Sharif Digital Repository

Gamma ray spectroscopy has a special place in the industrial applications of nuclear radiation. Currently, the most common device for gamma ray detection and spectroscopy is the sodium iodide scintillation detector. Long life and high efficiency and reasonable price of these detectors are the reasons for the development of the use of these detectors in industries and laboratories. But these detectors in the classification of energy sensitive detectors are considered as detectors with low resolution. The presence of broad peaks in the gamma ray spectrum of this detector increases the possibility of interference of peaks related to different energies and makes it difficult to identify the... 

Cryptocurrency miners by solving complex mathematical calculations are responsible for verifying the transactions made in the blockchain network as well as maintaining its security, and as a reward for these activities, they receive bitcoins from the network. The devices used to mine cryptocurrency in order to perform the aforementioned calculations need high electricity consumption, so that the main cost of mining is related to its electricity consumption. For this reason, the ever-increasing development of the blockchain network, as well as the significant growth of the value of Bitcoin, has increased the number of cryptocurrency miners, especially in countries with low electricity costs.... 

According to increasing energy demand in Iran and the world, the role of natural gas as a relatively clean and cost-effective source has received more attention. Given the high share of the residential sector in the country's natural gas consumption, providing a model for forecasting the demand of this sector is of great importance for policy makers and decision makers in this field. In the present study, we employ three popular methods of machine learning, support vector regression, artificial neural network and decision tree to predict the consumption of natural gas in the residential sector in Tehran according to meteorological parameters (including temperature, precipitation and wind... 

Energy storage systems are expected to play an important role in future renewable-based power systems. Recently, lithium-ion batteries have been attracting considerable attention as alternative energy storage systems due to their advantages. In this study, viability of battery storage systems as large-scale energy storage systems is studied in the context of a residential energy hub. A stochastic optimization problem is proposed for energy management of the energy hub where wind generation and demand are considered uncertain. In order to account for the degradation costs of the battery storage system, a data-driven State-of-health prediction model is developed based on the relevance vector... 

In this work, we aim to find a way to identify and forecast transients in nuclear power plants with the aid of semi-supervised machine learning algorithm. Forecasting and identifying transients in nuclear power plants at the early stages of formation are essential for safety considerations and precautionary measures. The use of machine learning algorithms provides an intelligent control mechanism that, along with the main operator of the power plant, raises the transient detection and identification rate. Our algorithm of choice is to change the way data is presented, which is a semi-supervised learning approach. The algorithm consists of two methods: quantum dynamics clustering... 

Condition monitoring and operation optimization framework of water and energy hybrid systems has been developed in the present research work, by taking into account the variation in behavior of the system over time under changing environmental conditions. System modeling is performed using the physical laws governing the behavior hybrid energy-water system. In addition, machine learning has been used to estimate the deviation of the mathematical model from system operation, which may be due to the effect of depreciation of machinery parts and other uncertain parameters. Using machine learning and mathematical modeling together results in increased accuracy in predicting system behavior over... 

This thesis introduces a new method for identification and forecasting of future states of nuclear power plants (NPPs) parameters. The proposed method consists of four steps. First, the type of transients is recognized by the modular identifier which has been developed using the latest advances of error back propagation (EBP) learning algorithm. In second step, for more robustness of modular identifier against noisy input data, auto-regressive integrated moving average (ARIMA) method is used. A hybrid network is then used to forecast the selected parameters of the identified transient. ARIMA model is used to estimate the linear component of the selected parameters. The neural network... 

Road casualty is the fifth leading cause of death in Iran. To adopt proper countermeasures there is a need to evaluate the consequences of the implemented policies. Despite the development of crash time series models, these methods have not been in accordance with the multivariate, seasonal, and non-linear nature of crash data. On the other hand, the interpretable crash causal analysis frameworks are descriptive and they lack predictive power. Moreover, the unobserved homogeneity between observations has been widely overlooked in the crash causal analysis literature. This thesis introduces a novel causal analysis methodology by combining the interpretability and prediction power of the... 

The primary purpose of the present research is development and implementation of advanced state estimation and control techniques for space rendezvous and docking. To achieve this aim, the present study has first investigated the use of graph neural networks (GNNs) to filter out the noise of the sensor data in the state estimation process. The measurement package is consisted of a gyroscope, star trackers, and a GPS sensor providing inputs to the GNNs. The obtained results showed that the use of GNNs significantly improves the accuracy of the state estimation compared to traditional methods. In addition, the study has focused on developing advanced control techniques for spacecraft position... 

Structural Health Monitoring (SHM) of civil infrastructures is of paramount importance in ensuring the safety and reliability of these structures. SHM involves the use of sensors and data analysis techniques to continuously monitor the structural condition of infrastructure, detect damage or degradation, and provide insights for maintenance and repair. Concrete cracks are one of the most common and critical types of damage in civil infrastructure, which can compromise the structural integrity and safety of the infrastructure if left undetected and untreated. Therefore, the development of effective and efficient crack detection techniques using computer vision and machine learning can... 

