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Thakare V.V.,Anand Engineering College | Singhal P.,Madhav Institute of Technology and Science
International Journal of RF and Microwave Computer-Aided Engineering | Year: 2010

Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in-depth different designs and analysis methods of microstrip patch antenna using artificial neural-network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design.

Gupta R.A.,Indian National Institute of Engineering | Wadhwani A.K.,Madhav Institute of Technology and Science | Kapoor S.R.,Rajasthan Technical University Kota
IEEE Transactions on Energy Conversion | Year: 2011

Even though induction motors are frequently used electromagnetic devices in industries owing to their high reliability, high efficiency, and low maintenance requirements, they are prone to various faults and failures. Most of these faults occurring in the induction motors are perceptible in nascent stages. This averts the inopportune machine failures and helps to adeptly plan the maintenance schedules. Most of the methods used for preprediction of faults in induction motors are based on complicated techniques involving tortuous mathematical analysis. Although the importance and accuracy of these methods cannot be overruled, but a simple method is required as a first stage necessary condition test, which can classify the motor health condition into one of the three broad categories, namely, healthy, fault prone, and critical. This paper discusses a simple method based on symbolic dynamic analysis of stator current samples to detect faults in the induction motors. The experimentation has been performed on a 3Φ, 1.5 kW, 4P, 1440 RPM squirrel cage motor to validate the proposed scheme. The data captured through the laboratory setup have been used to corroborate the proposed symbolic dynamic-based scheme. © 2010 IEEE.

Khaliq A.,Jamia Millia Islamia University | Agrawal B.K.,Jamia Millia Islamia University | Kumar R.,Madhav Institute of Technology and Science
International Journal of Refrigeration | Year: 2012

In the proposed cogeneration cycle, a LiBr-H2O absorption refrigeration system is employed to the combined power and ejector refrigeration system which uses R141b as a working fluid. Estimates for irreversibilities of individual components of the cycle lead to possible measures for performance improvement. Results of exergy distribution of waste heat in the cycle show that around 53.6% of the total input exergy is destroyed due to irreversibilities in the components, 22.7% is available as a useful exergy output, and 23.7% is exhaust exergy lost to the environment, whereas energy distribution shows 44% is exhaust energy and 19.7% is useful energy output. Results also show that proposed cogeneration cycle yields much better thermal and exergy efficiencies than the previously investigated combined power and ejector cooling cycle. Current investigation clearly show that the second law analysis is quantitatively visualizes losses within a cycle and gives clear trends for optimization. © 2011 Elsevier Ltd and IIR. All rights reserved.

Jain T.,Madhav Institute of Technology and Science | Singh S.N.,Indian Institute of Technology Kanpur | Srivastava S.C.,Indian Institute of Technology Kanpur
Applied Soft Computing Journal | Year: 2011

In a competitive electricity market, available transfer capability information is required by market participants as well as the system operator for secure operation of the power system. The on-line updating of available transfer capability information requires a fast and accurate method for its determination. This paper proposes a radial basis function neural network based method for available transfer capability estimation in an electricity market having bilateral as well as multilateral transactions. Euclidean distance based clustering technique has been employed to select the number of hidden radial basis function units and unit centres for the radial basis function neural network. In order to reduce the number of inputs and the size of the neural network, a feature selection has been performed using two different methods based on Euclidean distance based clustering and random forest technique and the performance of the radial basis function neural network, trained with features selected using these two methods, has been compared. The effectiveness of the proposed method has been tested on 39-bus New England system and a practical 246-bus Indian system. © 2010 Elsevier B.V. All rights reserved.

Sharma P.,Madhav Institute of Technology and Science | Arya K.V.,ABV Indian Institute of Information Technology and Management | Yadav R.N.,Maulana Azad National Institute of Technology
Signal Processing | Year: 2013

This paper presents an efficient face recognition method where enhanced local Gabor binary pattern histogram sequence has been used for efficient face feature extraction and generalized neural network with wavelet as activation function is being used for classification. In this method the face is first decomposed into multiresolution Gabor wavelets the magnitude responses of which are applied to enhanced local binary patterns. The efficiency has been significantly improved by combining two efficient local appearance descriptors named Gabor wavelet and enhanced local binary pattern with generalized neural network. Generalized neural network is a proven technique for pattern recognition and is insensitive to small changes in input data. The proposed method is robust-to-slight variation of imaging conditions and pose variations. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate, equal error rate and time complexity. © 2012 Elsevier B.V.

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