Sharif Digital Repository

With the development of science and industry increasingly need for seamless and high-strength rings, the attention of many researchers and industrialists paid on the ring rolling process. Then, Studying details of ring rolling process is very important. The most important issues in ring rolling process which are not sufficiently considered are: stress components distribution in ring, real amounts of friction coefficient, pressure distribution over the rollers, required power, required torque and force, and finally the minimum thickness of the ring during the process, all of these parameters have developed as analytical functions. In the present study, the minimum possible thickness of the... 

Gantry milling machines are one of the types of machine tools that have vast application in various industries. Considering requirements such as quality and roughness of work pieces which are becoming restricted day after day, it is necessary that various parts of this machine have sufficient functionality and precision so that it can fulfill the quality requirements; and production of these expensive machines be cost-effective. One of the most important parts of a gantry milling machines is its crossbeam that undergoes deflections when exposed to applied forces during work conditions and can have undesirable effect on work piece precision. Therefore, the goal of this research is to design... 

During the last six decades, there were remarkable developments in the field of gas turbines. In the early 1950’s, the effciency of gas turbines was 20% and it is improved up to more than 40% for modern gas turbines. In addition, due to the implementation of the combined power cycles, the thermal effciency have been reached to 60%. According to the recent developments, further improvements in this field seem to be more costly and diffcult to obtain. Rotating detonation combustor (RDC) is a new generation of detonation combustors which has simpler design and several advantages compared to jet engine combustors, but it has its intrinsic problems which seem resolvable. Given that the RDC... 

A significant large amount of modern SOCs is occupied by SRAMs. Nowadays more than 70% of the Microprocessor area is occupied by SRAMs. The fast growth of battery-operated portable applications has compelled the SRAM designers to consider subthreshold operation as a viable choice to reduce the power consumption. In most such applications, the speed of the SRAM is not the challenging parameter therefore the thrust toward low power design influence the design choices in various parts of the SRAM architecture. With technology scaling to the nanometer, Bitline leakage current and offset voltage deteriorate SRAM reading performance since SRAM cell current is close to the Bitline leakage current.... 

After introducing “Prosopect Theory” by Kahneman and Tversky in 1979, it is mostly used as an alternative to expected utility theory in order to study human decision making under risky situatutions. Loss aversion has been considered as an important aspect of this theory. In this study, we review loss aversion and the effects of education and gender on it. Finally, we study the effect of repeating a gamble on its participants’ loss aversions. The results show that education and gender have direct effects on loss aversion and repeating a gamble can decrease loss aversion of participants.

 

Spam filtering is one of the large scale applications of machine learning. Much research has been carried out in the machine learning field with regards to spam filtering. Most of this work falls in the areas of batch learning or offline incremental learning. In batch learning, the learning process is carried out once on all the learning data. In applications such as spam filtering, in which the learning data is large in comparison to memory resources and data is generated in a stream, using incremental learning is required, in which the learning phase is repeated periodically. In each learning iteration of an offline incremental learning algorithm, a new set of data is learnt by the... 

The spherical ensemble is a well-studied determinantal process with a fixed number of points on the sphere. The points of this process correspond to the generalized eigenvalues of two appropriately chosen random matrices, mapped to the surface of the sphere by stereographic projection. This model can be considered as a spherical analogue for other random matrix models on the unit circle and complex plane such as the circular unitary ensemble or the Ginibre ensemble, and is one of the most natural constructions of a (statistically) rotation invariant point process with repelling property on the sphere. In this dissertation we study the spherical ensemble and its local repelling property by... 

In this thesis, we study a continuous time financial market of some risky assets and a risk-free asset for investment in a finite time period. We use mean-variance approach for investment in this market. In the model considered here, the mean returns of individual assets are explicitly affected by underlying Gaussian economic factors. Using past and present information of the asset prices, a partial-information stochastic optimal control problem with random coefficients is formulated. Here, the partial information is due to the fact that the economic factors can not be directly observed. In first step, by filtering and in secound step by solving the stochastic control problem, we show that... 

The integrative supply chain management is one of the main business processes between final consumer and supplier and it is formed by responsibilities such as production, services and information those have added value for customers and beneficiaries. Therefore and as the goal of a chain is maximization of gained value for the factors involved in chain, so, this goal can be more accessible by decreasing transportation (transportation method, tracking, …) and stock costs (conservation, ordering, …). As its purpose, this thesis intends to introduce and increase the options of goods transportation possibilities between retailers’ warehouses and evaluation of its effect on transportation and... 

