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Davangere, India

Upadhyay A.,Motilal Nehru National Institute of Technology | Prajapati Y.K.,Motilal Nehru National Institute of Technology | Singh V.,Banaras Hindu University | Saini J.P.,BIET
Optics Communications | Year: 2015

Comprehensive analysis of reverse index waveguide based sensor with metamaterial as a guiding layer to achieve high sensitivity for detection of microbe has been made. The detection will be done of larger cells like bacteria using reverse index profile of waveguide in four layer planar waveguide structure. Proposed four layer structure has larger adlayer sensitivity over the similar waveguide based sensor having dielectric material as guiding layer. © 2015 Elsevier B.V. All rights reserved. Source

Khan S.Z.,BIET | Suman S.,Indian National Institute of Engineering | Pavani M.,Indian National Institute of Engineering | Das S.K.,Indian National Institute of Engineering
Geoscience Frontiers | Year: 2016

Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN) using data available in the literature. The performance of FN was compared with support vector machine (SVM) and artificial neural network (ANN) based on statistical parameters like correlation coefficient (R), Nash - Sutcliff coefficient of efficiency (E), absolute average error (AAE), maximum average error (MAE) and root mean square error (RMSE). Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output. © 2015 China University of Geosciences (Beijing) and Peking University. Source

Samanta C.K.,BIET | Padhy S.K.,Siksha O' Anusandhan University | Panigrahi S.P.,Gandhi Institute for Technological Advancement | Panigrahi B.K.,Indian Institute of Technology Delhi
IET Electrical Systems in Transportation | Year: 2013

This study deals with energy management (EM) in hybrid electric vehicles. This study designs EM as an optimisation problem, then, optimises it using particle swarm optimisation (PSO) and some of its hybridisations. This study will be first in the literature to introduce PSO to the problem of EM in electric field. Moreover, this study proposes some novel applications of hybrid PSO, such as PSO-DE and PSO-QI. Encouraging simulation results obtained in this study that may attract for a case study for practical implementations. © The Institution of Engineering and Technology 2013. Source

Kumar N.,BIET | Gautam R.K.,Indian Institute of Technology BHU Varanasi | Mohan S.,Indian Institute of Technology BHU Varanasi
Materials and Design | Year: 2015

AA5052/ZrB2 composites with different volume percent (i.e. 0, 3, 6, 9 and 10vol.%) ZrB2 particles were developed by in-situ reaction of molten AA5052 alloy with two inorganic salts K2ZrF6 and KBF4 at a temperature of 860°C. The in-situ composites were characterized by DTA, XRD, SEM, TEM for reaction analysis and morphology. Their mechanical properties like hardness and tensile properties were evaluated using standard methods. Morphology studies show that grain size of Al-rich phase reduces due to the presence of ZrB2 particles. Microstructural studies also reveal the uniform distribution of second phase particles, clear interface, good bonding, dislocations and morphology of ZrB2 particles. It is found that ZrB2 particles are mostly in nano size with hexagonal or rectangular shape, however, few particles in micron size are also observed. Density and hardness of the composites increases with increase in the amount of reinforcement. Ultimate tensile strength and 0.2% yield strength (YS) also improved continuously with increase in the volume fraction of ZrB2 particles up to 9vol.% but beyond this composition strength reduced. It is important to note that with dispersion of ZrB2 particles in base alloy an improvement in ductility is observed which is contrary to many other composites. © 2015 Elsevier Ltd. Source

Gupta A.K.,Krishna Institute of Engineering and Technology | Singh Y.P.,BIET
Communications in Computer and Information Science | Year: 2011

This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Feature Recognition Neural Network model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when an incomplete or noisy version of that pattern is presented. An associative memory is a storehouse of associated patterns that are encoded in some form. In auto-association, an input pattern is associated with itself and the states of input and output units coincide. When the storehouse is incited with a given distorted or partial pattern, the associated pattern pair stored in its perfect form is recalled. Pattern recognition techniques are associated a symbolic identity with the image of the pattern. This problem of replication of patterns by machines (computers) involves the machine printed patterns. There is no idle memory containing data and programmed, but each neuron is programmed and continuously active. © 2011 Springer-Verlag. Source

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