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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Tran, Duc-Tan
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Computer Science & IT Electrical & Electronics Engineering

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Probability distributions in Kerala’s rainfall: implications for hydro energy planning Baranitharan, Balakrishnan; Chandran, Karthik; Subramaniyan Mathan, Vaithilingam; Chowdhury, Subrata; Nguyen Thi, Thu; Tran, Duc-Tan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3372-3381

Abstract

Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.
Improving complex shear modulus imaging quality through enhanced frequency combination techniques Nguyen, Cuong-Thai; Thi Thu Ha, Pham; Duy Phong, Pham; Hai Luong, Quang; Bo Quoc, Bao; Tran, Duc-Tan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9033

Abstract

This study aims to improve the accuracy of complex shear modulus imaging (CSMI), a technique used to assess the elasticity and viscosity of soft tissues, essential for analyzing tissue structure and detecting tumors. CSMI methods are primarily divided into quasi-static and dynamic approaches, with the dynamic method estimating the complex shear modulus (CSM) by combining particle velocity measurements with force excitation. However, CSM estimation is vulnerable to errors from noise and the estimation method itself. To address noise, various filtering techniques are commonly applied. Additionally, errors from the estimation process can be minimized using approaches like frequency combination methods. In this research, we introduce an enhanced frequency combination method that substantially increases the accuracy of CSM parameter estimation, leading to higherquality CSMI outcomes. The proposed method achieves the lowest estimation error and the highest Q-index value compared to previous works. The proposed approach offers a valuable advancement in soft tissue imaging, supporting more reliable and precise diagnostic capabilities.
Real-time sleep posture classification using wearable accelerometers and machine learning models Nguyen, Thi Thu; Quoc, Bao Bo; Prakash, Kolla Bhanu; Tran, Duc-Tan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10105

Abstract

Sleep posture plays a critical role in sleep quality and health, influencing conditions such as sleep apnea. Accurate classification of sleep postures is essential for diagnosing and treating sleep-related disorders. The sleep posture can be detected by using wearable acceleromter. This paper presents an realtime classification system for four sleep postures by integrating accelerometer data with a machine learning (ML) model. The proposed system was tested with various ML models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector classifier (SVC), and logistic regression (LR), across multiple performance metrics. The results demonstrate that the LR model, when combined with accelerometer data, significantly outperforms other methods, achieving a classification accuracy of 91%. This paper also discusses the system’s potential for real-time deployment on embedded devices, contributing to advancements in sleep posture monitoring.
Co-Authors Baranitharan, Balakrishnan Bo Quoc, Bao Chandran, Karthik Chowdhury, Subrata Duy Phong, Pham Hai Luong, Quang Nguyen Thi, Thu Nguyen, Cuong-Thai Nguyen, Thi Thu Prakash, Kolla Bhanu Quoc, Bao Bo Subramaniyan Mathan, Vaithilingam Thi Thu Ha, Pham