Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...

The main contribution of the present work is to investigate the capability of exponential-type constitutive equationto model the warm deformation flow curves of a eutectoid steel in the temperature range of 620-770 °C andat the strain rates in the range of 0.01-10 s-1 conducted on a Gleeble-1500 thermomechanical simulator. Warmdeformation in this temperature range f...

Hydrograph modeling and prediction of groundwater levels are the main concerns of most hydrogeological calculations and water resource management process. The present study is an application of Sequential Gaussian Simulation (SGSim) method for predicting groundwater levels using recorded monthly data (180 months) related to 21 piezometers of Behbahan aquifer, southwest of Iran. To generate real...

introduction patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. in this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4d) xcat anthropomorphic phantom. materials and methods to access 4d modeling of motion...

A discrete-cosine-transformation (DCT) based decomposition method is proposed for modeling part form error, which decomposes the error field into a series of independent error modes. Compression, which ensures a compact model, is achieved by correlation reduction or mode truncation based on good energy compaction property of DCT. The part error related to assembly process (rigid body modes) and...

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...

estimation of reservoir water saturation (sw) is one of the main tasks in well logging. many empirical equations are available, which are, more or less, based on archie equation. the present study is an application of radial basis function neural network (rbfnn) modeling for estimation of water saturation responses in a carbonate reservoir. four conventional petrophysical logs (pls) including d...

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...