Sharif Digital Repository

The prevention of combustion instability is unquestionably the greatest technical challenge and can be a significant cost driver in new engine development programs. In order to meet challenge, a predictive stability code is required. The main objective of this work is to develop a mechanistic engineering code for analysis of high frequency combustion instability in LRE. The developed MEchanistic Combustion Stability COmputer Program (MECSCOP), evaluates combustion stability using the transfer function approach, whereby the ability of the chamber to dissipate acoustic oscillations is compared to the oscillatory combustion driving mechanisms. The driving mechanisms are classified by the source... 

Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available that is not feasible in many cases. In this article, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method, community diffusion (CoDi), does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in... 

Squeak and rattle (S&R) noises are important in-cabin sources of annoyance for occupants. Originally, S&R is generated as a result of colliding and slamming of car's trim and body structure which in turn occurs because of the dynamic displacements of components excited by road and power-train. Squeak noise is interpreted as periodic stick and slip phenomena in the contact boundary of two neighbor surfaces. While rattle noise is the emitted noise when adjacent surfaces collide and impact. Squeak noise takes place at low frequency while rattle phenomenon happens in high-frequency range, even though the excitation sources (road and power-train line) works in low frequency range. In practice,... 

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known... 

Since compressive sensing utilizes a random matrix to map the sparse signal space to a lower dimensional transform domain, it may be possible to apply this matrix at the same time for encrypting the signal opportunistically. In this paper, a compressed sensing based encryption method is considered and the secrecy of measurement matrix of compressive sensing is analysed from the detection theory perspective. Here, the detection probability of intended and unintended receivers are compared by applying the Neyman-Pearson test. We prove that the detection probability of eavesdropper will be reduced significantly because he does not know the transform domain subspace. Furthermore, in some... 

These days, the pace of utilizing CAE tools is increasing with an eye to the vehicle development time and cost considerations. This paper deals with the NVH virtual analyses performed for a trimmed body of a B-segment sedan car. Since the main source of annoyance in low frequency range is the booming noise, the NVH diagnoses are performed by FEM tools. Firstly, the finite element model of the car is generated and this is followed by the modal analysis simulations for the car and cavity models in order to investigate the frequency attributes of the car and identify the NVH characteristics of body structure and acoustic cavity. Afterwards, noise transfer function (NTF) analyses are performed... 

Differentiating between real and fake news propagation through online social networks is an important issue in many applications. The time gap between the news release time and detection of its label is a significant step towards broadcasting the real information and avoiding the fake. Therefore, one of the challenging tasks in this area is to identify fake and real news in early stages of propagation. However, there is a tradeoff between minimizing the time gap and maximizing accuracy. Despite recent efforts in detection of fake news, there has been no significant work that explicitly incorporates early detection in its model. The proposed method utilizes recurrent neural networks with a... 

Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks does not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. To jointly discover omitted diffusion activities and hidden network structures, we develop a probabilistic generative model called "DiffStru."The interrelations among links of... 

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence... 

Background: In line with the advancement of Artificial Intelligence (AI), innovative solutions have been designed to improve health-related Sustainable Development Goals (SDGs). Accordingly, there is an increasing trend in the realm of AI and SDG research areas. Objectives: This study aimed to analyze the trends and patterns of AI research in health-related SDGs using bibliometric analysis. Methods: The bibliometric approach facilitated the identification of key terms and countries from previous research. We used VOSviewer to map and analyze data obtained from three databases: Scopus, Web of Science, and PubMed. Results: Our findings illustrated that research on health has been a popular... 

This paper investigated the headway distribution of platooning vehicles in work zones with and without automated Speed Photo Enforcement (SPE). Data from two work zones were collected and analyzed. The portion of vehicles traveling with a very short headway (less than 0.7 sec) was also examined. In addition, the behavior of vehicles near the law enforcement vehicles was analyzed in terms of applying brakes and changing lane in one of the work zones. The results indicated that the mean headway of cars in the median lane significantly increased in both work zones when the SPE van was present compared to the base condition. For trucks, the mean headway increase was significant only in the... 

