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The Application of Fuzzy Customer Clustering Results in Distribution Fleet Assignment to Customer Demands Considering Sales and Distribution Simultaneously
, M.Sc. Thesis Sharif University of Technology ; Haji, Alireza (Supervisor)Test case prioritization using test case diversification and fault-proneness estimations
, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) ; 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...
Airport Capacity Determination by Fuzzy Logic
, M.Sc. Thesis Sharif University of Technology ; Malaek, Mohammad Bagher (Supervisor)
Abstract
An automatic air traffic controller system is designed in this research. The major goal is to aid the human controller in decision making at real air traffic control situations. The air traffic system is modeled by queuing theory. Since respond rapidity is very important in air traffic control, so the fuzzy logic is utilized to construct a rule-base. This rule-base determines optimum policies while considering the operational matters, minimum separation distance requirements, air traffic controller techniques, and work-load. This project is an initial design for air traffic automation system which considers scheduling optimality as well as delay reduction. The case study is done on Denver...
Reaction Modeling of Mercaptan Conversion to H2S and Hydrocarbons over ZSM-5 Catalysts in Microreactor
, M.Sc. Thesis Sharif University of Technology ; Kazemeini, Mohammad (Supervisor)
Abstract
Microfluidic devices carry fluids in channels with the diameter in the scale of micrometers. Low consumption of raw materials, higher selectivity, enhancement of heat and mass transfer, and improved safety considerations are benefits of the application of microfluidic devices. In this venue, catalytic microreactors get high potential in the filed of the catalytic reaction. In recent years due to strict international environmental regulations, the sulfur content in fossil fuels should be at the minimum content. MEROX process is to most common route to remove mercaptan from the LPG dreams in the material gas refineries. However, in this process, the production of DiSulfid Oil (DSO) is a...
Providing a Model for Formulation of Technological Strategy in the Field of Hybrid & Electric Vehicles in Iran Khodro Car Maker Company
, M.Sc. Thesis Sharif University of Technology ; Arasti, Mohammad Reza (Supervisor)
Abstract
Companies are facing increasingly faster pace of technological evolution. Running a business with a large supply chain without considering the technological changes in the environment might provoke a difficult situation and result in competitive disadvantage. The field of hybrid and electric vehicles has developed a lot in the last decade in the world and the technologies of this field are developing in a dynamic circumstance. Iran Khodro, as the largest automaker in Iran, cannot be indifferent to this reality. Despite the huge oil and gas resources in the country, is it necessary to develop hybrid and electric vehicles in Iran? This is the main question we are trying to answer in the first...
Numerical simulation of vortex-induced vibration of a smooth circular cylinder at the subcritical regime
, Article Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment ; Volume 236, Issue 4 , 2022 , Pages 916-937 ; 14750902 (ISSN) ; Nemati Kourabbasloo, N ; Mohtat, P ; Tanha, A ; Sharif University of Technology
SAGE Publications Ltd
2022
Abstract
The present paper focuses on the simulation of vortex-induced vibration (VIV) of a rigid, smooth circular cylinder with elastic supports subject to a cross-flow at the subcritical regime of Reynolds number, 30,000
Deep Zero-shot Learning
, M.Sc. Thesis Sharif University of Technology ; 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 ; 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 ; 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 ; 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 ; 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 ; 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 ; 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 ; 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 ; 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...
Implementation of absolute quantification in small-animal SPECT imaging: Phantom and animal studies
, Article Journal of Applied Clinical Medical Physics ; Volume 18, Issue 4 , 2017 , Pages 215-223 ; 15269914 (ISSN) ; Vosoughi, N ; Tanha, K ; Assadi, M ; Ghafarian, P ; Rahmim, A ; Ay, M. R
John Wiley and Sons Ltd
2017
Abstract
Purpose: Presence of photon attenuation severely challenges quantitative accuracy in single-photon emission computed tomography (SPECT) imaging. Subsequently, various attenuation correction methods have been developed to compensate for this degradation. The present study aims to implement an attenuation correction method and then to evaluate quantification accuracy of attenuation correction in small-animal SPECT imaging. Methods: Images were reconstructed using an iterative reconstruction method based on the maximum-likelihood expectation maximization (MLEM) algorithm including resolution recovery. This was implemented in our designed dedicated small-animal SPECT (HiReSPECT) system. For...
An Approximation Method for Reliability Analysis of Standby Systems
, M.Sc. Thesis Sharif University of Technology ; Eshraghniaye Jahromi, Abdolhamid (Supervisor)
Abstract
Analyzing reliability of large cold-standby systems has been a complicated and time-consuming task, especially for systems with components having non-exponential time-to-failure distributions. In this research, an approximation model, which is based on the central limit theorem, is presented for the reliability analysis of binary cold-standby systems whit non-identical components. The proposed model can estimate the reliability of large cold-standby systems with binary-state components having arbitrary time-to-failure distributions in an efficient and easy way. The accuracy and efficiency of the proposed method are illustrated using several different types of distributions, such as...
Multi-label Classification by Considering Label Dependencies
, M.Sc. Thesis Sharif University of Technology ; 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 ; 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 ; 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...