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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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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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Low-Cost System for Investigating a Small Motor Fault Classification Based on Current Signal
(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 low-cost system for motor fault classification. This system uses a microcontroller with built-in ADC and communication capability. The two purposes of this article are to investigate the quality of the system for data acquisition and capability of the system for detecting early motor faults both by microcontroller on the system and personal computer. The embedded software on the system is designed to record current signals from a current sensor, compute FFT-based features and classify the fault based on tinyML method. Data communication between the system and the personal computer can be done by both serial port and TCP socket over Wi-Fi. The performances of the system and the personal computer are compared by the experiment as well as the quality of data recorded from the built-in ADC and a digital oscilloscope. The broken rotor bar and bearing fault in a 2.2kW induction motor are investigated. The classifier used in the experiment is a small feed forward neural network which can be implemented on both the proposed low-cost system and the personal computer. Although the recorded electrical current data by built-in ADC is contaminated with noise, the fault classification on the personal computer yield accuracy up to 90%.
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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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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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Anomaly Flying Height Prediction Based on Clustering Techniques in Hard Disk Drive Manufacturing
(2025-01-01)
Kanjanapruthipong, Worawit
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Konghuayrob, Poom
In this research, we present a method for predicting anomaly flying height (FH) profiles in hard disk drive (HDD) manufacturing by analyzing FH data at the FH1 stage. Anomalies at FH1 can lead to calibration issues at FH2, disrupting the production process. We propose an AI-based approach using unsupervised clustering techniques to group FH profiles of the read/write head. We evaluated four clustering algorithms, KMeans, MiniBatchKMeans, Birch, and BisectingKMeans, along with the Elbow method to determine the optimal number of clusters. By identifying anomalous FH profiles early at FH1, the method enables proactive intervention, reducing calibration process time and improving production efficiency. Our model achieved an accuracy of 0.939 without relying on manual feature selection (e.g., pressure and temperature), which is often difficult to capture using traditional linear or rule-based models owing to the nonlinear nature of FH profiles. These results demonstrate the practical potential of clustering techniques in enhancing HDD manufacturing processes.
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Optimization of 3D SLAM and Localization for Quadruped Robot Dogs Using Autoencoder-based Techniques
(2025-01-01)
Thamrongveerachart, Chawapon
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Thepsit, Thitipong
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Ningnoi, Paphop
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Konghuayrob, Poom
This paper presents an optimized 3D SLAM and localization framework for quadruped robot dogs using a custom autoencoder-based point cloud compression technique. Our approach combines denoising, voxelization, self-attention, and PointNet modules to reduce the size of the point cloud by more than 80% while removing outliers and maintaining structural clarity. We use a 3D laser-scan matcher for localization, which combines onboard odometry from the Unitree Go2 quadruped robot with LiDAR odometry from a Livox Mid-360 sensor. Our approach maintains accurate pose estimation and considerably reduces processing time by working directly on compressed point clouds. Comparative tests show that the system is appropriate for real-time applications in unstructured contexts due to its enhanced map clarity and decreased localization delay. The proposed framework offers an effective and computationally efficient solution for deploying 3D SLAM in agile legged robots operating in dynamic or hazardous settings.
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Design and Evaluation of a Reliable Automated ECU Reprogramming Framework
(2025-01-01)
Sunthonmani, Phaninthara
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Konghuayrob, Poom
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Suwan-Ngam, Warachart
Reliable reprogramming of Electronic Control Units (ECUs) is vital for safety and functional integrity in modern vehicles. Manual flashing methods remain prone to human errors, inconsistent outcomes, and scalability issues. This research introduces an automated ECU flashing or reprogramming framework using the Vector Toolchain, combining Flash Pack automation with Unified Diagnostic Services (UDS) routines to ensure structured and repeatable processes. Large-scale experiments across multiple ECUs were conducted to assess robustness, error recovery, execution time, and scalability, supported by comprehensive logging and automated reporting for traceability. Experiments on multiple ECUs show reduced errors, faster execution, and improved scalability with success rate more than 80%. The proposed approach enhances reliability and reproducibility in large-scale as well as provides methodological insights and foundation flashing issue analysis for future research.
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Characterization and verification of the optimal feedback gain of a satellite magnetorquer-based attitude control system
(2024-12-01)
Panyalert, Thanayuth
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Manuthasna, Shariff
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Chaisakulsurin, Jormpon
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Masri, Tanawish
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Palee, Kritsada
In spacecraft mission planning and operation, the attitude determination and control subsystem (ADCS) of a satellite provides information about the orientation of the satellite in the inertial reference frame. Furthermore, this subsystem produces the control actions required to adjust the orientation of the satellite, especially in the low-Earth orbit (LEO) regime. This paper focuses on the satellite's three-axis attitude control problem within the context of active and passive control, which includes detumbling control, pointing control, magnetic control, and attitude stabilization after solar panel wing deployment using magnetorquers as the primary actuators. The objective is to stabilize and reduce the angular rate while orienting the satellite to the desired attitude. The proposed satellite attitude control system (ACS) strategies are designed, developed, characterized, and verified. These strategies encompass the B-dot control algorithm for detumbling control along with pointing control and attitude stabilization after solar panel wing deployment. hardware-in-the-loop simulation (HiLs) tests are conducted to assess the performance of the satellite magnetorquer-based ACS in the presence of noise. These tests involve a relative Earth's magnetic field (EMF) generator in conjunction with SGP-4-based satellite orbital propagator high-level control software. Additionally, cascade proportional-integral-derivative (PID) and state-dependent Riccati equation (SDRE) controllers are implemented to generate sufficient torque using three-axis magnetorquers on a frictionless air-bearing platform. The platform is balanced to closely simulate the dynamic motion of a spacecraft in space. The testing includes a single initial condition and three inertia conditions for stabilization after solar panel wing deployment. Finally, the effectiveness of the cosimulation as a primary experiment through an integrated HiLs process is validated. This comprehensive approach confirms the control system's performance and its ability to meet mission requirements.

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