Sharif Digital Repository

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper, the goal is the prediction of cell membrane deformation under a certain force and to visually estimate the force of indentation on the membrane from... 

In this paper, a new constructive auto-associative neural network performing nonlinear principal component analysis is presented. The developed constructive neural network maps the data nonlinearly into its principal components and preserves the order of principal components at the same time. The weights of the neural network are trained by a combination of Back Propagation (BP) and Genetic Algorithm (GA) which accelerates the training process by preventing local minima. The performance of the proposed method was evaluated by means of two different experiments that illustrated its efficiency. ©2007 IEEE  

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power... 

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence... 

Neural Networks (FFNNs) that are trained with the Levenberg-Marquardt back-propagation algorithm were used. Models were evaluated using mean squared error (MSE) and coefficient of determination (R2) as statistical performance parameters. In traffic noise modelling, the noise level at a receptor position due to the source of traffic emission is modelled as a function of the traffic conditions, road gradient, road dimensions, speed and height of buildings around the road. The curse of dimensionality problems is caused by the large number of input variables in the ANN model. The Hybrid Genetic Algorithm-Gamma Test (GA-GT) as a data pre-processing method for determining adequate model inputs was... 

Mechanical failures in most cases originate from the exterior layers of the components. It is considerably effective to apply methods and treatments capable to improve the mechanical properties on component’s surface. Surface nanocrystallization produced by severe plastic deformation (SPD) processes such as severe shot peening (SSP) is increasingly considered in the recent years. However, artificial intelligence systems such as artificial neural network (ANN) as an efficient approach instead of costly and time consuming experiments is widely employed to predict and optimize the science and engineering problems in the last decade. In the present study the application of ANN in predicting of... 

Numerous studies yet have been carried out on downscaling of the large-scale climate data using both dynamical and statistical methods to investigate the hydrological and meteorological impacts of climate change on different parts of the world. This study was also conducted to investigate the capability of feedforward neural network with error back-propagation algorithm to downscale the provincial segmentation of Iran (30 provinces) on a daily scale. This model was proposed for the downscaling daily temperature, precipitation and wind speed data, and it was calibrated and verified by using the daily outputs derived from the National Center for Environmental Prediction (NCEP) database... 

Blur identification is one important part of image restoration process. Linear motion blur is one of the most common degradation functions that corrupts images. Since 1976, many researchers tried to estimate motion blur parameters and this problem is solved in noise free images but in noisy images improvement can be done when image SNR is low. In this paper we have proposed a method to estimate motion blur parameters such as direction and length using Radon transform and Feed-Forward back propagation neural network for noisy images. To design the desired neural network, we used Weierstrass approximation theorem and Steifel reference Sets. The experimental results showed algorithm precision... 

This paper proposes a fusion-based gender recognition method which uses facial images as input. Firstly, this paper utilizes pre-processing and a landmark detection method in order to find the important landmarks of faces. Thereafter, four different frameworks are proposed which are inspired by state-of-the-art gender recognition systems. The first framework extracts features using Local Binary Pattern (LBP) and Principal Component Analysis (PCA) and uses back propagation neural network. The second framework uses Gabor filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses lower part of faces as input and classifies them using kernel SVM. The fourth framework uses... 

This article aims to compensate the velocity and position errors that exist when the star sensor starts to work in a strapdown inertial navigation system aided by celestial navigation. These systems are integrated via unscented Kalman filter to estimate the current attitude and the gyros fixed bias, precisely. Since an accurate integration is desired, the nonlinear attitude equations are utilized in filter and these equations are propagated through a precise discretization method. Then, implementing the back-propagation and smoothing techniques, the initial attitude and the accelerometers fixed bias are also estimated. Finally, carrying out a parallel navigation, the velocity and position... 

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error back-propagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time... 

In this work the effects of backwash pressure, duration of acid and sodium hydroxide backwashing, sodium hydroxide concentration, and the duration of forward washing on performance of permeate flux recovery (PFR) were investigated. A two-level fractional factorial design (FFD) was used to design the experiments. The ability of back propagation neural network (BPNN) and radial basis function neural network (RBFNN) in predicting the performance of cleaning-in-place (CIP) of hydrophilic polyethersulfone (PES) membrane were investigated. It is found that BPNN has better ability in predicting the PFR performance than RBFNN. The best architecture of BPNN was a network consisting of 1 hidden layer... 

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from... 

In this work, an optimum multilayer perceptron neural network is developed to model the correlation between hot working parameters (temperature, strain rate and strain) and flow stress of IN625 alloy. Three variations of standard back propagation algorithm (Broyden, Fletcher, Goldfarb and Shanno quasi-Newton, Levenberg-Marquardt and Bayesian) are applied to train the model. The results show that, in this case, the best performance, minimum error and shortest converging time are achieved by the Levenberg-Marquardt training algorithm. Comparing the predicted values and the experimental values reveals that a well trained network is capable of accurately calculating the flow stress of the alloy... 

In this paper, an artificial neural network (ANN) model has been developed to predict the yield and tensile strengths of hot pressed NiAl intermetallic compound based on the experimental data from Albiter et al. [A. Albiter, M. Salazar, E. Bedolla, R.A.L. Drew, R. Perez, Mater. Sci. Eng. A 347 (2003) 154]. The predicted results, with a correlation relation between 0.9791 and 0.9921, show a very good agreement with the experimental values. Furthermore, the sensitivity analysis was performed to investigate the importance of the effects of chemical composition and temperature on the mechanical behavior of hot pressed NiAl intermetallic compound. © 2008 Elsevier B.V. All rights reserved  

In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input... 

The phenomenon of the hydraulic jump is so complex that despite considerable laboratory and prototype studies, estimation of its main characteristics in a generalized and accurate form is still difficult. The Artificial Neural Network (ANN) approach aims at limiting the needs for costly and time-consuming experiments. In this study, two ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis function using orthogonal least-squares algorithm (RBF/OLS), were used to predict the roller length, sequent depth, and the relative energy loss of the B-jump. Based on a pre-specified range of jump parameters, the input vectors include: upstream bed slope (tan θ),... 

Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The...