Sharif Digital Repository

In this project, a ceramic biocompatible coating was applied on Titanium which is included Hydroxyapatite by AC Plasma Electrolyte Oxidation method. First, coating was performed in 5 different solutions and various duration in 500mA/Cm2 as a current density. Then, by microscopic and macroscopic images and size of porosity and also thickness of coat, 3 solutions were chosen for adding nanoparticles: Solution 1 which includes NaH2PO4 and Ca(CH3COO)2 , Solution 3 which includes Ca(CH3COO)2 and Na-Beta G and Solution 4 which includes Ca(H2PO4)2, HMP, NA2(EDTA) and Ca(CH3COO)2 . Also the time 10 minutes was chose as a appropriate time. The average of the porosities size in solution 1 was about... 

Reinforcement learning is a field of machine learning which is more similar to human training procedures.It uses reward signals to train an agent designed to act in that environment. Deep neural networks enhance the agent’s ability to determine and act better in its complex environment. Most previous works have addressed model-free agents, which ignore modeling details of the environment that in turn can be used to achieve better results. On the other hand, humans utilize a model-based approach in their decision-making process. They use their knowledge to predict the future and choose the action that leads them to a better state. To combine the benefits of model-based and model-free designs,... 

In this thesis, equations related to large deformation of a polymer were derived by considering viscoelastic effects. To express type of material behavior which includes elastoplastic and viscoelastic property at the same time, two-layer viscoplastic model was used. An axisymmetric model was used for modeling problem in finite element software. By derivation of parameters related to two layer model and entering them in finite element software and also entering other properties required to model the forming process, modeling was performed by special profile connecting input and output sections. Due to favorable properties of spline functions, profiles used in die geometry, were selected from... 

Stop-skipping and timetable synchronization in a transit network for subway operation are two effective strategies to reduce passengers' total travel time.Most of the current research on the topic does not consider stop-skipping strategy and timetable synchronization simultaneously. This paper proposes a mixed-integer programming model based on passenger smart-card data concerning the trains' capacity to minimize passengers' total waiting time and in-vehicle time and maximize the number of passengers who successfully reach their destination. Since increasing the number of trains, stations, or the study horizon, exponentially increase the size of the problem, seeking practical methods to... 

Land use and transportation are interrelated, and as a result in cities’ development plans, there is a need for a model that can consider both of them simultaneously. The model presented in this study is bi-level. The upper level designs the zonal population and employment, and transportation network link capacities. Its objective is to minimize the total cost of residence, receiving goods and services, and transportation. The lower level consists of two inter-related problems. One of these problems determines the allocation of population and employment and the other obtains link flows by a joint bi-modal mode choice assignment model. This study aims to develop a meta-heuristic algorithm to... 

The goal of this project is the development of a control algorithm for a flock of non-holonomic aerial agents. For this purpose,the swarm architecture having some unique features such as robustness, flexibility, and scalability is utilized. Swarm is defined as a group of simple agents having local interactions between themselves and the environmentwhich shows an unpredictable emergent behavior.Behavior based control which is inspired from the animal behaviors is employed to control the swarm of mobile agents. Accordingly, the necessary behaviors which are distance adjustment, velocity agreement, and virtual leader tracking together with a fuzzy coordinator are designed. In this study, in... 

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... 

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... 

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...