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A novel approach for measuring the void fraction in stratified air-water systems utilizing an 8-blade capacitance-based sensor, sinogram, and a deep neural network

Mayet Abdulilah Mohammad, Shahsavari Mohammad Hossein, Alizadeh Sayed Mehdi, Hanus Robert, Parayangat Muneer, Raja Ramkumar M., Muqeet Mohammed Abdul, Salman Mohammed Arafath, Kubiszyn Piotr

Advances in Science and Technology. Research Journal

2025

Vol. 19, no. 1

269--283

Measuring Void Fraction (VF) in a pipeline is crucial for ensuring operational efficiency, safety, and environmental responsibility in various engineering applications. There are several methods commonly used to measure VF in multiphase flow systems. Capacitance sensors are a dependable and practical option for measuring VF, providing benefits such as versatility, sensitivity, cost-effectiveness, and ease of use. In this study, simulations were performed to produce different VF levels of an air-water stratified two-phase flow, ranging across 31 distinct VF values from completely full to entirely empty. Moreover, an 8-blade concave capacitive sensor was designed and utilized for VF measurements. In order to use the power of the Finite Element Method (FEM), COMSOL Multiphysics was employed to produce the desired void fractions and measure the capacitance value of each pair of electrodes. The capacitance values of these electrode pairs were measured, resulting in the creation of sinograms corresponding to different VF. These sinograms were utilized as inputs for a Deep Neural Network (DNN) developed in Python, specifically a Multilayer Perceptron model, to estimate VFs. Furthermore, to enhance user understanding, sinograms were employed to reconstruct fluid images using the back-projection method. The results demonstrated an accuracy of 0.002, a significant improvement over previous methodologies in VF measurement.

Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning

Usman Mohammed, Zubair Mohammed, Ahmad Zeeshan, Zaidi Monji, Ijyas Thafasal, Parayangat Muneer, Wajid Mohd, Shiblee Mohammad, Ali Syed Jaffar

Archives of Acoustics

2021

Vol. 46, No. 1

41--53

Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.