Sharif Digital Repository

In this paper the direct torque control (DTC) of the induction motor fed by four switch three phase inverter (FSTPI) is investigated. A modified space vector modulation (SVM) approach with optimal voltage vector sequence is proposed to compensate the effect of dc-link voltage variations caused by circulating phase current through the capacitor bank. SVMDTC scheme of FSTPI fed induction motor is based on stator flux oriented control. According to simulation results, this approach significantly reduces the effect of dc-link voltage variation on the output torque, flux and current waveforms  

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are considered in a stepwise regression to identify significant variables. The president’s approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the... 

Leakage current is originated from common-mode voltage (CMV) time variations in a grid-connected photovoltaic (PV) system and results in several deficiencies. In this paper, a new method is proposed for CMV reduction in a three-phase voltage source inverter (VSI) system. The proposed method is based on space vector modulation and it utilizes only odd and zero vectors. By using the proposed method, the number of fluctuations of the CMV waveform per switching cycle is reduced; therefore, the amount of high-frequency harmonics of CMV is decreased. In addition, because of applying both active and zero vectors by the proposed method, the total harmonic distortion becomes less than most of other... 

In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available. Assuming a low-rank structure for the matrix, one natural solution would be to first apply a matrix completion to the data, and then to solve the resulting compressed sensing problem. In big data applications such as massive MIMO and medical data, the matrix completion step imposes a huge computational burden. Here, we propose to reduce the computational cost of the completion task by ignoring the columns corresponding to zero elements in the sparse vector. To... 

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are considered in a stepwise regression to identify significant variables. The president’s approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the... 

The current research attempted to apply a numerical investigation for external freestream-flow influence on thrust-vector control. The freestream-flow Mach numbers varying from 0.05 to 1.1 were studied at different flow conditions. Computational modeling and simulation of a converging diverging nozzle with shock-vector control structure was achieved with utilizing the Unsteady-RANS approach and Spalart-Allmaras turbulence model. The present investigation has shown that, freestream-flow is an essential parameter on performance of shock-vector nozzle. Numerical results demonstrate that, increasing freestream Mach number would reduce the thrust-vectoring effectiveness. Furthermore, optimizing... 

The growth of intelligent attacks has prompted the designers to envision the intrusion detection as a built-in process in operating systems. This paper investigates a novel anomaly-based intrusion detection mechanism which utilizes the manner of interactions between users and kernel processes. An adequate feature list has been prepared for distinction between normal and anomalous behavior. The method used is introducing a new component to Linux kernel as a wrapper module with necessary hook function to log initial data for preparing desired features list. SVM neural network was applied to classify and recognize input vectors. The sequence of delayed input vectors of features was appended to... 

Let n be a positive integer and let Sn be the set of all nonnegative integers less than n which are relatively prime to n. In this paper, we discuss structural properties of circulant graphs generated by the Sn′s and their complements. In particular, we characterize when these graphs are well-covered, Cohen–Macaulay, Buchsbaum or Gorenstein. © 2020, Springer Science+Business Media, LLC, part of Springer Nature  

The objective of this paper is to predict the effect of hydrocarbon contamination on the shear strength parameters of sand by using various machine learning platforms. Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine were methods used to predict the hydrocarbon contamination impacts on the internal friction angle and cohesion of contaminated sand. Random forest exhibited the best results for cohesion, whereas, for the friction angle, the gradient boosting method outperformed other approaches. Moreover, the multi-output support vector machine yielded better results than those pertaining to a single support vector... 

The feasibility of integrating co-flow fluidic thrust-vectoring idea into the dynamics of a small flapless aerial tail-sitter is investigated in this article. The aircraft trimmability in different phases of flight and stability in take-off and level flight, are the main issues of concern for the study presented herein. In this respect, the vehicle's characteristic equations are derived by linearization of the general non-linear equations of motion. Since the vehicle was supposed to be merely controlled by fluidic thrust-vectoring, the concept was novel and some new derivatives are introduced. Margins of the required thrust-vector angle, to obtain a steady-state flight condition, are... 

Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled envi-ronments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable... 

AC drive users with sophisticated applications are demanding greater reliability to avoid process interruptions. AC motor drive systems are susceptible to sensors failure. A novel fault tolerant Field Oriented Control system for induction motors is introduced. The system maintains speed control in the event of sensors malfunction and adverse signal conditions, providing enhanced reliability. Different motor models are combined by a Fuzzy aggregation system in order to give a reliable estimate of flux vector. The proposed control system is an effective and easy to implement method giving a potential for motor drive reliability enhancement. © 2006 IEEE  

Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the... 

The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is... 

A numerical upwind-biased procedure which respects the essence of upwinding is suitably extended in order to reduce the false diffusion induced by a first-order approximation. In this regard, some arbitrarily first and second order gradient terms are added to the primary upwind approximation. The additional terms are then discretized using second-order schemes which essentially produce dispersive errors. The suitable choices for the weights of the new added terms result in lowering the dissipative role of the original upwind scheme. Additionally, the implicit appearance of the third-order terms, which are the consequences of second-order discretizations, helps to reduce the dissipative... 

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization, a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor, for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and... 

Nanocomposites containing ZrO2 and HA have been considered in various fields due to their unique mechanical properties. The principal purpose of this paper is to select the models with the maximum accuracy for the prediction of microhardness of CHA/ZrO2 nanocomposite. For this purpose, three models, including gene expression programming (GEP), gray wolf optimization algorithm (GWOA), and least squares support vector machine (LS-SVM), were implemented to predict and optimize the microhardness of the CHA/ZrO2 nanocomposite. Finally, the results showed that the data obtained from the LS-SVM model were closer to the preliminary data than the others. According to the results, the LS-SVM could...