I investigate methods of integrating prior hierarchical structure into hierarchical portfolio optimization methods. My contributions to the literature are forming a prior hierarchical structure based on investors' priorities and generating a unique representative distance matrix, which can be used as an input to other portfolio optimization methods too. In addition, I use SIC and GICs industry classifications as priory information for S&P500 companies and use them as a complementary input to the Hierarchical Risk Parity model and Hierarchical Equal Risk Contribution and compare the resultant portfolios' performance with (López de Prado, 2019)’s method of integrating prior information and... 

In this study, we combined supervised and unsupervised machine learning algorithms, included the benefits of true identification of good borrowers and costs of false identification of bad borrowers, and then proposed a model for predicting the default of loan applicants with a profit-based approach. The results show that our proposed model has the best performance in profit measure in comparison with individual supervised models. In fact, we first divided the data into two train sets and one test set. We have constructed our model by training unsupervised models on the first train set and supervised models on the second train set. The results of implementing the model on the Australian and... 

In many methods of calculating Value-at-Risk (VaR), we need to calculate the value of the portfolio several times for different scenarios. Because an explicit formula is not available to calculate the value of some fixed income assets, calculating VaR for portfolios containing these assets imposes a heavy computational burden. In this study, we introduce a new method for calculating VaR for such portfolios. In this method, some of the existing scenarios are selected as basic scenarios and the value of the portfolio is calculated only for each of them. Next, using the calculated values, the portfolio values for other scenarios are estimated by interpolation (or extrapolation). Finally, by... 

differential equations. These methods have extensive applications in the financial domain, including in risk hedging and derivative pricing. One of the main advantages of using deep learning methods in this area is their high capability to solve high-dimensional problems. Despite the introduction of these new methods, some of them have not performed well in certain problems. In this research, efforts have been made to improve the efficiency of these methods by enhancing the neural network architecture and modifying the learning process  

Investors are looking for profit making oppurtunities and using them to maximize their wealth. For investing in stock exchange, investors should be able to valuate the stock price rationally, so that he/she can define which stocks are trading below their intrinsic value. Stock price is the discount of future dividends of stock, but for discounting accurate rate is needed. Main goal of this study is presenting a way to calculate the proper discount rate or expected rate of return for investors. Method which has been studied in this thesis is “Fama & French five factor asset pricing model” which has been publish 2015 (Fama & French, 2015). The model concentrates on five factors to describe the... 

In this research, we consider a made-to-order production system which receives orders for a single product. The demand is stochastic and price-dependent. We also consider a sale index that links the demand of each period to the demands as well as to the quality of the previous periods. The variety of quality levels and its effect on the amount of orders make this revenue management complicated. Furthermore, the price of each product is related to its quality and sales index. Furthermore, reference price causes the model to be significantly more complicated. We model the problem within the framework of dynamic programming approach to maximize the discounted profit of the firm during a... 

In this study, the steps for designing an intelligent network for the Urban Air Mobility transportation system is proposed, and these steps are implemented for the city of Los Angeles as a case study, which is one of the busiest and most populous cities in the world.. Initially, the demand and its distribution in the study area is estimated; Then, according to the distribution of demand and existing infrastructure, the optimal location of stations are determined; afterwards, considering a well-designed electric vertical take-off and landing aircraft called Spricho the network is simulated, its performance and quality are examined and the optimal combination of the number of planes and the... 

High-quality online services demand reliable and fast packet delivery at the network layer. However, clear evidence documents the existence of compromised routers in the ISP and enterprise networks, threatening network availability and reliability. A compromised router can stealthily drop, modify, inject, or delay packets in the forwarding path to launch Denial-of-Service, surveillance, man-in-the-middle attacks, etc. So researches tried to create intrusion detection methods to identify adversarial routers and switches. To this end, data-plane fault localization (FL) aims to identify faulty links and is an effective means of achieving high network availability. FL protocols use... 

Confining nematic liquid crystal between two curved boundary conditions while the nematic molecules have a degenerate planar anchoring leads to complex and beautiful textures of molecular disparagement(defect) in the bulk and on the surfaces. Such the liquid crystal shells are made with double-emulsion techniques in microfluidic devices have provided applications for micro-scale colloidal linkers.In this work, we have numerically minimized the elastic energy in one-constant approximation in present of Fournier’s degenerate surface potential with finite element method. The nematic shell is confined between two spherical surfaces that we have studied the final energies and their related... 

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...