The performance of the power plant decreases during its lifetime and deviates from its design and initial operation conditions; Maintenance issues, variety of operational patterns, market limitations and financial goals have been caused this situation. Knowing the appropriate actions and finding the optimal operation conditions of the power plant can support the system to restore its initial operational performance and bring it closer to its design condition. In this research, with historical data helps of a steam thermal power plant in Kermanshah, unsupervised machine learning techniques have been used to identify operational patterns, which lead to the identification of optimal operating... 

As an essential laboratory instrument in aerospace engineering, wind tunnels need to be calibrated. Therefore, this thesis takes into account the calibration of the subsonic wind tunnel at the Sharif University of Technology, with the test section dimensions of 90 × 42 × 30 cm3. It is necessary to accurately and correctly determine the effective parameters for the wind tunnel results to be reliable. A pitot-static tube, a standard wing, pressure sensors, a force balance, a turbulence sphere, rake, yawhead, a digital level, a protractor, and a data acquisition system were used for this experiment. For calibration, variables including flow uniformity, flow Angularity, turbulence intensity,... 

The pressurized structure such as aircraft fuselage and pipeline (in the oil and water transportation) have a great importance in today’s world. There is a growing tendency in improving design factors, safety and maintenance of such valuable structures. Change of design’s philosophy during the time from infinite life to safe damage and safe life and after that damage tolerance, are a process leading to many changes in design and consequently the methods of inspection, evaluation and maintenance. In this way, using old methods like non-destructive test and evaluation (NDT/E) is encountered with different problems when damage is tiny and there is no coincidence in examination (inspection)... 

Attitude estimation means calculating the state variables during the flight, especially in landing and takeoff phases. If we can extract the optical flow using the sensors mounted on the flying object, due to the fact that the optical flow is created by linear and rotational speed of the object relative to the surrounding, we are able to calculate the relative attitude by analyzing the optical flow. Indeed the purpose is developing this idea by using artificial neural networks.
First, we find the optical flow patterns for every attitude condition near the ground, using geometrical calculations. Then we produce an optimal neural network by these patterns. This network has the ability to... 

In this project an algorithm is designed for automatic landing of unmanned helicopter on a pad moving with six degrees of freedom. The designed controller is based on state dependent riccati equations (SDRE).by developing nonlinear mathematical model of helicopter and then converting this model to state dependent coefficient (SDC) form. A nonlinear compensator is added to controller to compensate effect of some nonlinear terms of model that are not able to translate and then state dependent riccati equations are solved.
The relative pose-estimation of landing pad is based on vision. Corner detection algorithm is used to identify and detect features by processing of image taken from... 

This project aims to present a solution in order to navigate and control a fixed wing Unmanned Air Vehicle (UAV) for the landing phase using position-based visual servoing (PBVS) and then implement the Hardware-in-the-loop simulation for that objective. In all phases of the simulation, it is assumed that a camera is attached under the UAV nose and is aligned with the UAV body x-axis. Initially, the situation wherein the UAV should be able to estimate its relative position by the Haralic algorithm based on four points as runway features and then give the command to a PID controller for autolanding will be simulated. Afterwards, a real camera should capture the runway image created by virtual... 

Prediction of shear plane angle is a way for prediction of the mechanism of chip formation, machining forces and so on. In this study, Merchant, Corrected Merchant, Lee and Shaffer, Lee and Shaffer with Built up Edge, Corrected Lee and Shaffer and Slip line theories are used for prediction of shear plane angles and cutting forces in machining of Al/SiCp metal matrix composite. The experimental cutting forces are compared with the calculated cutting force based on shear plane angles extracted from Merchant, Corrected Merchant, Lee and Shaffer, Lee and Shaffer with Built up Edge, Corrected Lee and Shaffer and Slip line theories. The variation of these cutting forces with cutting speed, feed... 

In this research, we will examine the emotions in the comments recorded about the tweets that have gone viral on the Twitter social media. We use Ekman's emotion classification model to classify emotions. Also, we choose the desired brands from the international brands of the fashion and clothing industry. These brands are divided into 2 luxury and non-luxury categories. Finally, we select 10 luxury brands and 9 non-luxury brands and extract all their tweets in a period of 6 months. Tweets that have been published more than a certain amount are considered as viral tweets, and using a machine learning model designed for this purpose, the emotions in the comments of users are analyzed and... 

Health influencers leveraged social networks to connect with people and society during the Covid-19 crisis. Platforms like Twitter served as suitable channels for disseminating published messages through word-of-mouth communication. In times of crises like Covid-19, individuals and organizations involved in managing the situation harnessed this capability, contingent upon the quantity and quality of word-of-mouth exchanges. During public crises, public polarity and sentiment toward the issue and the maintenance of public morale hold paramount significance. Consequently, crafting messages that garner positive word-of-mouth communication became a focal point for health influencers. In this... 

In the basic model of this thesis, which is called nested cluster optimization, only one distance function is used for clustering to form clusters with similar characteristics, while depending on whether the optimization model is long-only or long-short, different functions can be used. The aim of this thesis is to find the optimal distance function between assets in the simple nested cluster optimization so that during three different and separate strategies, based on three criteria of the lowest risk, the highest Sharpe ratio, and the highest return, the optimal distance function of assets is selected and clustering and finally weighting the portfolio to be done. The optimal distance...