Showing 2 results for Srinivas
S. R. Sadu, P. V. Prasad, G. N. Srinivas,
Volume 14, Issue 1 (March 2018)
Abstract
This paper presents the comparative study of three phase twenty five level diode clamped and cascaded H-bridge multilevel inverters. The comparison is made in respect of requirement of devices, quality of output voltage and reduction of total harmonic distortion at the multilevel inverter terminals. In this work multicarrier sinusoidal pulse modulation control methods of Phase disposition (PD-PWM), phase opposition disposition (POD-PWM) and Alternative Phase Opposition Disposition (APOD-PWM) pulse width modulation control strategies are applied for both diode clamped and cascaded H-bridge multilevel inverters and compared its total harmonic distortion. The performance of both diode and cascaded H-bridge multilevel inverters is investigated and compared. Based on simulation results it is observed that the output voltage of the cascaded H-bridge multilevel inverters is better as compared to the diode clamped multilevel inverter. The proposed multilevel inverters are simulated using MATLAB/Simulink software.
Srinivas Babu N, Shashikiran S, M Jayanthi, Rajani N, K M Palaniswamy, M R Kushalatha,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. The modified CNN's improved feature extraction and classification accuracy are maintained throughout construction. To obtain good performance a TBX11K publicly accessible dataset is used it consists of 11000 images of which 4600 chest x-ray (CXR) images are considered in this research, and the suggested model is verified. This approach significantly increases the accuracy of categorizing TB symptoms. The PCA in this system locates the elements and extracts a large amount of variance technique applied to the full chest radiograph for pulmonary tuberculosis identification accuracy using SVM is 93.14% and modified CNN 96.72% respectively. When it comes to helping radiologists diagnose patients and public health professionals screen for tuberculosis in places where the disease is endemic, the proposed system SVM and modified CNN perform better than existing methods.
