Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Data fusion of multi-source satellite data sets for cost-effective disaster management studies

Abstract: A common approach to multisource data fusion is to aggregate the information in a stacked vector and treat it as a unique dataset. The statistical classifiers used in these data fusion approaches are also transformed by integrating the contextual information from neighboring pixels, to improve the accuracy of a fuzzy-logic-based fusion scheme. A decision level fusion approach was developed by Gokaraju et. al, 2012, which combines statistical methods and machine learning techniques. Here, each data sample is integrated through separate classifiers such as empirical methods and support vector machines (SVMs) and then used a Probabilistic Neural Network (PNN) to fuse the decisions for a unified consensus decision. The data fusion approach consists of either pixel-level or feature-level data fusion in combination with machine learning techniques for classification. The intermediate results of the disaster management studies, such as levee land-slide and tornado debris assessment using data fusion techniques, are presented in this paper. For levee landslide studies, we used the multi-temporal datasets of air-borne synthetic aperture radar sensor (UAVSAR). For Tornado disaster studies, we used multi-source and multi-temporal datasets of both synthetic aperture radar sensor (RADARSAT-2) and multispectral sensor (RapiEye) datasets. The results of data fusion approach outperformed the non-data fusion techniques in both studies with kappa accuracies of 82.8% and 72%.

Power System Dynamic State Estimation Using Exponential Smoothing Methods

Abstract: For Secure operation and real time monitoring of power system, phasor estimation of voltages and currents has become ineludible at the power system buses. Phasor Measurement Unit (PMU) is the device which measures bus voltage phasor at the bus to which it is connected and current phasors through the lines connected to that bus. Power System Dynamic State Estimation (PSDSE) incorporating synchronized phasor measurements acquire the ability to predict system state one step ahead. Exponential Smoothing methods, in which more weightage is given to recent data points and weightage decreases exponentially as the data points become older, are the most popular forecasting methods which ensure highly accurate and reliable phasor estimation. This paper illustrates PSDSE on an IEEE 14- bus test system using exponential smoothing methods with measurements obtained from PMU. The performance of different methods is compared.