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.

Nonlinear regression Models have widespread applications across diverse scientific disciplines‎. ‎Achieving precise fitting of the optimal nonlinear Model is essential‎, ‎taking into account the biases inherent in Bayesian optimal design‎. ‎This study introduces a Bayesian optimal design utilizing the Dirichlet process as a prior‎. ‎The Dirichlet process is a fundamental tool in exploring Nonparametric Bayesian inference‎, ‎providing multiple well-suited representations‎. ‎The research paper presents a novel one-parameter Model‎, ‎termed the ``unit-Exponential distribution"‎, ‎specifically designed for the unit interval‎. ‎Additionally‎, ‎a representation is employed to approximate the D-optimality criterion‎, ‎considering the Dirichlet process as a functional tool‎. ‎Through this approach‎, ‎the aim is to identify a nonparametric Bayesian optimal design.

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

In this paper, we have introduced a five‐, parameter bivariate Model by taking a geometric minimum of the modified Exponential distributions. It is observed that the maximum likelihood estimators of the unknown parameters cannot be obtained in closed form. We propose to use the EM algorithm to compute the maximum likelihood estimators of the unknown parameters. Several simulation experiments have been performed to determine the effectiveness of the proposed EM algorithm. We analyzed two datasets for illustrative purposes, and it is observed that the proposed Models and the expectation‐, maximization algorithm perform at a satisfactory level.

In this article, the autoregressive Model of order one with Exponential innovations is considered. The maximum likelihood and Bayes estimators of the autoregression parameter, under squared error loss function with non-informative prior are examined. A simulation study is conducted to compare the behavior of the estimators via their relative bias and risks. Moreover, a real data example is presented.

Supply chain is an accepted way of remaining in the competition in today's rapidly changing market. This paper presents a coordinated seller-buyer supply chain Model in two stages, which is called Joint Economic Lot Sizing (JELS) in literature. The delivery activities in the supply chain consist of a single raw material. We assume that the delivery lead time is stochastic and follows an Exponential distribution. Also, the shortage during the lead time is permitted and completely back-ordered for the buyer. With these assumptions, the annual cost function of JELS is minimized. At the end, a numerical example is presented to show that the integrated approach considerably improves the costs in comparison with the independent decisions by seller and buyer.

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.