Sharif Digital Repository

Rail noise prediction models enable consideration of different scenarios for the optimal management of noise prevention and mitigation. This project is aimed at developing an equation that enables computation of LA,max for the Tehran-Karaj commuter train, a type of Diesel-Electric Locomotive. The form of the proposed model is derived from equations for predicting LA,max for a single locomotive pass-by, proposed in the manual prepared by Harris Miller Miller & Hanson Inc. for the US Federal Transit Administration, and in the French rail noise prediction model. The algorithm for predicting LA,max for the Tehran-Karaj commuter train has been developed on the basis of the 50 measurements from 5... 

Trip production has long been considered as a major element in trip demand estimation. Many models have been presented for this purpose. Models use socio-economic variables in order to predict trip production. This paper develops an Adaptive-Network-based Fuzzy Inference System (ANFIS) models to predict school trip production. ANFIS can construct an input-output mapping based on both human knowledge and stipulated input-output data pairs. In order to improve models' generalization capability, a heuristic algorithm is used to generate reasonable initial values for data loss in training data set. Models with different Membership Functions (MFs) were trained, validated and tested with real data... 

In this paper, the effect of viscosity on internal ballistics simulation of a solid rocket motor with an internal burning cylindrical grain is numerically investigated. Axisymmetric compressible Navier-Stokes equations are discretized by finite volume approach on structured mesh and are solved using upwind Roe's scheme. A quasi steady procedure is used to simulate the regression of the burning surface by using a steady flow solver. Grid resolution is studied to insure acceptable accuracy. It is finally shown that most physical phenomena are quite accurately simulated by using an Euler solver, while NS solvers do not increase the accuracy as much they increase the CPU time  

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database. © 2006 IEEE  

Extraction of lipoxygenase-1 was investigated by using the aqueous two-phase system formed by sodium sulfate-poly(ethylene glycol)-buffer. The extraction was performed in a 47-mm inner diameter spray column, operating in a semibatch manner. The effects of dispersed-phase flow rate and phase compositions on fractional dispersed-phase hold up (εD) and volumetric masstransfer coefficient (KDa) were respectively studied. It was found that the hold-up and volumetric mass-transfer coefficient increased with an increase in dispersed-phase velocity and decreased with increasing phase composition. Correlations have been developed by nonlinear regression for estimation of fractional dispersed-phase... 

To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp inputs, we develop a mathematical programming model in this paper. The method is constructed on the basis of minimizing the square of the total difference between observed and estimated spread values or in other words minimizing the least square errors. The advantage of the proposed approach is its simplicity in programming and computation as well as its performance. To compare the performance of the proposed approach with the other methods, two examples are presented. © 2004 Elsevier Inc. All rights reserved  

The second-order rate coefficients for aromatic nucleophilic substitution reaction between 1-chloro-2,4-dinitrobenzene and aniline have been measured in aqueous solutions of ethanol and methanol at 25°C. The plots of rate constants versus mole fraction of water show a maximum in all-aqueous solutions. The effect of four empirical solvent parameters including hydrogen bond donor acidity (α), dipolarity/polarizability (π*). normalized polarity (ETN), and solvophobicity (Sp) has been investigated. This investigation has been carried out by means of simple and multiple regression models A dual-parameter equation of log k2 versus Sp and α was obtained in all-aqueous solutions (n = 41, r = 0.962,... 

A hybrid method consisting of principal component analysis (PCA), multiple linear regressions (MLR), and artificial neural network (ANN) was developed to predict the retention time of 149 C3 - C12 volatile organic compounds for a DB-1 stationary phase. PCA and MLR methods were used as feature-selection tools, and a neural network was employed for predicting the retention times. The regression method was also used as a calibration model for calculating the retention time of VOCs and investigating their linear characteristics. The descriptors of the total information index of atomic composition, IAC, Wiener number, W, solvation connectivity index, Xlsol, and number of substituted aromatic... 

