Sharif Digital Repository

In this paper, we study the discriminative modeling of Spoken Language Understanding (SLU) using Conditional Random Fields (CRF) and Statistical Machine Translation (SMT) alignment models. Previous discriminative approaches to SLU have been dependent on n-gram features. Other previous works have used SMT alignment models to predict the output labels. We have used SMT alignment models to align the abstract labels and trained CRF to predict the labels. We show that the state transition features improve the performance. Furthermore, we have compared the proposed method with two baseline approaches; Hidden Vector States (HVS) and baseline-CRF. The results show that for the F-measure the proposed... 

The existing powerful steganalyzers can find out the presence of secret information in images with high accuracy. Increasing the embedding capacity of cover images reduces the detection risk of stego images. In this respect, we introduce boosted steganography scheme (BSS) that has a preprocessing stage before applying steganography methods. The goal of BSS is increasing the undetectability of stego images. Due to the dependence of embedding capacity of images to their content, we proposed an ensemble steganalyzer to estimate the embedding capacity of each cover image. Since the content of cover images has less significance in steganography, therefore to have more security, the steganographer... 

We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach identifies vectors that are orthogonal to the subspaces in which the training data concentrate. We study conditions on the coefficients of training data that guarantee that ideal normal vectors deduced from the dictionary are local optima of the criterion. We illustrate the behavior of the criterion on a 2D example, showing that the local minima correspond to ideal normal vectors when the number of training data is sufficient. We conclude by describing an algorithm that can be used to optimize the criterion in higher... 

In this letter, we identify a class of absolutely continuous probability distributions, and show that the differential entropy is uniformly convergent over this space under the metric of total variation distance. One of the advantages of this class is that the requirements could be readily verified for a given distribution. © 1997-2012 IEEE  

Automatic image annotation methods generate a list of tags for each test image and present it in a matrix structure. To achieve a more accurate annotation, we propose a method with the aim of correcting the tag list. In our method, we detect an indicator for each group of tags and use it to rectify the annotation results. To find a correct indicator, we apply a deep feature vector generated by the “AlexNet” model. Using this indicator, we determine the suitable tags for an image. The purposed method is independent of feature vector, dataset, and annotation method. It can be applied to the currently available annotation methods. Our experiments showed improvement in all annotation methods... 

In this paper, we introduce a new measure of correlation for bipartite quantum states. This measure depends on a parameter α , and is defined in terms of vector-valued Lp -norms. The measure is within a constant of the exponential of α -Rényi mutual information, and reduces to the trace norm (total variation distance) for α =1. We will prove some decoupling type theorems in terms of this measure of correlation, and present some applications in privacy amplification as well as in bounding the random coding exponents. In particular, we establish a bound on the secrecy exponent of the wiretap channel (under the total variation metric) in terms of the α -Rényi mutual information according to... 

In this paper, the Conley index theory is used to examine the Poincaré index of an isolated invariant set. Some limiting conditions on a critical point of a planar vector field are obtained to be an isolated invariant set. As a result, the existence of infinitely many homoclinic orbits for a critical point with the Poincaré index greater than one is shown. © Sharif University of Technology  

The paper presents the vector model of the Brushless Doubly-Fed Machine (BDFM) in the rotor flux oriented reference frame. The rotor flux oriented reference frame is well known in the standard AC machines analysis and control. Similar benefits can be sought by employing this method for the BDFM The vector model is implemented in MATLAB/SIVIULINK to simulate the BDFM dynamic performance under different operating conditions. The predictions from the vector model are compared to those from the coupled circuit model in simulation. The results are shown for the cascade mode of operation. © 2008 IEEE  

This paper concerns an understanding of thrust vectoring, in the presence of co-flowing secondary air. An experimental and computational study was performed on a circular-shape nozzle, integrated with a Coanda surface, to investigate the feasibility of multi-directional deflection of the engine exhaust gases. The vectoring angle was controlled by the relative blowing momentum of air that was injected tangentially through the slots, surrounding the main nozzle. A state of the art design that uses a small step, just after the main nozzle to turn the flow into eddy, was also employed to enhance the effectiveness of the method. The secondary section was divided into four identical sections for... 

The size and complexity of modern power systems necessitate the use of Frequency Dependent Network Equivalents (FDNE) for part of the system. In order to give realistic simulation the frequency response of the equivalent must mach that of the system it represents. In this paper a method for developing FDNE is presented and demonstrated by approximation its admittance matrix Y by rational function in the frequency domain based on vector fitting method. The developed FDNE is accurate and efficient. An example showing the accuracy and efficiency of the developed FDNE when used to reduce a radial network is presented. ©2006 IEEE  

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from... 

In oil and gas fields, it is a common practice to flow liquid and gas mixtures through choke valves. In general, different types of primary valves are employed to control pressure and flow rate when the producing well directs the natural gas to the processing equipment. In this case, the valve normally is affected by elevated levels of flow (or velocity) as well as solid materials suspended in the gas phase (e.g., fine sand and other debris). Both surface and subsurface chokes may be installed to regulate flow rates and to protect the porous medium and surface facilities from unusual pressure instabilities.In this study a reliable, novel, computer based predictive model using Least-Squares... 

Recently many works focus on the vehicle type recognition because it is important in security and authentication systems. Computational complexity and low recognition rate especially when the system has to recognize among a large number of vehicles, are two major problems in vehicle type recognition. In recent years wavelet and contourlet transform have been applied in the recognition tasks successfully. In this paper we proposed a method for recognizing vehicle type in different lighting conditions. We used wavelet and contourlet as tools for feature extraction. These features are powerful and robust to illumination and scale variation. We reduced the dimension of feature vector by resizing... 

Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally,... 

The burning of fossil fuels such as gasoline, coal, oil, natural gas in combustion reactions results in the production of carbon dioxide. The phase behavior of the carbon dioxide + water system is complex topic. Unlike methane, CO2 exhibits a minimum in the water content. These minima cannot be predicted by existing methods accurately. In this communication, two mathematical-based procedures have been proposed for accurate computation of CO2 water content for tempe-ratures between 273.15 and 348.15 K and the pressure range between 0.5 and 21 MPa. The first is based on least squares support vector machine (LSSVM) algorithm and the second applies multilayer perceptron (MLP) artificial neural... 

Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique...