Showing 4 results for Dey
S. K. Gudey, S. Andavarapu,
Volume 17, Issue 3 (September 2021)
Abstract
A three-phase dual-port T-type asymmetrical multilevel inverter (ASMLI) using two sources, solar forming the high voltage level and the battery forming the low voltage level, is considered for grid interconnection. A vertical shifted SPWM is used for the ASMLI circuit. A transformerless system for grid interconnection is achieved for a 100-kW power range. A well-designed boost converter and a Buck/Boost converter is used on the front side of the inverter. Design of battery charge controller and its controlling logic are done and its SOC is found to be efficient during charging and discharging conditions. A closed-loop control using PQ theory is implemented for obtaining power balance at 0.7 modulation index. The THD of the current harmonics in the system is observed to be 0.01% and voltage harmonics is 0.029% which are well within the permissible limits of IEEE-519 standard. The power balance is found to be good between the inverter, load, and the grid during load disconnection for a period of 0.15s. A comparison of THD’s, voltage, current stresses on the switches, and conduction losses is also presented for a single-phase system with respect to a two-level inverter which shows improved efficiency and low THD. Hence this system can be proposed for use in grid interconnection with renewable energy sources.
A. O. Akande, F. A. Semire, Z. K. Adeyemo, C. K. Agubor,
Volume 19, Issue 2 (June 2023)
Abstract
The quality of signal at a particular location is essential to determine the performance of mobile system. The problem of poor network in Lagos, Nigeria needs to be addressed especially now that the attention is toward online learning and meetings. Existing empirical Path Loss (PL) models designed elsewhere are not appropriate for predicting the 4G Long-Term Evolution (LTE) signal in Nigeria. This research developed a modified Okumura-Hata model in 4G network. The Okumura-Hata model being the closest to the measured values was modified using the PL exponent. The modified model was enhanced by Gravitational Search Algorithm (GSA). The measured data, modified and existing models were simulated using MATLAB R2018a software. Root Mean Square (RMSE) was used to evaluate the performance modified and existing and models. The result showed that Enhanced GSA model outperformed the existing models. The study successfully developed a modified PL model for LTE in Lagos, Nigeria. Therefore, modified model will be a good model in network planning for voice and fast online data connection in 4G LTE network.
Biswapriyo Sen, Maharishi Kashyap, Jitendra Singh Tamang, Sital Sharma, Rijhi Dey,
Volume 20, Issue 2 (June 2024)
Abstract
Cardiovascular arrhythmia is indeed one of the most prevalent cardiac issues globally. In this paper, the primary objective was to develop and evaluate an automated classification system. This system utilizes a comprehensive database of electro- cardiogram (ECG) data, with a particular focus on improving the detection of minority arrhythmia classes.
In this study, the focus was on investigating the performance of three different supervised machine learning models in the context of arrhythmia detection. These models included Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). An analysis was conducted using real inter-patient electrocardiogram (ECG) records, which is a more realistic scenario in a clinical environment where ECG data comes from various patients.
The study evaluated the models’ performances based on four important metrics: accuracy, precision, recall, and f1-score. After thorough experimentation, the results highlighted that the Random Forest (RF) classifier outperformed the other methods in all of the metrics used in the experiments. This classifier achieved an impressive accuracy of 0.94, indicating its effectiveness in accurately detecting arrhythmia in diverse ECG signals collected from different patients.
Pampa Debnath, Diptadip Barai, Rajorshi Mandal, Ayeshee Sinha, Jeet Saha, Arpan Deyasi,
Volume 20, Issue 2 (June 2024)
Abstract
A novel architecture is proposed in the present paper for detection and monitoring of air pollution at real-time condition following industrial standard, embedded with gas sensors which are able to identify both organic as well as inorganic hazardous contents. A vis-à-vis comparative analysis is carried out with existing literature highlighting cons of most referred circuits, both in component, system and power consumption levels, and a generalized drawback is reported citing their inefficacy for real-time data collection and accuracy level. Detailed review is reported based on qualitative assessments also, and henceforth, justifies the significance of the proposed design; where not only higher ranges of detection are possible, however is also associated with lower power consumption (26.41% and 10.71% respectively compared to the two latest circuits) and finer detection of dust particles even at extremely low concentration. The architecture will help to implicate precautionary steps at real-time condition for controlling the harmful effect in Society.