Anti-HIV activity for a set of 107 inhibitors of the HIV-1 reverse transcriptase, derivatives of 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), was modeled with the aid of chemometric techniques. The activity of these compounds was estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques and compared with the previous works. The results obtained using the MLR method indicate that the anti-HIV activity of the HEPT derivatives depends on the reverse of standard shadow area on the YZ plane and the ratio of the partial charges of the most positive atom to the most negative atom of the molecule. The best computational neural network model was... 

In this research the application of design of experiment (DOE) coupled with the artificial neural networks (ANN) in kinetic study of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst at 400-500 °C and a method of data collection/fitting for the experiments were presented. The proposed reaction network composed of consecutive and simultaneous reactions with kinetics expressed by simple power law equations involving a total of 20 unknown parameters (10 reaction orders and 5 rate constants each expressed in terms of a pre-exponential factors and activation energies) determined through non-linear regression analysis. Because of the complex nature of the system, neural... 

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index... 

This paper describes the development of a numerical model with the ability to simulate and analyze the mechanical behavior of different types of Double Bolted Flush End-plate Beam (DBFEB) splice connections, which thus far has not been reported. Moreover, Bolted Flush End-plate Beam (BFEB) splice connections have been investigated for calibration of the results using Finite Element Modeling (FEM). The initial stiffness, rotation capacity, strength ratio, ductility, failure mode, and the performance of these two types of connections have been investigated and compared with each other with respect to FEMA 356. Also, classification of these two types of connections is performed by Eurocode 3... 

Petrography and image analysis have been widely used to identify and quantify porous characteristics in carbonate reservoirs. This paper uses the thin section images of 200 carbonate rock samples to predict the absolute permeability using intelligent and empirical methods. For each thin section, several pore network parameters are extracted from thin section images of rocks including the average pore size, average throat size, average throat length and average 2-D coordination number of pore network. A neural-based model successfully predicts the permeability of samples using pore network parameters as the inputs. Second neural network is applied for predicting absolute permeability... 

Climate change affects temperature, magnitude, and also the duration of the Urban Heat Island (UHI), which has severe impacts on the environment, communities, and the people's lives. This study evaluates UHI formation in the city of Tehran (Iran) using a reliability framework, which is able to include related uncertainties into the modeling procedure. First, 2 km air temperature field from Weather and Research Forecasting (WRF) model is downscaled to a 50 m grid spacing using a probabilistic downscaling method, which combines inverse distance weighting (IDW) interpolation and a Bayesian regression model. This downscaling method not only can produce a long record of the fine resolution of the... 

An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index... 

Heat transfer between heat exchangers and the surrounding environment, referred to as heat-in-leak, is a crucial phenomenon in the cryogenic applications which can substantially degrade the heat exchanger performance. Present research is organized to investigate the mechanism of heat transfer between the shell-side fluid and the shell wall of spiral wound heat exchangers (SWHEs) to determine the heat transfer coefficient used in the heat-in-leak calculations. The heat transfer characteristics are studied using computational fluid dynamics (CFD) tools. First, 20 dissimilar SWHE models with respect to the geometrical parameters are built and then numerically simulated at different Reynolds... 

This paper describes a three-dimensional (3D) finite-element (FE) model developed to assess the seismic behaviour of a novel prefabricated beam-to-beam connection termed a double-bolted flush end-plate beam (DBFEB) splice connection under cyclic loading. The seismic response was analysed and evaluated in terms of the hysteretic behaviour, rigidity, resistance degradation, ductility, energy dissipation capacity and equivalent damping coefficient. It is shown that the required performance can be achieved by controlling the end-plate thickness as well as the bolt grade and diameter; these parameters were therefore chosen as the key geometric variables investigated here for parametric study.... 

We propose two nonlinear regression methods, namely, Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models. Both methods try to make the residual of regression independent from regressors, while putting no assumption on noise distribution. In both methods, two adversarial networks are trained simultaneously where a regression network outputs predictions and a loss network that estimates mutual information (in AdOR) and KL-divergence (in AdOSE). These methods can be formulated as a minimax two-player game; at equilibrium, AdOR finds a deterministic map between inputs... 

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offord's charge over mass term (Q/M2/3), corrected steric substituent constant (E s,c) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report - up to 42 amino acid residues as compared to 13 amino acids in the initial study - that also included highly charged and hydrophobic...