Article Per Year (5 Year)
p-Index From 2021 - 2026
1.066
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science Journal of Engineering and Technological Sciences
Nguyen, Chi-Ngon
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
On-device training of artificial intelligence models on microcontrollers Thai, Bao-Toan; Tran, Vy-Khang; Pham, Hai; Nguyen, Chi-Ngon; Nguyen, Van-Khanh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2829-2839

Abstract

Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.
Towards optimal fillet portioning: a computer vision system for determining the fish fillet volume Nguyen, Chanh-Nghiem; Vo, Ngọc-Tan; Nguyen, Ngoc-Thanh; Tran, Nhut-Thanh; Nguyen, Chi-Ngon
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp550-558

Abstract

Portioning large fish fillets for packaging is usually performed manually by skilled workers. Automating this process and obtaining packaged products with attractive shapes and affordable weights will be beneficial for promoting purchase decisions. Towards developing an automated fish fillet portioning system, this study investigated a computer vision approach for determining the fillet volume. A belt conveyor would transport a fish fillet to the image capture booth, where its cross-section areas would be calculated for volume determination. The developed system could be operated with a conveyor speed ranging from 7.5 to 30.6 mm/s. The system performance was evaluated at a conveyor speed of 7.5 mm/s using small catfish fillets from 142.2 to 225.4 cm3. A mean percent error of 9.2% was observed, and the smallest percent error of 3.8% was obtained with a 225.4 cm3 fillet. With minor measurement errors obtained for larger fillets, the proposed computer vision system showed great potential for developing a cost-effective automated system for customized fish fillet partitioning to accelerate purchase decisions and also for quality control and classification of the fish fillets.
Real-time anomaly detection in electric motor operation noise Nguyen, Van-Khanh; Thai, Bao-Toan; Tran, Vy-Khang; Pham, Hai; Nguyen, Chi-Ngon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3814-3826

Abstract

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.
Analysis and modeling of a pneumatic artificial muscle system Tran, Vinh-Phuc; Tran, Nhut-Thanh; Nguyen, Chi-Ngon; Nguyen, Chanh-Nghiem
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp874-884

Abstract

Hysteresis is a common challenge in achieving precise position control of pneumatic artificial muscles (PAMs). Accurate modeling of this phenomenon is essential for the development of efficient PAM control systems. This study evaluates four mathematical models for modeling PAM dynamics: Nonlinear AutoRegressive with eXogenous inputs (NARX), BoxJenkins (BJ), Prandtl-Ishlinskii (PI), and second-order underdamped system and one zero (P2UZ). To assess the effectiveness of these models, experiments were conducted with reference input signals of varying amplitudes. The accuracy and goodness of fit of these models were evaluated based on root mean square error (RMSE) and coefficient of determination. Results show that the P2UZ model achieved the highest fitness (97.15%) and the lowest RMSE (1.80 mm), followed closely by the NARX model with 96.83% fitness and an RMSE of 1.90 mm. The PI and BJ models demonstrated lower performance, with the BJ model showing the lowest fitness (90.79%) and the highest RMSE (3.25 mm). These findings provide valuable insights for improving PAM control and PAM-based automation systems by highlighting the strengths and limitations of each model.
Real-time Assessment of ECG Classification based on Time-series Data and Other Types of Features Tran, Thanh-Luan; Thai, Bao-Toan; Tran, Vy-Khang; Nguyen-Thi, Xuan-Nhi; Nguyen, Chi-Ngon; Nguyen, Van-Khanh
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.8

Abstract

Cardiovascular diseases are the leading cause of mortality worldwide. An increasing number of studies have applied artificial intelligence (AI) to identify anomalies and classify electrocardiograms (ECGs), supporting early detection and diagnosis. This study proposes and evaluates the classification of ECG signals based on time-series data and features extracted via fast Fourier transform (FFT) and discrete cosine transform (DCT), implemented on resource-limited microcontroller units (MCUs) for selected AI models. Two models, the artificial neural network (ANN) and the convolutional neural network (CNN), were proposed for classifying five common ECG labels. These models were trained and tested with three types of input data: time-series data, FFT features, and DCT features, sourced from an available database. After training, the optimized models were quantized to assess their accuracy before being deployed in real-time to measure inference time on the ESP32 MCU. Before quantization, the ANN model achieved the highest accuracy with both DCT and time-series inputs (98.0%); meanwhile, the CNN model performed best with time-series input (97.0%). After quantization, the ANN maintained the highest accuracy with time-series input (97.1%), followed by the ANN with DCT at 95.6%. CNN models remained stable, with post-quantization accuracy of 95.8% for time-series input, 94.9% for DCT, and 90.0% for FFT. In contrast, ANN with FFT input showed a significant drop to 65.6%.
Development of a soil electrical conductivity measurement system in paddy fields Ho, The-Anh; Bui, Van-Huu; Nguyen, Van-Khanh; Nguyen, Dinh-Tu; Nguyen, Chi-Ngon
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp389-400

Abstract

This study aims to develop a data collection system for measuring soil electrical conductivity (EC) in Paddy Fields using the Wenner measurement principle, with one electrode configuration based on the Veris 3100 measuring machine, and a second electrode configuration with fixed electrodes. The result is a system that measures soil EC using the fixed electrode configuration for more stable results compared to the coulter electrode configuration on the Veris 3100. This system comprises three main components: a 7-inch industrial touchscreen monitor used for real-time monitoring and storing collected data on external memory, a controller that provides reverse direct current (DC) power supply to the electrodes and measures voltage and current parameters on the electrodes, two mechanical configurations including six electrodes in contact with the soil. The system is developed to operate best after soil preparation and before seed sowing. The collected data is processed and compared to actual measurements using an artificial neural network (ANN), resulting in an R2=0.7449, equivalent to linear regression. The system has been successfully installed and operated on a handheld tractor and tested in Paddy Fields on the outskirts of Can Tho City, Vietnam.
Co-Authors Bui, Van-Huu Ho, The-Anh Nguyen, Chanh-Nghiem Nguyen, Dinh-Tu Nguyen, Ngoc-Thanh Nguyen, Van-Khanh Nguyen-Thi, Xuan-Nhi Pham, Hai Thai, Bao-Toan Tran, Nhut-Thanh Tran, Thanh-Luan Tran, Vinh-Phuc Tran, Vy-Khang Vo, Ngọc-Tan