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Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
Renewable Energy | Year: 2012

The data such as global solar radiation, air temperature, relative humidity, wind and moisture, was collected from 875 stations around the globe. Of which data from 210 stations fairly spread on the earth surface was used to develop the formula for estimating the monthly average daily global radiation on a horizontal surface. In this study, using air temperature, relative humidity, wind, moisture and few derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. Thus developed models have been validated with remaining 665 data sites. Finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ±30°, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations (models) can be used to estimate the monthly average daily global solar radiation in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over necessitated area. © 2011 Elsevier Ltd.


Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
Journal of Renewable and Sustainable Energy | Year: 2012

Solar energy is one of the most promising renewable energy sources. The availability of the solar energy potential data is very scarce and often not readily accessible. The main objective of this study was to estimate the monthly average global solar radiation at various locations for South America, by the generalized Iranna-Bapat's model. Iranna-Bapat's model is developed to estimate the value of global solar radiation at any location on earth surface. This model uses the most commonly measurable meteorological parameters such as ambient temperature, humidity, wind-speed, moisture for a given location. A total of 35 locations spread across the continent are used to validate this model. The computed values from Iranna-Bapat's model are compared with the measured values. Iranna-Bapat's model demonstrated acceptable results, and statistically displayed lower values of RMSEs. Therefore this model could be a good estimator for predicting the global solar radiation at other locations for South America, where such data is not available. © 2012 American Institute of Physics.


Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
International Journal of Applied Engineering Research | Year: 2011

Solar radiation is a primary driver for many physical, chemical and biological processes on the earth's surface. Complete and accurate solar radiation data at a specific region are quite indispensable to the solar energy related research. For locations where measured values are not available, a number of formulas and models have been developed to estimate solar radiation. This study aimed to develop new model(s) for estimating global solar radiation data using meteorological parameters. Measured solar radiation, air temperature, relative humidity, wind and moisture data from 210 sites around the earth was used for model development for estimating the monthly average daily global radiation on a horizontal surface. Several models and correlations that embrace such variables as the fraction of air temperature, relative humidity, wind and moisture, latitude, longitude and altitude have been selected. In this study, using air temperature, relative humidity, wind and moisture and derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. After validation with 665 data sites on these models, finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ± 30 o, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations can be used to estimate the monthly average daily global solar in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over large area. © Research India Publications.


Patil A.R.,Dktes Textile And Engineering Institute | Subbaraman S.,Annasaheb Dange College of Engineering and Technology
Proceedings - 2014 5th International Conference on Signal and Image Processing, ICSIP 2014 | Year: 2014

Extraction of significant and dominant features from possibly large set of database is a crucial task. The Performance of feature extraction technique depends on the dimensions of generated features and reconstruction. In this paper we provide a comparative study of different feature extraction techniques like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Principle Component Analysis (PCA), Local Binary Pattern (LBP), DCT+Gabor, DWT+ Gabor etc. and each technique is compared with each other based on Sebastian Marcel static hand postures database[24] consisting of six postures. We have used Neural Network to compare the performances of feature extraction techniques based on Recognition Accuracy (RA), False Acceptance Rate (FAR), False Recognition Rate (FRR) and also dimensions. We found that fusion of LBP and Gabor, DWT and Gabor provides good results. © 2014 IEEE.


Waghmode L.Y.,Annasaheb Dange College of Engineering and Technology | Sahasrabudhe A.D.,College of Engineering, Pune
International Journal of Computer Integrated Manufacturing | Year: 2012

The objective of this article is to present a methodology based on reliability and maintainability (R & M) parameters for effective implementation of life cycle costing in design and procurement of repairable systems. For this purpose, a number of life cycle cost models developed over the years have been reviewed, the important life cycle stages for repairable systems are identified and a generalised model for life cycle cost analysis is first proposed. The mathematical equations have been formulated for the life cycle stages, such as acquisition, installation and commissioning, operation, maintenance and repair and disposal. The focus is mainly on modelling the maintenance and repair costs, which are the major elements of repairable system life cycle cost. To model maintenance and repair costs, the stochastic point process approach is employed. The lifetime of repairable system is modelled using a two parameter Weibull distribution. The expected number of failures are estimated based on the assumption that the number of replacements of the components in an interval (0, t) follow renewal process (RP) in the first case and minimal repair process in the second case. Based on the expected number of failures, the lifetime maintenance and repair costs are estimated for the RP and the minimal repair process. A methodology to decide whether a renewal approach or minimal repair approach should be planned for a particular component is also presented. The proposed technique is then illustrated through its application to a typical repairable system, namely an industrial pump and the results obtained are presented along with a review for future work. The proposed model is believed to be a simple way for system designers to estimate and compare the life cycle cost of their different design alternatives at system design stage using system R & M parameters. © 2012 Taylor & Francis.

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