseline models trained solely on BCN20000. These results align with prior studies emphasizing the critical role of representative training data in mitigating racial bias in medical AI systems.
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Autonomous Rescue Hexapod Robot with AI Human Detection and Tracking
(2023-01-01)
Saengsint, Chissanupong
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Juntacheevakul, Kidtipod
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Chalermkitpaisan, Kirawut
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Suthapintu, Napak
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Tomaneenilrat, Pavares
The occurrence of catastrophic caused tons of thousands of lives. To enhance the efficacy of search and rescue operations in challenging to access region, we have introduced an autonomous hexapod robot equipped with a partial human detection and tracking system that is capable to identify fragments of the human body. The chosen hexagonal arrangement of six legs enables the robot to navigate through rough terrains. This configuration provides stable mobility, with an average acceleration of 0.8 m/s<sup>2</sup> and a peak acceleration of 3.1 m/s<sup>2</sup> during walking, making the camera of the robot remains steady for human identification. Due to the employed DeeplabV3+ architecture for the training process, partial human bodily structure detection has achieved 94% accuracy. Moreover, the robot can track the detected individual by maintaining 1m distance.
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The Development of Adaptive Gray Level Mapping Combined Partical Wwarm Optimization for Measuring the Dimeter Size of Automotive Nut
(2020-04-01)
Pondech, Wichai
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Saenthon, Anakkapon
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Konghuayrob, Poom
In line assembly process, it is necessary to use the accurate tools to inspect the work piece. In order to measure the width, thickness or depth of the nut used in the commercial car, previously in Thai Steel Cable Company used the caliper to measure size of the automotive nut operated by human as an sample test inspection. Even the result on this technique is high accuracy, however; there are some disadvantages based on this method such as human error, idle time and also fatigue by the human. Therefore, this research focus on the development of the new measurement technique that utilized industrial camera together with the image processing algorithm to measure the entire nut with 100% inspection test. Circular Hough Transform (CHT) is applied to be the basic concept used for finding the circle position and measuring nut diameter. Although the CHT technique can measure diameter of the interested nut, but the result's accuracy is not acceptable due to measuring error from the various range of light condition. This research proposed the new technique, Adaptive Gray Level Mapping algorithm (AGLM) to increase the quality of the input picture before measuring the radius by CHT technique. Moreover, the beta in AGLM is optimized by particle swarm optimization (PSO) technique that applies 50% of nut data and other is used for validate. The results show the effectiveness of the proposed AGLM combined PSO that increase the accuracy of the visual measuring method via compare to the conventional threshold with CHT technique.
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Optimizing Wafer Classification in Industrial Manufacturing Using Particle Swarm Optimization and Deep Learning
(2025-01-01)
Suwannoot, Pisit
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Konghuayrob, Poom
In this study, we examine the application of convolutional neural networks (CNNs) for wafer pattern classification, with a focus on enhancing training efficiency and model performance. To achieve this, particle swarm optimization (PSO) is employed to improve the model performance while reducing its complexity, a critical factor in production environments. By minimizing the number of layers, the proposed method accelerates training, reduces resource consumption, and enhances defect detection accuracy. Wafer failure patterns are classified into four categories: vertical, rectangular, edge, and horizontal. The approach achieves an impressive F1-score of 0.988, significantly surpassing the traditional CNN’s score of 0.83. By integrating PSO, the method considerably improves the visual inspection process for hard disk drives, contributing to high-quality production. This optimization not only streamlines workflows but also enables manufacturers to address issues more rapidly, aligning with Industry 4.0’s objectives of automation and intelligent monitoring.
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Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line
(2023-01-01)
Yanyong, Sarucha
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Parichatprecha, Rattapoohm
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
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Konghuayrob, Poom
In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard.
