Modeling of Fluid Flow and Heat Transfer at the Entrance Zone of a Partially Filled Porous Channel

, M.Sc. Thesis Sharif University of Technology Inanloo, Saeed (Author) ; Nouri Borujerdi, Ali (Supervisor)

Abstract

This paper numerically studies the convection heat transfer enhancement of a developing two dimensional laminar flow in a pipe partially filled with porous materials. One of the most important effects of the systems with porous materials in them, is that they can improve some heat transfer components if they be used in a proper way. This study has been performed under both local thermal equilibrium (LTE) and local thermal non-equilibrium (LTNE) conditions. Two energy equations are used in non-thermal equilibrium condition between fluid and porous material. Darcy-Brinkman-Forchheimer model is used to model the flow inside the porous medium. The effects of different parameters such as, Darcy...

Test case prioritization using test case diversification and fault-proneness estimations

, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) Mahdieh, M ; Mirian Hosseinabadi, S. H ; Mahdieh, M ; Sharif University of Technology

Springer 2022

Abstract

Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we...

Electronic structure of Ni-doped BaFe2−xNixAs2 (x = 0, 1, 2) superconductor in the nonmagnetic and magnetic states

, Article Journal of Superconductivity and Novel Magnetism ; Volume 28, Issue 8 , 2015 , Pages 2365-2371 ; 15571939 (ISSN) Inanloo, Z ; Khosroabadi, H ; Abolhassani, M. R ; Sharif University of Technology

Springer New York LLC 2015

Abstract

The spin configuration, equilibrium crystal structure, and electronic structure of BaFe2−xNixAs2 (x = 0, 1, 2) have been investigated by using ab initio pseudopotential Quantum Espresso code in the generalized gradient approximation. The total energy of different Fe(Ni) spin configurations has been calculated to determine the ground state in each doping. The results show an antiferromagnetic order in x = 0.0 and a nonmagnetic state for x = 2.0. Equilibrium lattice and internal parameters for this system have been calculated and compared with the literature data. This study shows that the lattice parameters in the magnetic calculations have been...

Increasing the Life-time of Wireless Sensor Networks Using Data Prediction

, M.Sc. Thesis Sharif University of Technology Inanloo, Mahdieh (Author) ; Hemmatyar, Afshin (Supervisor)

Abstract

Wireless sensor networks (WSNs) can be used in a variety of applications. The prime shortcoming of these networks is their energy constraint. The main energy consumer in a sensor node is its radio transmitter. Therefore data prediction is one of the most effective methods to reduce the data transmission rate. By data prediction, a large amount of energy is saved; which results in the longevity of the network life. Environmental variations almost have similar effects on different sensor sources in a sensor device. So, considering the correlation between different sources reduces the noise impact and increases data prediction accuracy. In this thesis, we use temporal and multisource...

First-principle electronic structure study of Ni-doped BaFe2-xNixAs2 (x = 0, 1, 2) superconductor

, Article Physica C: Superconductivity and its Applications ; Vol. 506, issue , 2014 , p. 151-153 Inanloo, Z ; Khosroabadi, H ; Abolhassani, M. R ; Akhavan, M ; Sharif University of Technology

2014

Abstract

The electronic structure of BaFe2-xNixAs2 (x = 0, 1, 2) as a function of Ni doping has been investigated. Electronic density of states and the band structures are calculated within the first-principle density functional theory for non-magnetic phase. Pseudopotential quantum espresso code in the generalized gradient approximation has been used. Lattice and ionic position parameters of the system have been taken from the experimental data and have been optimized to find the equilibrium structure parameters. The electronic structure is characterized by a sharp Fe/Ni3d peak close to the Fermi level and is dominated by Fe/Ni3d and As4p hybridized states similar to the other Fe-based...

A new approach for multi-source data prediction in wireless sensor networks: Collaborative filtering

, Article 2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012 ; 2012 ; 9781467358293 (ISBN) Inanloo, M ; Ashouri, M ; Gheibi, S ; Hemmatyar, A. M. A ; Sharif University of Technology

2012

Abstract

The prime shortcoming of Wireless Sensor Networks (WSNs) is their energy constraint. The main energy consumer in a sensor node is its radio transmitter. One of the most effective methods to reduce the data transmission rate is data prediction. By data prediction, the amount of transmitted data is reduced; which results in energy saving and the longevity of the network life. Environmental variations almost have similar effects on different sensor sources in a sensor device. So, considering the correlation between different sources reduces the noise impact and increases data prediction accuracy. In this paper, temporal and multi-source correlations are used, to reduce data transmission in...

Deep Zero-shot Learning

, M.Sc. Thesis Sharif University of Technology Shojaee, Mohsen (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. On the other hand, with recent advances made by deep neural networks in computer vision, a rich representation can be obtained from images that discriminates different categorizes and therefore obtaining a unsupervised information from images is made possible. However, in the previous works, little attention has been paid to using such unsupervised information for the task of zero-shot learning. In this...

Multi-Modal Distance Metric Learning

, M.Sc. Thesis Sharif University of Technology Roostaiyan, Mahdi (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for...

Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold

, M.Sc. Thesis Sharif University of Technology Mahdieh, Zahra (Author) ; Bagheri, Reza (Supervisor)

Abstract

This project aimed to fabricate the bone scaffolds with applying thermoplastic starch-based nano-biocomposites. The starting materials for this scaffold are as follows: thermoplastic starch, ethylene vinyl alcohol as the polymer matrix and nanoforsterite as the ceramic reinforcing phase. Furthermore, vitamin E was used as antioxidant for preserving starch against thermo-mechanical degradations. Likewise, 3D pore structure was developed using azo-dicarbonamide and water in injection moulding process. With blending thermoplastic starch and ethylene vinyl alcohol, some thermoplastic starch’s properties including degradation rate and water absorption were modified. In addition, having...

Unsupervised Domain Adaptation via Representation Learning

, M.Sc. Thesis Sharif University of Technology Gheisary, Marzieh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these...

Deep Learning for Multimodal Data

, M.Sc. Thesis Sharif University of Technology Rastegar, Sarah (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Recent advances in data recording has lead to different modalities like text, image, audio and video. Images are annotated and audio accompanies video. Because of distinct modality statistical properties, shallow methods have been unsuccessful in finding a shared representation which maintains the most information about different modalities. Recently, deep networks have been used for extracting high-level representations for multimodal data. In previous methods, for each modality, one modality-specific network was learned. Thus, high-level representations for different modalities were extracted. Since these high-level representations have less difference than raw modalities, a shared...

Deep Learning For Recommender Systems

, M.Sc. Thesis Sharif University of Technology Abbasi, Omid (Author) ; Soleimani, Mahdieh (Supervisor)

Abstract

Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to...

Adversarial Networks for Sequence Generation

, M.Sc. Thesis Sharif University of Technology Montahaei, Ehsan (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Lots of essential structures can be modeled as sequences and sequences can be utilized to model the structures like molecules, graphs and music notes. On the other hand, generating meaningful and new sequences is an important and practical problem in different applications. Natural language translation and drug discovery are examples of sequence generation problem. However, there are substantial challenges in sequence generation problem. Discrete spaces of the sequence and challenge of the proper objective function can be pointed out.On the other, the baseline methods suffer from issues like exposure bias between training and test time, and the ill-defined objective function. So, the...

Improving Sampling Efficiency of Probabilistic Graphical Models

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mohsen (Author) ; Beigy, Hamid (Supervisor)

Abstract

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...

Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mostafa (Author) ; Rabiee, Hamid Reza (Supervisor)

Abstract

Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an...

Multi-label Classification by Considering Label Dependencies

, M.Sc. Thesis Sharif University of Technology Farahnak Ghazani, Fatemeh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In multi-label classification problems each instance can simultaneously have multiple labels. In these problems, in addition to the complexities of the input feature space we encounter the complexities of output label space. In the multi-label classification problems, there are dependencies between different labels that need to be considered. Since the dimensionality of the label space in real-world applications can be (very) high, most methods which explicitly model these dependencies are ineffective in practice and recently those methods that transform the label space into a latent space have received attention. A class of these methods which uses output space dimension reduction, first...

Adaptation for Evolving Domains

, M.Sc. Thesis Sharif University of Technology Bitarafan, Adeleh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify...

Cancer Prediction Using cfDNA Methylation Patterns With Deep Learning Approach

, M.Sc. Thesis Sharif University of Technology Mahdavi, Fatemeh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Liquid biopsy includes information about the progress of the tumor, the effectiveness of the treatment and the possibility of tumor metastasis. This type of biopsy obtains this information by doing diagnosis and enumerating genetic variations in cells and cell-free DNA (cfDNA). Only a small fraction of cfDNA which might be free circulation tumor DNA (ctDNA) fragments, has mutations and is usually identified by epigenetic variations. On the other hand, the use of liquid biopsy has decreased, and tumors in the final stages are often untreatable due to the low accuracy in prediction of cancer. In this research, the aim is to predict cancer using cfDNA methylation patterns. We obtain these...

Answering Questions about Image Contents by Deep Networks

, M.Sc. Thesis Sharif University of Technology Chavoshian, Mohammad (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Due to the recent advances in the learning of multimodal data, humans tend to use computer systems in order to solve more complex problems. One of them is Visual Question Answering (VQA), where the goal is finding the answer of a question asked about the visual contents of a given image. This is an interdisciplinary problem between the areas of Computer Vision, Natural Language Processing and Reasoning. Because of recent achievements of Deep Neural Networks in these areas, recent works used them to address the VQA task. In this thesis, three different methods have been proposed which adding each of them to existing solutions to the VQA problem can improve their results. First method tries to...

Conditional Text Generation with Neural Networks

, M.Sc. Thesis Sharif University of Technology Ali Hosseini, Danial (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

By the improvement of machine learning methods specially the Deep Learning in the last decade, there were expanding usage of these methods in Language Modeling task. As the essence of a language model is more basic, recently huge networks are trained with language model objective but fine-tuned on target tasks such as Question Answering, Sentiment Analysis and etc. which is a promising sign of its importance and usage in even other NLP tasks. However, this task still has severe problems. The Teacher Forcing based methods, suffer from the so-called exposure bias problem which is due to the train/test procedure discrepancy. Some solutions such as using Reinforcement Learning which has high...