Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN)

Abstract: This paper introduces a novel automatic hybrid classifier to detect and classify the Power Quality(PQ) problems in power systems using Wavelet Packet Transform(WPT) and Artificial Neural Networks (ANN). Various PQ events like Normal, Sag, Swell and Interruptions are obtained by modeling three phase distribution system using MATLAB simulink. For classification, the selection of suitable features from the disturbance signal is extremely important. Recent literature survey deals with various signal processing techniques like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT) were used for feature extraction. From the above techniques, the main issues addressed are selection of optimal feature subset and the design of ANN architecture model. To build a competent and robust classifier, it is essential to extract the utilizable feature vectors from the disturbance signal that can optimize data size as well as incorporate the main characteristics of the signal. This paper addresses these issues, and the distinctive feature vectors are obtained with reduced number of coefficients by using Energy Entropy based WPT. From these, the best discriminative feature vectors with reduced number of coefficients are obtained and are used as input to ANN, so that the burden over classification can be reduced. The classification performance is compared with FFT based ANN. The simulation results obtained have significant improvement over existing methods. Thus the proposed hybrid classifier provides the most excellent detection among PQ problems that arises in real time by improving classification accuracy in terms of both computation time and means square error.

Effects of antioxidants to reduce the harmful pollutants from diesel engine using preheated palm oil–diesel blend

Abstract: At present, IC engines are the primary power source in transit and must never be modified. The palm oil was collected from the palm tree and used in an internal combustion engine as an alternative fuel. In the present investigation, the palm oil is preheated to 110 °C using a heat exchanger to get a homogeneous mixture. The two additives, i.e., butylated hydroxytoluene (BHT) and n-butanol, were combined with preheated palm oil. The four fuels were prepared PN100, PND20, PND20 mixing with the 2000 ppm BHT of 1000 mL oil and other blends preheated PND20 palm oil mixing with the 2000 ppm n-butanol of 1000 mL oil. The blend 20 brake thermal efficiency was increased by 11.70% when compared with the mineral fuel, because the proper air–fuel mixture takes place. The oxides of nitrogen and smoke opacity were decreased by 15.32% and14.53%, respectively, for the blend of preheated palm oil and n-butanol as compared to mineral fuel due to the burning of more fuel through the combustion process. The hydrocarbon and carbon monoxide emissions were decreased for PND20AB and PNDAN blends by 35.71% and 14.53%, respectively, as compared to diesel. The preheated palm oil mixed with antioxidants was utilized as the alternative fuel in the conventional engine.

Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

Abstract: In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization.

Deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region

Abstract: In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex hyperspectral medical images is proposed. The performance of computer-aided diagnosis by verifying the region in hyperspectral image by pre and post-cancerous region classification is enhanced. For this a deep Boltzmann machine (DBM) architecture of the bipartite structure as an unsupervised generative model was developed. The performance of DBM is compared with deep convolutional neural network architecture. For implementation, a three-layer unsupervised network with a backpropagation structure is used. From the presented dataset, image patches are collected and classified into two classes, namely non-informative and discriminative classes as labelled classes. The spatial information is used for classification and spectral-spatial representation of class labels is formed. In the labelled classes, the accuracy, false-positive predictions, sensitivity are obtained for the proposed fully-connected network. By the proposed cognitive computation technique an accuracy of 95.5% with 93.5% sensitivity was obtained. From the obtained classification, accuracy and success rate DBM provide a better classification of complex images compared to traditional convolution network.