Faculty Publications
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Publications by NITK Faculty
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Item A Self-Balancing Five-Level Boosting Inverter with Reduced Components(Institute of Electrical and Electronics Engineers Inc., 2019) Sandeep, N.; Jagabar Sathik, J.S.; Yaragatti, U.R.; Krishnasamy, K.Two-Stage boosting multilevel inverters (MLIs), which are highly suitable for photovoltaic power plants, are known to suffer because of the high voltage stress on the switches of second stage. One of the ways to confront this issue is through eliminating the front-end booster. However, this leads to increased structural and control complexity of the resulting integrated boosting MLI. This letter presents a single-stage boosting MLI requiring lesser number of switches, diodes, and capacitors for renewable power generation applications. It requires nine switches and only one capacitor for five-level voltage generation. The topology has inherent self-balancing capability, thereby does not need additional balancing circuitry. The proposed topology has a uniform peak inverse voltage stress on the switches of value equal to the input dc voltage. A less complicated logic-form-equations-based gating pulse generation scheme is designed for enabling the proposed MLI to maintain its capacitor voltage. Further, a comparative study with state-of-the-art topologies is carried out to demonstrate the superior performance of the proposed topology. Finally, the feasibility of the proposed topology is validated through experimental tests and the corresponding results are elucidated. © 1986-2012 IEEE.Item A New Single-Source Nine-Level Quadruple Boost Inverter (NQBI) for PV Application(Institute of Electrical and Electronics Engineers Inc., 2022) Singh, A.K.; Raushan, R.; Mandal, R.K.; Ahmad, M.W.Multi-level inverters (MLIs) with switched capacitors are becoming popular due to their utilization in AC high-voltage applications as well as in the field of renewable energy. To achieve the required magnitude of output voltage, the switched capacitor (SC) technique employs a lesser number of DC sources in accordance with the voltage across the capacitor. Designing an efficient high-gain MLI with fewer sources and switches needs a rigorous effort. This paper introduces a prototype of a nine-level quadruple boost inverter (NQBI) topology powered by one solar photo-voltaic source using fewer capacitors, switches, and diodes when compared to the other SC-MLIs topology. The suggested NQB inverter produces nine levels of voltage in its output by efficiently balancing the voltages of the two capacitors. The various SC-MLIs are compared in order to highlight the benefits and drawbacks of the proposed nine-level quadruple boost inverter (NQBI) topology. To validate the efficacy of the proposed solar photovoltaic based NQBI without grid connection, detailed experimental results are presented in a laboratory setting under various test conditions. © 2013 IEEE.Item A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites(Institute of Electrical and Electronics Engineers Inc., 2023) Lakshmana Rao, L.R.; Rao, R.S.; Pais, A.R.; Gabralla, L.A.Phishing is a type of online scam where the attacker tries to trick you into giving away your personal information, such as passwords or credit card details, by posing as a trustworthy entity like a bank, email provider, or social media site. These attacks have been around for a long time and unfortunately, they continue to be a common threat. In this paper, we propose a boosting based multi layer stacked ensemble learning model that uses hybrid feature selection technique to select the relevant features for the classification. The dataset with selected features are sent to various classifiers at different layers where the predictions of lower layers are fed as input to the upper layers for the phishing detection. From the experimental analysis, it is observed that the proposed model achieved an accuracy ranging from 96.16 to 98.95% without feature selection across different datasets and also achieved an accuracy ranging from 96.18 to 98.80% with feature selection. The proposed model is compared with baseline models and it has outperformed the existing models with a significant difference. © 2013 IEEE.