Sharif Digital Repository

Tehran possesses various environmental crises due to excessive population growth, a huge increase of vehicles and heavy concentrated industries. One of the most important concern is air pollution. Spectral Analysis by discrete Fourier transform are described and applied to harmonic analysis of time series for detecting Present periodicities.
The current work proposes an approach for the determine the contribution of different frequencies to the data variance using air quality measured data. In this research, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. Because of The air quality data in Tehran have irregular sampling periods and... 

Implying non-model based approach and using seasonal data, we determined cyclical component of housing market and discussed about its lagging or leading behavior to overall economic business cycles in Iran. To extract cyclical component we applied band pass filters, including Hodrick-Prescot, Baxter- King, Butterworth and Christiano-Fitzjerald, and Bry-Boschan’s algorithm on any time series which is able to explain housing market’s behavior in aggregate level. After examining many criteria we found that real residential investment in urban cycles depict cyclical behavior of housing investment. Real residential investment’ cycles in urban lag monetary base rate cycles and m1 rate cycles which... 

Human as an important part of the natural environment and can exert their positive or negative effects in the form of long-term and short-term on a natural environment. Human impacts on the natural system are very complex and consist of various components. The most considerable among them from past to now are maybe land use and land cover change, although, the impact of dam construction, water pollution, air pollution and etc, can not be neglected. To quantify the impact of these changes, many researchers have studied meteorological and climatic parameters using statistical relationships, but one major problem always existed, the low spatial accuracy of meteorological data. In recent years,... 

One of the most surprising lessons of dynamical systems theory is that the phase spaces of simple nonlinear systems may contain strange attractors. For certain simple systems, such as pendulum, direct observations of the phase space and finding strange attractors are possible. But physical systems in which all the relevant dynamical variables can be simultaneously monitored are relatively scarce. Takens’ theory shows how a time-series of measurements of a single observable state can be often used to reconstruct qualitative features of the phase space of the system. In this research, using the time-series of one measurable state, an algorithm is proposed to control chaos. The approach... 

This project tries to select the inputs which really affect the change in the direction of S&P500. For this purpose, design of experiments and analysis of variance are used. T tests are carried out to calculate the statistical significance of mean differences. Experiment results indicate that the designed neural networks with the selected inputs significantly outperform the traditional logit model with respect of the number of correct predictions. Moreover, real trades are simulated using the neural network predictions in the test period and the results show that using the designed neural network can significantly increase the income.

 

Failure of mechanical parts in the industry lead to a larger system downtime and even imposing economic losses to the factory. For this Purpose, for many years, researchers have been trying to find ways to predict early failure and to prevent losses from occurring. Creation of new sciences like artificial intelligence, helped researchers in this field.In the current study, using experimental data of a set of bearings that have been tested and recorded in the Intelligent Systems Research Center, A new approach with sufficient accuracy is presented for the prediction algorithm. Among the features extracted, three features of entropy, root mean square and maximum are the most appropriate... 

This thesis firstly studies the parameters affecting P/E ratio. These parameters vary from Macroeconomics level, Economic growth and Inflation, to company level. Then this study deploys Neural Networks to predict magnitude of P/E and change direction of P/E ratio. To increase accuracy, thesis uses three different method of normalizing for Input data. Finally, results are compared to results of regression method  

Sign language is recognized by considering the combination of the hand gesture, orientation, location and patterns of hand and arm movement. These complex interactions make the character and word recognition very challenging. In this paper, with the aid of sensory gloves and multiple inertial measurement units (IMUs) we measure the shoulder and elbow joint trajectories and hand gesture to train the discriminant functions to recognize the words intended and represented by sign language. For recognition of hand gesture, the Naive Baysian classifier was used while for Eulerian angles a new time series similarity measures were computed. Different aggregation method was used to integrate temporal... 

Shortage in fossil fuels resources together with the pollution concerns have caused a systematic change in power system planners and decision makers policies to renewable energies as an alternative to produce electrical energy. However, some intrinsic features of the wind energy overshadow its profitability. Inability to predict the wind speed changes and consequently the output level of wind turbines, being an uncontrollable generation unit, and also being an intermittent unit can be accounted as the main attributes of renewable-based units. Taking into account these features, one can conclude that new challenges can be brought into existence in planning and operation issues of... 

The importance of transfer learning in image-related problems comes from its many advantages that are sometimes undeniable. Previous researches have well shown the success of transfer learning in this area using deep neural networks. However, transfer learning for time series data has not yet been done in a conventional and automated manner. The main reason for avoiding transfer learning in this domain relates to the dynamic and stochastic nature of the time series, where they show a time-varying behavior. Previous experiments have shown that transfer learning between two heterogeneous time series could harm the forecasting accuracy of a model. Therefore, in this thesis, we aim to explore... 

Learning is an essential mechanism for the survival of living things. There are different types of learning, and value learning is among the most important types. A child learns that water resolves the thirst need by repeatedly experiencing this situation. Eventually, the value of water, which has been valueless before that, increases gradually in his mind. How this concept is encoded in the brain? previous works reveal the role of different neurons and regions that are relevant to value learning. However, population analysis and dynamic modeling are less considered. Moreover, the links between different brain regions are unknown.Finding the relationship between two relevant regions of the...