Osareh F.,Chamran University of Ahwaz |
Mostafavi E.,Library and Information Science
Information Sciences and Technology | Year: 2011
The purpose of this research was to examine the validity of Lotka and Pao's laws with authorship distribution of "Computer Science" and "Artificial Intelligence" fields using Web of Science (WoS) during 1986 to 2009 and comparing the results of examinations. This study was done by using the methods of citation analysis which are scientometrics techniques. The research sample includes all articles in computer science and artificial intelligence fields indexed in the databases accessible via Web of Science during 1986-2009; that were stored in 500 records files and added to "ISI.exe" software for analysis to be performed. Then, the required output of this software was saved in Excel. There were 19150 articles in the computer science field (by 45713 authors) and 958 articles in artificial intelligence field (by 2487 authors). Then for final counting and analyzing, the data converted to "Excel" spreadsheet software. Lotka and Pao's laws were tested using both Lotka's formula: xn □ = c (for Lotka's Law); also for testing Pao's law the values of the exponent n and the constant c are computed and Kolmogorov-Smirnov goodness-of-fit tests were applied. The results suggested that author productivity distribution predicted in "Lotka's generalized inverse square law" was not applicable to computer science and artificial intelligence; but Pao's law was applicable to these subject areas. Survey both literature and original examining of Lotka and Pao's Laws witnessed some aspects should be considered. The main elements involved in fitting in a bibliometrics method have been identified: using Lotka or Pao's law, subject area, period of time, measurement of authors, and a criterion for assessing goodness-of-fit.
Rezaeian-Zadeh M.,Islamic Azad University at Shiraz |
Zand-Parsa S.,Shiraz University |
Abghari H.,Urmia University |
Zolghadr M.,Chamran University of Ahwaz |
Singh V.P.,Texas A&M University
Theoretical and Applied Climatology | Year: 2012
This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg-Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1. 2°C, 1. 8°C, and 1. 7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation. © 2012 Springer-Verlag.
Heidari A.,Chamran University of Ahwaz |
Pahlavani M.R.A.,Malek-Ashtar University of Technology
Turkish Journal of Electrical Engineering and Computer Sciences | Year: 2016
This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized Benders decomposition along with a genetic algorithm are proposed to include minimum up/down time constraints, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, MATLAB software and CPLEX and KNITRO solvers are applied. To verify the proposed technique and algorithm, two case studies, the IEEE 14- And 118-bus systems, are implemented for optimal generation scheduling and reliability issues. Finally, a comparison with other solution techniques is given. © TÜBİTAK.
Ghanavati M.,Tarbiat Modares University |
Majd V.J.,Tarbiat Modares University |
Ghanavati M.,Chamran University of Ahwaz
2011 International Siberian Conference on Control and Communications, SIBCON 2011 - Proceedings | Year: 2011
This paper presents a method to design a predictive controller for a single Inverted Pendulum (SIP) system. The design goal is to balance the pendulum in the inverted position in the presence of parameter variations and measurement white noise Using linearization technique, the model of SIP system is transformed to a linear polytopic system. Since we need to estimation of states, a new robust model predictive control (RMPC) strategy is developed for this system with considering white measurement noise. Simulation results are presented to show the robustness of the closed-loop system against parameter variations and measurement white noise. © 2011 IEEE.
Hajipour B.,Chamran University of Ahwaz |
Gholamzadeh R.,Chamran University of Ahwaz
European Journal of Economics, Finance and Administrative Sciences | Year: 2010
In this study we analyze the relationships between three dimensions of the market launch strategy for new products - order of entry, scale of entry and product positioning - and dimensions of performance - firm competitive position, customer satisfaction and profitability. We test our model on a sample of 118 manufacturing firms, applying structural equation modeling based on the Partial Least Squares (PLS) methodology. Our findings reveal that order of entry and product positioning included in our model of market entry affect performance, and the pioneers gain advantages in terms of stronger competitive position and higher customer satisfaction which, in turn, do increase profitability. But, scale of entry has no effect on firm performance. © EuroJournals, Inc. 2010.