This paper presents and compares two different methods using in the forecasting of wind power turbine (WPT) outputs. These two forecasting methods, which utilize different types of input to forecast the output of WPT, are the Meteorology Forecasting Method (MFM) and the Observational Forecasting Method (OFM). The MFM determines the unit output from the forecasted wind speed at the WPT installation site, using the input from a composite data set created from the original annual-hourly weather data. Three different techniques can be used in MFM to forecast the wind speed, and the best result is selected for conversion calculation of the output of WPT. OFM, however, forecasts the unit output based on five observed annual-hourly data obtained from the operation of target WPT. Two different techniques can be used in the OFM simulation. The results from these techniques for each method are compared and the best one will be used for the final forecast of the WPT outputs. This paper presents and compares the forecasting results of WPT output obtained from MFM and OFM. Furthermore, in order to increase the result precision and decrease the forecast error, a new composite data system is also developed and proposed.The methodologies proposed in this paper will be very useful for designers, planners and operators of the wind power turbines

Most speech enhancement algorithms focus on obtaining an estimator relying on stochastic Models. In this paper, a minimum mean-square error (MMSE) estimator under a stochastic– Deterministic Model is proposed where a heavy-tail distribution called t-Location-Scale (tls) is used for Modeling Discrete Fourier Transform coefficients of clean speech signals and exponential and sinusoidal Models are employed as Deterministic Models. In the exponential Model, the frequency and damping coefficient are estimated by using the Matrix Pencil method. Also, in previous studies, the number of exponential components in the Deterministic Model for stochastic-Deterministic speech enhancement algorithm has been considered to be one. In this paper, the corresponding exponential Model is developed to have an arbitrary number of exponential components. The speech enhancement experiments are performed in three modes, exponential-Gaussian (the first proposed method), exponential-tls (the second proposed method), and sinusoidal-Gaussian. Comparisons are made with the exponential-Gaussian method (with only one exponential component), as well as with the Weiner and tls stochastic estimators. The implementation results in the presence of six noise types from Noisex-92 dataset show that the two proposed methods improve the segSNR values and have quite similar PESQ values comparing with the stochastic based speech enhancement methods.

The following study is based on a hybrid statistical-Deterministic Model designed for the assessment of the daily concentration of sulfur dioxide ( 2SO ), carbon monoxide ( CO ) and particulate matter ( 10PM ) as major pollutants in the Greater Tehran Area (GTA): the capital of Iran. The Model uses three available or assessable variables including economic, meteorological and environmental in the GTA for the year 2003. Economic sectors which are examined in this study are firstly traffic, secondly residential-commercial heating and thirdly industry. The Model determines to what degree each of the aforementioned sectors, in accordance to their associated fuel consumption, is responsible for air pollution. The Model also relates emission data from the three sectors whilst taking into consideration meteorological parameters. Thereafter, economic and meteorological parameters as independent explanatory variables opposed to the concentration of pollutants measured at the monitoring network stations which are dependent variables. All data is given in the form of time series for the year 2003 in specified areas discussed. The method adopted for the calculation of the regression coefficients of the Model, is based on nonlinear least squares multiple regression analysis. The Model has been tested on the available monitoring network stations for aforementioned pollutants in the GTA. Model verification has been carried out spatially in the year 2003 and temporally for the year 2005. Results show that the concentration of pollutants in the GTA can be estimated using this Model. Areas of further research are outlined which indicate possible enhancement of this approach and relevant application extensions.

Today, managers in all organizations, including banks, seek optimal utilization of various departments' available resources and capacities. Considering the emerging sales culture and customer segmentation within the comprehensive banking system, managers need to assess efficiency using conventional Models in data envelopment analysis in various areas, including deposit acquisition, providing financial services to customer groups, and generating profits. This paper aims to present a fuzzy network data envelopment analysis Model along with undesirable variables and shared resources to measure the efficiency of Iranian bank branches in the comprehensive banking system. This study considers a non-radial network data envelopment analysis Model based on scarcity variables. Given the uncertainties and uncertainties surrounding certain variables in data envelopment analysis Models in the real world, a fuzzy Model based on scarcity variables, taking into account undesirable variables and shared resources, is proposed. The results indicate that the proposed Model can assist industry managers in adopting various policies in different segments of the comprehensive banking process in a competitive comprehensive banking environment. The main issue that occurs in the evaluation of the performance of a comprehensive banking system in many real-world situations is that the inputs and outputs of the data envelopment analysis Model are not precise and desirable. In this study, non-Deterministic real-world data has been examined using network data envelopment analysis.

Congestion, delay and creation of long queues in urban networks make transportation authorities to take traffic management methods for optimizing the current capacity of infrastructures. One of the effective methods in this case is applying route guidance systems. Information and route guidance systems are a kind of intelligent transportation technologies which aid drivers to choose the proper route to their destinations. In this research, a route guidance system was developed which the operator (Traffic Management Center) can control the network by every level he wishes for. An overlapping parameter was introduced for every route which compares the cost of every route with the cost of the same one in system optimality condition.For Modeling purpose, two programming Model were applied which the first Model produced the inputs of the second Model. The second Model, considering the overlapping factor and the tolerance limit of the operator, guide the users. In the introduced assignment process, when the overlapping parameter reaches the tolerance limit of the operator, the route will be eliminated from the guidable route set and other routes will be introduced and reachable for users. The developed Model is tested for simple illustrative network. Results showed that the proposed pattern has the most effects in operator tolerance period from 1 to 1.4. When the tolerance parameter of the operator reaches 1.1, the total travel time of the network is decreased more than 13 percent. Also, results showed that only a little share of users (15 percent of users in most case) in comparison with UE condition will change their routes which promise the problems with disobedience of users and also validity of results.

In this paper, an economic production quantity (EPQ) inventory Model with scrap and rework is developed. The inventory Model is for multiple products and all products are manufactured in a single machine. Clearly, the existence of one machine results in limited production capacity and thus in shortages. Therefore, shortages are permitted and partially backordered. We show that the Model of the problem is a constrained non-linear program and use the GAMS Modelling language to solve it. Our objective is to minimize the joint total cost of the system and the supply cost of the warehouse space, subject to capacity, service level, and budget and warehouse space constraints. Subsequently, a nonlinear programming solver BARON is used to solve the Model. At the end, a numerical example is provided to demonstrate the applicability of the Model to real-world manufacturing problems. To verify the solution obtained and to evaluate the performance of MCDM (Multi-Criteria Decision Making) methods, a TUKEY test is employed to compare the means of the primary objective values, the mean values of the second objective, and the mean of the CPU time needed for solving the problem using various methods of MCDM. Also, to compare the methods, we used the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The results show that the Torabi-Hasini method is the most efficient method to solve the Model and the solving capacities of the methods differ significantly. Finally, some conclusions and future research are discussed.

To deal with different water conditions, a Model was developed to determine the optimal irrigation level and the optimal cropping area for major agricultural crops in lands of Moghan irrigation and drainage network in downstream of Aras Dam. In this Model, in the stochastic conditions, with considering the uncertainty of required water supply, decision variables (optimal irrigation levels and optimal planting area in certain time steps) are obtained using stochastic dynamic programming (SDP) method. Expected value of the maximum profit from planting crops is considered as the return function. Also in Deterministic conditions, the Model was run by considering 4 scenarios. The results show the superiority of cropping pattern and optimal irrigation levels (even in water deficiency conditions) in terms of all studied factors compared to the common cropping pattern (even without water deficiency) in the region. Minimum value for factor of the required water per hectare is related to scenario 2 (cropping of low water requirement crops with pressurized irrigation in Deterministic conditions) which is equal to 5368. 14 m3. This factor in scenarios 1, 3 and 4 is 9079. 78; 13496. 25and 9211. 73 m3 respectively and in the common cropping pattern in the region is 10900m3. Maximum value for factor of profit per hectare is related to scenario 2, equal to 102 million Rials. The mentioned factor for scenarios 1, 3 and 4 are 96. 5, 73 and 89. 5 million Rials, respectively.