Sharif Digital Repository

Partial oxidation of methane (POX) is one of the most common processes to produce synthesis gas. In this study the kinetics of this process is examined and some reduced mechanisms are introduced to model it in this study. The experimental data from Research Institute of Petroleum Industry are used as reference experimental data. Inlets of this reactor are oxygen and methane with equivalence ratio between 2.53 and 2.9. Moreover, the experimental results of previous studies with the air as the oxidant in equivalence ration between 2.4 and 2.6 are used as basis for comparison of simulation results. During the first step, partial oxidation of methane in non-catalytic porous media is simulated by... 

Quality and intelligibility are two aspects of speech that are affected by various factors, such as background noise and echo. The performance of many commercial and military speech-based systems depends on at least one of these aspects of speech. Therefore, this research aims to design an improvement model to remove background noise and reverberation from the speech signal. The model training framework is based on deep learning methods and has a supervised approach in the time domain. The input of this system is the raw waveform of the speech signal mixed with noise and reverberation, and the output is the enhanced waveform of this signal. An architecture is proposed in this thesis based on... 

Sensor Networks are set of devices which are distributed throughout an environment and are connected to each other, usually wirelessly, to collect environmental information including temperature, aire pressure, moist, pollution and physiological functions of the human body. Each device consists of a microprocessor, converter and power supply, transmitter and a receiver. In this study we intend to investigate such setup and the measured signals assuming they are sparse. A sparse signal is a discrete time signal most of indices of which are equal to zero. With this assumption at hand, we will be able to reduce the sampling rate and take advantage of sparse signal processing techniques. This... 

In this study pain level estimation using facial expression is investigated. To do this, there are two approaches, one approach is sequence level pain estimation and the other one is frame level pain estimation. In sequence level, after feature extraction from all frames of sequence, each sequence is represented by a fixed length feature vector, this feature vector is constructed by concatenating min, max and mean of frame features of that specific sequence, then KLPP is applied in order to reduce feature vector dimension and in the end a linear regression is implemented to predict the pain labels of the sequence. In the frame level, two approaches are introduced, the first one is based on... 

In this thesis, a heterogeneous nanocatalyst based on graphene oxide was synthesized. To improve the graphene oxide, it was reacted with paraaminobenzoic acid and then metal salts (cobalt and manganese) were coated on it. The structure of the catalyst was confirmed by field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), quantitative X-ray diffraction (EDS) and infrared (FT-IR) spectroscopy. Finally, the catalytic properties of the catalysts were evaluated in the oxidation reaction of different alcohols in the presence of oxygen. In the presence of benzyl alcohols with lethal electron groups, the oxidation conditions are stricter and the reaction efficiency is lower.... 

The existing continual learning methods are mainly focused on fully-supervised scenarios and are still not able to take advantage of unlabeled data available in the environment. Some recent works tried to investigate semi-supervised continual learning (SSCL) settings in which the unlabeled data are available, but it is only from the same distribution as the labeled data. This assumption is still not general enough for real-world applications and restricts the utilization of unsupervised data. In this work, we introduce Open-Set Semi-Supervised Continual Learning (OSSCL), a more realistic semi-supervised continual learning setting in which out-of-distribution (OoD) unlabeled samples in the...