Sharif Digital Repository

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... 

Feeder reconfiguration is one of the most prominent tasks for loss reduction, reliability improvement and also congestion alleviation in distribution networks. Besides improving abovementioned operational issues, during a long period of time, proper switching in appropriate times can causes a lot of economic savings. Furthermore, it can be intensified when capacitors are implemented and controlled in the network. To reach this purpose, most of the previous literature focuses merely on the power grid reconfiguration. Few of these researches have considered this problem as a dynamic one and studied that in a period of time, e.g. one day, considering fixed level of loads despite the others that... 

Due to the importance of hepatitis disease, especially hepatitis C, from public health view point, this research aims to investigate and suggest an efficient medical therapy for this disease. First, available models are analyzed and the suitable one is chosen. Three controllers namely, fixed PI, adaptive PI and exact linearization control (GLC) are designed to control the hepatitis C disease using the drug efficacy as the manipulated variabel. Controller parameters are determined by the Genetic Algorithm (GA) and their performances are evaluated using the selected model. Uncertainties have been considerd in both model structure and parameters and controllers performances are investigated in... 

Traffic classification is crucial for the execution of many security and managerial tasks in a network, such as identifying malicious data, detecting disruptive users, and preventing the passage of traffic from certain applications. Nowadays, a significant portion of traffic in computer networks is encrypted. Therefore, the classification of encrypted traffic requires the use of solutions for analyzing encrypted traffic. In recent years, many solutions based on machine learning and deep learning have been proposed for analyzing encrypted traffic. While most of these solutions focus on improving the accuracy of network traffic classification, less attention has been paid to their... 

In this thesis, combustion of liquid fuel spray under the condition of hot and diluted oxidizer, has been investigated. To this end, a novel type laboratory-scale test rig was designed to study the physics governing the MILD-Spray combustion conditions with an applied approach. The underlined test rig eliminates the operational problems of its predecessor test stands and is used for a heavier fuel with much more complex chemical composition (kerosene). The test section is axially symmetrical, in which the fuel is injected by a pressure-swirl atomizer in the direction of hot and diluted co-flowing air. With the use of the aforesaid test rig, the effect of variables such as oxygen...