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Semnan, Iran

Semnan University is a prestigious university in Iran located in the city of Semnan, Iran, about 240 km east of Tehran.The university has over 15,000 students, 60 undergraduate programs, 95 graduate , and 55 PhD programs. It has 25 faculties, 2 colleges, 2 institutes, 9 research groups, one Science and Technology Park, one Advanced Technologies Incubator Centre. The initial nucleus of Semnan University was formed in 1975 with the establishment of Semnan Higher Education Center. It launched its activities with 580 students to study in seven programs with an area of 5000 square meters.After the victory of the Islamic Revolution, extensive and fundamental changes were implemented at the Centre. In 1989, Semnan Higher Education Centre started its work under new title of Semnan Higher Education Complex while it enhanced its Electronic & Civil programs to a Bachelor level. With opening of the faculty of engineering, faculty of teacher training and faculty of veterinary medicine, Semnan Higher Education Complex changed its status to Semnan University in 1994.Semnan University has so expanded to include four campuses: 1. Technical campus2. Basic science campus3. Human science campus4. New Science and Technology campusThe university has 608 full-time academic members. It is situated in the Northeast part of the Semnan city with an area of 800 hectares. Libraries, computer centres, sports halls, restaurants, coffee shop and several dormitories are other facilities of the university. Since Semnan University is relatively young and newly established it is still under expansion and construction. Wikipedia.

Amjady N.,Semnan University
IEEE Transactions on Power Systems

Prediction of daily peak load for next month is an important type of medium-term load forecast (MTLF) for electrical power systems, which provides useful information for maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources, etc. However, the exclusive characteristics of daily peak load signal, such as its nonstationary, nonlinear and volatile behavior, present a number of challenges for this task. In this paper, a new hybrid forecast engine is proposed for this purpose. The proposed engine has an iterative training mechanism composed of a novel stochastic search technique and Levenberg-Marquardt (LM) learning algorithm. The effectiveness of the proposed forecast strategy is extensively evaluated based on several benchmark datasets. © 2010 IEEE. Source

Amjady N.,Semnan University | Vahidinasab V.,University of Tehran
Energy Conversion and Management

In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and models the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. Furthermore, unit commitment and inter-temporal constraints of generators are considered in the suggested model converting it to a mixed-integer programming (MIP) optimization problem. Sensitivity of the proposed framework with respect to both the level of the market prices and adopted risk level is also evaluated in the paper. Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems illustrating the performance of the proposed self-scheduling model. © 2012 Elsevier Ltd. All rights reserved. Source

Aghaei J.,Shiraz University of Technology | Amjady N.,Semnan University
International Journal of Electrical Power and Energy Systems

Electricity market clearing is currently done using deterministic values of power system parameters considering a fixed network configuration. This paper presents a new day-ahead joint market clearing framework (including energy, spinning reserve and non-spinning reserve auctions), which considers dynamic security of power system in the market clearing. The proposed framework has a stochastic multiobjective model considering power system uncertainties. It consists of three stages. Firstly, the uncertainty sources, i.e. contingencies of generating units and branches, are modeled using the Monte Carlo simulation (MCS) method. Subsequently, in the second stage, the proposed multiobjective framework simultaneously optimizes competing objective functions of offer cost and dynamic security index, i.e. corrected transient energy margin (CTEM). This index is selected because of useful linearity properties which it posses based on the sensitivity of the CTEM with respect to power shift between generators. The optimization problem in the second stage takes DC power flow constraints and system reserve requirements into account. Finally, in the last stage, scenario aggregation based on the expected value of the decision variables produces the final results of the market clearing framework. The 10-machine New England test system is studied to demonstrate effectiveness of the proposed stochastic multiobjective market clearing scheme. © 2011 Elsevier Ltd. All rights reserved. Source

Nazemi A.,Shahrood University of Technology | Omidi F.,Semnan University
Transportation Research Part C: Emerging Technologies

The shortest path problem is the classical combinatorial optimization problem arising in numerous planning and designing contexts. This paper presents a neural network model for solving the shortest path problems. The main idea is to replace the shortest path problem with a linear programming (LP) problem. According to the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the original problem. 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 shortest path problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. © 2012 Elsevier Ltd. Source

In this study, Nano particles were co-deposited with chromium from a hexavalent chromium bath by the conventional electrodeposition onto steel substrate as a cathode. The main goal of this work is to improve the wear and corrosion resistance, microhardness, coefficient of friction and select the best coating condition to satisfy these parameters using combined Analytic Hierarchy Process (AHP) - Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The dependence of the mentioned parameters was investigated in relation to the Al2O3, TiO2, SiO2 concentration in bath and particle size and it was found that the best tribological behavior improves by decreasing the particle size and increasing the particles concentration in the bath up to 10g/l. AHP-TOPSIS method led to choose the Cr-Al2O3 nanocomposite coating achieved at 10g/l Al2O3 content with mean particle size of 10nm as the preferred alternative which is in good accordance with empirical findings. © 2013 Elsevier Ltd. Source

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