In this paper, the concept of hypo-elasticity is generalized to the micropolar continuum theory, and the general forms of the constitutive equations of the micropolar hypo-elastic materials are presented. A new co-rotational objective rate whose spin is the micropolar gyration tensor is introduced which describes the deformation of the material in view of an observer attached to the micro-structure. As special case, simplified versions of the proposed constitutive equations are given in which the same fourth-order elasticity tensors are used as in the micropolar linear elasticity. A 2-D finite element formulation for large elastic deformation of micropolar hypo-elastic media based on the... 

Cantilever arrays with nearest-neighbor interactions are considered to obtain the pull-in parameters. The interactions between the neighboring beams are a combination of the Casimir force and the electrostatic force with the first-order fringing field correction. A set of coupled nonlinear boundary value problems and a set of coupled nonlinear equations arise in the distributed and lumped parameter modeling of the array, respectively. The models are simulated numerically to obtain the pull-in parameters of the arrays with different number of beams. The pull-in parameters of large arrays converge to constant values, which are independent of the number of beams in the array. The constants... 

In this study we compare five classification methods for detecting activation in fMRI data: Fisher linear discriminant, support vector machine, Gaussian nave Bayes, correlation analysis and k-nearest neighbor classifier. In order to enhance classifiers performance a variety of data preprocessing steps were employed. The results show that although kNN and linear SVM can classify active and nonactive voxels with less than 1.2% error, careful preprocessing of the data, including dimensionality reduction, outlier elimination, and denoising are important factors in overall classification  

In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher Linear Discriminant, Gaussian naïve Bayes, Multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the 1D and 2D adjacency histograms, center of mass of the histogram characteristic function and co-occurrence matrices for feature extraction process. In order to reduce the proposed features dimension and select the best subset, genetic algorithm is used and the results are compared through principle component analysis and linear discriminant... 

A new adaptive steganography method based on image contrast to improve the embedding capacity and imperceptibility of the stego images is presented. The method exploits the average difference between the gray level values of the pixels in 2x2 blocks of non-overlapping spatially and their mean gray level in order to select valid blocks for embedding. The method was tested on different gray scale images. Results show that our proposed approach provides larger embedding capacity, while being less detectable by steganalysis methods such as χ2 attack and machine learning steganalysis systems, as compared to some well-known adaptive and non-adaptive steganography algorithms  

The multigrid method is one of the most efficient techniques for convergence acceleration of iterative methods. In this method, a grid coarsening algorithm is required. Here, an agglomeration scheme is introduced, which is applicable in both cell-center and cell-vertex 2 and 3D discretizations. A new implicit formulation is presented, which results in better computation efficiency, when added to the multigrid scheme. A few simple procedures are also proposed and applied to provide even higher convergence acceleration. The Euler equations are solved on an unstructured grid around standard transonic configurations to validate the algorithm and to assess its superiority to conventional explicit... 

The structural instability of an array of cantilevers, each of which interacts with two neighbouring beams through electrostatic and van der Waals forces, is studied. Distributed and lumped parameter modelling of the array result in a set of coupled nonlinear boundary value problems and a set of coupled nonlinear equations, respectively. These coupled nonlinear systems are solved numerically for different numbers of beams in the array to obtain the pull-in parameters. The pull-in parameters converge to constant values with an increase in the number of beams in the array. These constants, which are important in the design of cantilever arrays, are compared for the distributed and lumped... 

In this paper, the concept of energy pairs in the micropolar continuum is introduced. A brief review of the micropolar continuum theory is presented for using in the subsequent derivations. A mathematical Lagrangian strain and a wryness tensor for the micropolar continuum are introduced. Using the first law of thermodynamics and for isothermal processes, the power of deformation is obtained and the energy pairs in the Eulerian and Lagrangian descriptions are defined. Also, the micropolar stress and couple stress tensors which are energy pairs to the micropolar Lagrangian strain and wryness measures are determined. © 2006 Elsevier Ltd. All rights reserved  

In this paper, the large amplitude free vibration of a cantilever Timoshenko beam is considered. To this end, first Hamilton's principle is used in deriving the partial differential equation of the beam response under the mentioned conditions. Then, implementing the Galerkin's method the partial differential equation is converted to an ordinary nonlinear differential equation. Finally, the method of multiple scales is used to determine a second order perturbation solution for the obtained ODE. The results show that nonlinearity acts in the direction of increasing the natural frequency of the thick-cantilevered beam