Sharif Digital Repository

These days, the heat transfer phenomena is an important issue in internal combustion engines design because of direct effects on the performance, efficiency and emission of engine. One of the main parts is cylinder head which contacts with hot gases in combustion chamber and expose high heat flux. In this thesis, cylinder head of Tiba engine modeled by three dimensional and transient thermal analysis. In this modeling in order to consideration the effect of fluid flow, different domain of cylinder head (water jacket, intake/exhaust ports) has been simulated and appended to the model. Also combustion phenomena modeled separately, and then the heat transfer coefficient in the surface of... 

Wind power is site dependent and is by nature partially dispatchable. Furthermore, good wind sites are far from grid. Due to these problems, and along with the existing limitations in the transmission networks, a comprehensive analysis over an extended time is needed to properly explore all potential wind sites for wind capacity allocation. This problem is computationally expensive and decomposition methods are required to break down this problem. Here Benders decomposition approach is used, which is a popular technique for solving large-scale problems, to decompose the original problem into a master and a subproblem. The master problem is a linear problem, which allocates wind capacity to... 

Single nucleotide polymorphs, There are changes caused by a mutation in a nucleotide in the Dena sequence. Mononucleotide polymorphisms are the most common type of genetic variation. Some of these changes have little or no effect on cells, while others cause significant changes in the expression of cell genes that can lead to disease or resistance to certain diseases. Because of the importance of these changes and their effect on cell function, the relationships between these changes are also important. Over the past decade, thousands of single disease-related mononucleotide polymorphisms have been identified in genome-related studies. Studies in this field have shown that the expression of... 

In the past few years, many research works on the controllable integration of metal nanoparticles and metal-organic frameworks were done, since the obtained composite material shows a synergism effect in catalysis and photocatalysis, drug delivery applications, gas, and energy storage, as well as sensing. For the first time, in this study, we employed template-assisted growth to synthesize Au-HKUST-1 Nanocomposite. XRD analysis entirely confirms that employing this strategy in synthesizing Au-HKUST-1 was wholly successful, and the plasmonic properties of this nanostructure were studied via UV-visible spectroscopy. In the course of synthesis, gold nanoparticles with 70nm diameter were... 

Age-related macular degeneration (AMD) is the most common cause of vision loss in those aged over 50. There are two main types of AMD, Wet and Dry form. Wet AMD is more severe though more treatable. There are three conventional treatments for AMD including laser therapy, surgery and intravitreal injection of anti-VEGF into the eye. Delivery of drugs to the posterior segment of the eye is still challenging and several implants and devices are currently under investigation for their ability to stimulate the retina, producing visual percepts. The application of intravitreal bevacizumab (Avastin) has expanded tremendously from the time of its introduction into ophthalmic care since 3 years ago.... 

In analyzing the structural organization of many real-world networks, identifying important nodes has been a fundamental problem. The network centrality concept deals with the assessment of the relative importance of network nodes based on specific criteria. Central nodes can play significant roles on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. High computational cost and the requirement of full knowledge about the network topology are the most significant obstacles for applying the general concept of network centrality to large real-world social... 

Decision making often has different effects and results with unequal importance. Most of classifiers try to minimize the rate of misclassified instances. These classifiers assume equal costs for different misclassification types. However, this assumption is not true in many real world problems and different misclassification types have different costs. These differences can be applied by introducing the cost in the process of learning. In this manner, total cost of misclassification will be the evaluation metric of classification. In order to apply this metric to the problems, new learning algorithms are needed. Cost-sensitive learning is the related area of machine learning which deals with... 

Traditional classification techniques of machine learning assume that data have stationary distributions. This assumption for recent challenges where tremendous amount of data are generated at unprecedented rates with evolving patterns, is not true anymore. Classification of data streams has become an important area of machine learning, as the number of applications facing these challenges increases. Examples of such data streams applications include text streams, surveillance video streams, credit card fraud detection, market basket analysis, information filtering, computer security, etc. An appropriate method for such problems should adapt to drifting concepts by revising and refining the... 

Classification is one of the most important problems in machine learning area. Reliable and successful classification is essential for diagnosing patients for further treatment. In many applications such as bioinformatics unlabeled data is abundant and available. However labeling data is much more difficult and expensive to obtain. This dissertation presents a novel transductive approach for the development of robust microarray data classification. The transduction problem is to estimate the value of classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification method at all possible values and... 

Concept drift is a challenging problem in the context of data stream processing. As a result of increasing applications of data streams, including network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous studies have been conducted in the field of concept drift detection. In order to solve the problem of concept drift detection, an ideal method should be able to quickly and correctly identify a variety of changes, adapt quickly to new concepts, in the presence of limitations of memory and processing power. In this thesis, a new explicit concept drift detection method based on ensemble classifiers has been proposed for data... 

The rapid growth of broadband wireless access (BWA) has increased the demand of new  application  such  as  VoIP,  video  conferencing,  online  gaming  each  of  which  has  different requirement for quality of service. Due to limited bandwidth provided for these networks,  one  of  the  most  important  issues  is  how  effective  we  manage  bandwidth  in  order to support requests. The quality of service is an important indicator of the effective management  of  bandwidth.  Using  mechanisms  of  call  admission  control is  a  commonly  accepted method for balance between quality of service and increase of utilization resource  in  cellular  mobile  networks.  In  fact, ... 

Nowadays, with the increasing size of data,it’s impossible to collect data and fast classification by human, and needs for an automated classification and data analysis, is more interested. Data classification is a process of giving the training data along with their class labels to the learning agent, which learns the relation between the instances and the labels. Then make a prediction to the label of the training data.In this thesis we will observe the classification of the multi-label data. Multi-label data have more than one label. In other words, each instance appears with a vector of labels.In this thesis, a method based on nearest neighbor is proposed to classify the multi-label...