University of Shahrood is a university in the city of Shahrood in Semnan Province in Iran.The university was established as "The Shahrood College of Mines" in 1973, and was elevated to university status in 1994.Finally, thanks to the efforts and pursuit of university president and the staff at the time and the expansion of different programs and the development of post-graduate levels, another great achievement was gained for the university in that the Development Council of the ministry in a session dated 2002/6/9 announced his agreement with the promotion of the university to Shahrood University of technology,The university currently operates 11 faculties, offering 32 degrees to students at bachelors, masters, and PhD levels.The significant achievements of the university from 1997 up to now are as following:1. setting up the Ph.D. programs for mining engineering, physics and electrical engineering 2. setting up the master programs in 3. promotion of Shahrood University to Shahrood University of technology 4. the approval of long-term Academic Program of the university:According to this program approved by the Council of Development in session dated 2002/9/16 and due to the potentials of the university, the number of programs will increase to 93 by the end of the Nation Fourth Program of Development.The university was the first university in Iran and the forth in the world to offer Robotics Engineering at bachelors level.The university's faculty of mining and agriculture is among the best, offering numerous courses up to the PhD level. The university is among the growing universities in Iran.This university is one of several technical universities in Iran. Technical universities of Iran include: 1.Sharif university of Technology2.Amirkabir university of Technology3.Iran university of Science & Technology4.K. N. Toosi University of Technology5.Shahrood University of Technology 6.Sahand University of Technology 7.Babol Noshirvani University of Technology Wikipedia.
Fadafan K.B.,Shahrood University of Technology
European Physical Journal C | Year: 2011
We use the gauge-string duality to study heavy quarks in the presence of higher derivative corrections. These corrections correspond to the finite-coupling corrections on the properties of heavy quarks in a hot plasma. In particular, we study the effects of these corrections on the energy loss and the dissociation length of a quark–antiquark pair. We show that the calculated energy loss of heavy quarks through the plasma increases. We also find in general that the dissociation length becomes shorter with the increase of coupling parameters of higher curvature terms. © Springer-Verlag / Società Italiana di Fisica 2011.
Fadafan K.B.,Shahrood University of Technology
European Physical Journal C | Year: 2010
The effects of charge and finite 't Hooft coupling correction on drag force and jet quenching parameter are investigated. To study charge effect and finite 't Hooft coupling correction, we consider Maxwell charge and Gauss-Bonnet terms, respectively. The background is Reissner-Nordström-AdS black brane solution in Gauss-Bonnet gravity. It is shown that these corrections affect drag force and jet quenching parameter. We find an analytic solution of drag force in this background which depends on Gauss-Bonnet coupling and charge. We set Gauss-Bonnet coupling to be zero and find drag force in the case of Reissner-Nordström-AdS background. © 2010 Springer-Verlag / Societá Italiana di Fisica.
Alfi A.,Shahrood University of Technology
International Journal of Innovative Computing, Information and Control | Year: 2012
The difficulties of online identification mainly come from the unavoidable computational time to find a solution. This paper presents a novel particle swarm optimization (PSO), namely Dynamic Inertia Weight PSO (DIW-PSO), to cope with the online system parameter identification problem. In the proposed algorithm, to increase the efficiency and convergence speed of PSO algorithm, the inertia weight for every particle is dynamically updated based on the feedback taken from the fitness of the best previous position found by the particle. Also, a novel methodology is incorporated into DIW-PSO to be able to effectively response and detect any parameter variations of system to be identified. To illustrate the performance of DIW-PSO algorithm, a set of two wellknown representative benchmark functions is employed to evaluate it in comparison with Real-Coded Genetic Algorithm (RC-GA) and PSO with Nonlinearly Decreasing Inertia Weight (PSO-NDW). Simulations indicate that the DIW-PSO improves the search performance on the benchmark functions significantly. Also, the feasibility of this algorithm is demonstrated through identifying the parameters of a well-known nonlinear Lorenz chaotic system. The results exhibit that the proposed algorithm is a good promising PSO algorithm for online parameter identification. © 2012 ICIC International.
Fateh M.M.,Shahrood University of Technology
Nonlinear Dynamics | Year: 2010
This paper focuses on the uncertainty bound parameter (UBP) to design the robust control of electrical manipulators. The UBP is commonly obtained by considering the worst case of uncertainties in bounding functions. However, too high estimation of UBP may cause saturation of input, higher frequency of chattering in the switching control laws, and thus a bad behavior of the whole system, while too low estimation of UBP may cause a higher tracking error. A proper UBP is preferred to improve the performance of robust control system. A simple, less dependent and proper UBP is proposed based on the nominal model of electrical manipulator and feedbacks of joint accelerations. This work is motivated by recent experimental results in measuring acceleration by optical encoder. Modeling of an electrical manipulator with presence of uncertainties is presented for control purposes. The proposed robust control is justified by stability analysis. © 2010 Springer Science+Business Media B.V.
Nazemi A.,Shahrood University of Technology
Engineering Applications of Artificial Intelligence | Year: 2013
In this paper, a neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle to solve general convex nonlinear programming (GCNLP) problems. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the GCNLP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient. © 2012 Elsevier Ltd.