Search

Your search keyword '"Abdi, M. S."' showing total 5 results
Start Over Author "Abdi, M. S."
5 results on '"Abdi, M. S."'

4. Impurity Tracing in Nanostructured Al and Al/Al2O3 Composite Prepared by High Energy Milling.

Author

Tousi, S. S. Razavi, Rad, R. Yazdani, and Abdi, M. S.

Subjects
NANOSTRUCTURES, COMPOSITE materials, PROCESS control systems, ALUMINUM, CARBON, OXYGEN
Abstract

The nature and origins of major impurities and their effect on properties of a nano-grained Al and Al/Al2O3 composite prepared in a planetary ball mill have been investigated. Carbon and oxygen from the process control agent and Fe from the vial erosion were found to be the main impurities affecting the structural properties, such as lattice parameters and melting point of the milled aluminum mixture. An attempt was made to assess the effect of impurities on mechanical properties of the products using common theoretical formulas. [ABSTRACT FROM AUTHOR]

Published
2010
Full Text
View/download PDF

5. A comparison between artificial neural network method and nonlinear regression method to estimate the missing hydrometric data.

Author

Bahrami, J., Kavianpour, M. R., Abdi, M. S., Telvari, A., Abbaspour, K., and Rouzkhash, B.

Subjects
ARTIFICIAL neural networks, NONLINEAR theories, REGRESSION analysis, HYDRAULIC measurements, NONLINEAR statistical models, COMPARATIVE studies, ROOT-mean-squares
Abstract

Missing values are a common problem faced in the analysis of hydrometric data. The need for complete hydrological data, especially hydrometric data for planning, development and designing hydraulic structures, has become increasingly important. Reasonably estimating these missing values is significant for the complete analysis and modeling of the hydrological cycle. The major objective of this paper is to estimate the missing annual maximum hydrometric data by using artificial neural networks (ANN). Sixteen stations, with 28 years of measurements, in the catchment area of the Sefdroud watershed in the north of Iran were selected for this investigation. Comparison between the results of ANN and the nonlinear regression method (NLR) illustrated the efficiency of artificial neural networks and their ability to rebuild the missing data. According to the coefficient of determination (R²) and the root mean squared value of error (RMSE), it was concluded that ANN provides a better estimation of the missing data. [ABSTRACT FROM AUTHOR]

Published
2011
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results