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Two-Tier AI Surveillance: Enhancing Weapon Detection Through Edge and Cloud Collaboration
(2026-01-01)
Sun, Sanchai
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Songsuwankit, Kanoknuch
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Thamrongrongveerachart, Chawapon
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Yanyong, Sarucha
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Sri-on, Jiramate
This research presents a novel two-tier AI-based surveillance system designed for real-time weapon detection to enhance security and prevent potential robberies. The system leverages the computational capabilities of both edge and cloud resources, integrating a YOLOv5s model on a Jetson Nano for initial detection and a YOLOv8l model on a server equipped with an Nvidia A100 for refined analysis. The initial detection performed by the Jetson Nano rapidly identifies potential threats and forwards compressed images to the server, optimizing bandwidth usage and transmission speed. Upon receipt, the server applies image enhancement techniques to restore and upscale the images from 640 pixels back to 1280 pixels before further verification. Utilizing Python Django, the server processes the enhanced images with a more sophisticated model to ensure high accuracy in detection. The integration of edge computing optimizes the system’s performance by enabling real-time processing and reducing latency. This hybrid approach enhances the efficiency and scalability of the surveillance system, ensuring robust and timely detection. Upon confirming the presence of a weapon, the system sends immediate alerts via LINE Notify to both users and local law enforcement, thereby enabling prompt responses to potential security threats. Additionally, the Jetson Nano hosts a Flask application, allowing users to download previously recorded videos for further review and evidence collection. The proposed solution combines edge computing, cloud processing, and image optimization techniques to provide an efficient, scalable, and high-performance solution for real-time weapon detection which enhancing accuracy and reliability in real-world surveillance.
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The Cattle Rut Behavior Detection Base on AI Deep Neural Network
(2022-01-01)
Phaechaiyaphum, Phatsarut
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Konghuayrob, Poom
This paper proposes an application model of camera object detection to observe the cattle rut through its pose. This research is based on deep neural network method along with transfer learning technique. This paper employs four different deep convolutional neural network architectures 1) SSD Mobile-Net V2 2) SSD ResNet 101 V1 FPN 3) YOLOv5s and 4) Mask R-CNN. All the network mentioned above are pre-trained on COCO dataset. To observe the cattle rut behavior, the images of cattle behavior were taken in natural open environment, the data obtained are split into training and testing datasets. In this research cattle behaviors are categorized mainly in two classes, (1) normal behavior and (2) the cattle rut behavior. The result obtained after training the models shows that the YOLOv5s model obtained the highest mean average precision which is 95.5% and least training time. Thus, this paper proposes that YOLOv5s model can be applied to detect the cattle behavior for precise artificial breeding in order to increase the cattle population in farm to serve higher consumption demand in the near future.
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Decentralised and Centralised Fixed Structure H∞ Robust Loop Shaping for the MIMO Microsurgical Manipulation Based on PSOGSA
(2026-01-01)
Kaitwanidvilai, Somyot
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Konghuayrob, Poom
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Aoyama, Hisayuki
This paper presents decentralised and centralised fixed-structure H∞ robust control methods optimised by the Particle Swarm Optimisation and Gravitational Search Algorithm (PSOGSA) for a coupled multi-input multi-output (MIMO) microsurgical manipulator. The design framework explicitly considers uncertainties and disturbance constraints. Conventional H∞ loop-shaping controllers are typically of high order, complex, and difficult to implement in practice. To address this limitation, Proportional-Integral-Derivative (PID)-structured decentralised and centralised H∞ controllers are proposed, providing compact structures while retaining robustness. The novelty of this work lies in embedding H∞ robustness criteria into practical PID-based frameworks, bridging the gap between theoretical robust design and experimental implementation in microsurgical applications. The proposed controllers are evaluated against a reduced-order H∞ controller derived from Hankel norm approximation and a Ziegler–Nichols tuned PID controller, using both simulation and experimental studies. Results demonstrate that the proposed controllers achieve improved stability margins (0.449–0.521 compared with 0.436 for the reduced-order design), maintain low root-mean-square errors (≈0.067–0.089) and remain robust under voltage disturbances where conventional PID control fails. These findings confirm the contribution of a practical and efficient robust control strategy for enhancing the precision and reliability of microsurgical manipulators.
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Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
(2023-01-01)
Yanyong, Sarucha
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Konghuayrob, Poom
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Chaisiri, Punyavee
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Kaitwanidvilai, Somyot
The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.

Konghuayrob, Poom

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