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Institution

St. Joseph's College of Engineering

About: St. Joseph's College of Engineering is a based out in . It is known for research contribution in the topics: Wireless sensor network & PID controller. The organization has 1412 authors who have published 1477 publications receiving 13589 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the wind resources assessment models, site selection models and aerodynamic models including wake effect are reviewed and different performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed.
Abstract: Energy is an essential ingredient of socio-economic development and economic growth. Renewable energy sources like wind energy is indigenous and can help in reducing the dependency on fossil fuels. Wind is the indirect form of solar energy and is always being replenished by the sun. Wind is caused by differential heating of the earth's surface by the sun. It has been estimated that roughly 10 million MW of energy are continuously available in the earth's wind. Wind energy provides a variable and environmental friendly option and national energy security at a time when decreasing global reserves of fossil fuels threatens the long-term sustainability of global economy. This paper reviews the wind resources assessment models, site selection models and aerodynamic models including wake effect. The different existing performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed. This paper also reviews different techniques and loads for design, control systems and economics of wind energy conversion system.

908 citations

Journal ArticleDOI
TL;DR: A cost effective and eco-friendly technique for green synthesis of silver nanoparticles from 1 mM AgNO(3) solution using the extract of Piper longum leaf as reducing as well as capping agent is described.

294 citations

Journal ArticleDOI
TL;DR: In this article, the optimal process parameters were determined with reference to tensile strength of the joint and confirmed by conducting the confirmation run using the predicted optimal parameters using optimum parameters, which were optimized using Taguchi L16 orthogonal design of experiments.

255 citations

Journal ArticleDOI
TL;DR: The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy on the considered image dataset compared with the alternatives.
Abstract: Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives.

247 citations

Journal ArticleDOI
TL;DR: The purpose of this study is to consider the adsorption as a beneficial treatment of emerging contaminants also advanced and cost effective emerging contaminates treatment methods.

246 citations


Authors

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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202210
2021234
2020160
2019177
2018171
2017101