Sharif Digital Repository

The production of agricultural products is one of the most important human economic activities. The issue of mechanized and optimal use of pesticides is vital for human health and the environment, and the use of helicopters makes this possible. Although spray-based systems in helicopters are one of the most effective ways to produce agricultural products, it is still unclear how droplet movement in aerial spraying is affected by the complex downwash flow created by rotors. Modeling agricultural air spray to identify the spray trend of droplets in the air stream, downwash flows, and consequent vortices has attracted more attention as a result of the development of computational fluid... 

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... 

The development of smart meters enables gathering and analysis of a large amount of data about electrical energy consumption in electric power distribution systems. This data and the obtained behavioral patterns of customers have a wide variety of applications. To name a few, classification of customers based on their consumption patterns, damaged smart meter identification, non-technical loss identification and measuring participation rate of customers in demand response programs are among the applications. So far, many studies have been done for consumption pattern identification. However, abnormality detection in electric energy consumption has captured growing attention due to the... 

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... 

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... 

Soft manipulators mounted on free-floating spacecraft exhibit highly nonlinear and coupled behaviors due to the strong dynamic interaction between the manipulator and the rigid base, which makes accurate modeling and controller design a challenging problem. These challenges become more pronounced with increasing degrees of freedom, continuous deformation of the manipulator, and the non-Euclidean nature of the configuration space. Consequently, the development of modeling and control frameworks that can systematically span from simplified models to fully three-dimensional descriptions of soft space manipulators is essential for practical applications in orbital environments. In this... 

One of the most important problems in online learning is a prediction with expert advice. In each step we make our prediction not only based on previous observation but also use expert information. In this thesis, we study the different well-known algorithms of expert advice and generalize problems when data arrival is in the two-dimensional grid. regret is a well-studied concept to evaluate online learning algorithm. online algorithm when data arrive consecutively in T time step has regret O (√(T)) . regret in two-dimensional grid with T row and P column is O(T√(P)).
2010 MSC: 68Q32 ; 68T05 ; 90C27