Sharif Digital Repository

Synchronous machines is one of the most popular machines in industry and in power plants. Because the dynamic of this machine is nonlinear, it can exhibit complex behaviors. In this project we have studied a special case in Permanent Magnet Synchronous Machines. A case in which speed of rotor, frequency, currents amplitude and voltages amplitude change in an irregular manner. This special case is called chaos. Chaos occurs for some specific values of parameters and inputs. In this study in the first step, we have tried to find the chaotic area of parameters of system (Prediction of chaos). Then in the next step, we have designed some controllers which can eliminate chaos in the system. The... 

In this thesis, based on an idea of A. Daneshgar [On mathematical foundations of soft computing, 11th IFSC, Zahedan, Iran(2011).], we propose a general framework to model computation which is based on discrete dynamical systems. In this regard, we formulate and justify local property as the key concept characterizing computation machines within this framework. We show that our model covers most known computation machines as classical and BSS models, Petri nets, cellular automata, Markov chains and etc. . We also study the computational power of our machine based on a variant of restrictions imposed on its different components, as its memory, or its control, and we suggest that such a... 

With the expansion of network equipment including routers and wireless modems, these equipment have become more susceptible to backdoors and hacker attacks. One way to cause troubles to these systems is through backdoor insertion. Recently, several attacks via backdoor insertions have been reported by some of the major router companies. In this project, a complete description of routers, ways of intrusion to routers and how to change settings in routers in order to measure traffic intensity are explained. In this thesis we manually setup a backdoor in a router in order to observe its effects on local networks. Furthermore, we purposely attack a router in order to find the followings: 1. Ways... 

In this thesis, we have discussed image classification by sparse representation. Sparse representation is used in two different ways for image classification. The first goal of sparse representation is to make an efficient classifier, that can learn the subspace, in which the data lies. In this field we have surveyed various methods. We also proposed a method, called ”Locality Preserving Dictionary Learning” that works approximately better than state of the art similar methods, specially when training data is limited. We have reported the result of lassification on four datasets including MNIST, USPS, COIL2 and ISOLET. Another use of sparse representation, is to extract local features from... 

Transfer Learning is one of the most important fields in the Machine Learning area. Respect to the advances that we have seen in the Computer Science, especially in the Machine Learning area, we need a tool that can transfer learnings from different domains to each other. As data distribution varies, many statistical models require restructuring using new training data. In many applications, re-assembling training data and re-structuring models is inefficient and costly, so reducing the need for this practice seems appropriate. In these cases, knowledge transfer or learning transfer between domains may be desirable. For example, in the area of the B rain-Computer Interface, when it... 

Nowadays, using different types of data has shown significant impacts on analyzing the related systems. Growth in data volume, systems complexity and existence of error and obscurity in collecting the data, increased the necessity of inventing new data analysis methods. Location-based data is an important data type for such analyses which are collected from sensors in different places. These data besides other official organization's information like municipality or Google … provide us a bulk volume of raw data. Such collections of raw data are mostly diverse, heterogeneous, bulk and outspread. Inspite of that, raw data with machine learning algorithms lead to considerable practical...