Sharif Digital Repository

Bonding gravity with Quantum mechanics has always been a challenging issue. So that, accomplishing a theory of quantum gravity is one of the most important research topics in High-Energy Physics.Information paradox in black holes is among such issues which connect quantum mechanics with gravity. Black holes as a natural solution of Einstein equations have a distinction, with other solutions, since there exists an event horizon that imparts space-time into two causally disconnected regions.According to Hawking’s calculations, black holes would evaporate over time by the thermal radiation. This resulting thermal radiation has less information than the initial collapsed matter in a pure state.... 

Today’s biggest IoT companies are built on the extraction of insight from data of sensors, and data processing has become crucial in IoT businesses. Nevertheless, the size of data which should be processed is growing significantly fast. The pace of the data growing has changed the nature of data processing in IoT. Today, IoT industries demand highly scalable and fault tolerant data processing architectures which can handle the massive amount of data. In this research, we presented a distributed architecture for scalable and resilient data processing based on the Liquid which is a nearline and offline big data architecture. We used the Reactive Manifesto to design the architecture highly... 

Based on fog computing paradigm, new applications have become feasible through the use of hardware capabilities of smart phones. Many of these applications require a vast amount of computing and real-time execution should be guaranteed. Based on fog computing, in order to solve these problems in is necessary to offload heavy computing to servers with adequate hardware capabilities. On the other side, the offloading process causes time overhead and endangers the real-timeliness of the application. Also, because of the limited battery capacity of the handheld devices, energy consumption is very important and should be minimized.The usual proposed solution for this problem is to refactor the... 

The process of portfolio construction and optimization can be broken down into three main steps: selecting appropriate assets, allocating capital, and monitoring and adjusting the portfolio. This study focuses on evaluating the performance of the Hierarchical Risk Parity (HRP) method for capital allocation in investment portfolios, specifically in Iran’s capital market. The aim is to enhance the method's effectiveness by implementing alternative correlation calculation approaches, such as Wavelet and Chatterjee correlations. The study utilizes three different portfolios containing assets from the Tehran Stock Exchange, the US stock market, and the cryptocurrency market. The primary objective... 

Stock price patterns are a technical analysis approach to forecast future trends with tremendous practical benefits. However, the current algorithms solely rely on machine learning techniques and deep neural networks which could be a problem in countries where data sets such as these are not available. We propose an algorithm based on geometry and mathematics for this problem, leading to an O(n^3logn + n^2k) complexity, where k is the number of triangular patterns  

The main topic of this dissertation is to find methods for obtaining parametric solutions and linear/nonlinear invariants of Diophantine equations and consists of 4 chapters. The first chapter consists of some introductory discussions. The second chapter begins with a review of linear invariant, U-invariants, covariants and the concept of semi-invariants. The generators of maximum degree 3 for producing all linear invariants are introduced in Chapter 2 as well. Moreover, the relations between 3rd and 4th degree Hilbert invariants in terms of Procesi bases are included in this chapter.In chapter 3, a general conjecture is given to check whether there are finitely many solutions to a... 

This paper develops a novel insight and procedure that includes a variety of algorithms for finding the best solution in a structured multi-agent system with internal communications and a global purpose. In other words, it finds the optimal communication structure among agents and the optimal policy in this structure. First, a unique reinforcement learning algorithm is proposed to find the optimal policy of each agent in a fixed structure with non-linear function approximation like artificial neural networks (ANN) and eligibility traces. Secondly, a mechanism is presented to perform self-organization based on the information of the learned policy. Finally, an algorithm that can discover an... 

Representation learning methods on graphs have enabled using machine learning methods on graphs' discrete structure by transferring them to a continuous domain. As graphs' structures are not always static and may evolve through time, dynamic representation learning methods have recently gained scholars' attention. Several methods have been proposed to enable the model to update the embeddings graph changes, or new interactions happen between nodes. These online methods could significantly reduce the learning time by refreshing the model as the changes occur, so we don't need to retrain the model with the complete graph information. Moreover, by using the temporal information of interactions,... 

In the past few decades, with the advancement of technology, using autonomous robots has been receiving growing interest. Autonomous semi-submersible vehicles are a subset of autonomous underwater vehicles (AUV). This type of vehicle operates near the surface of the water and is semi-submerged. Nonlinear coupled dynamics, structural uncertainties, model parameters dependency to robot velocity, external disturbances, and model constraints are AUV’s workspace challenges. For this reason, depth control of these vehicles in the presence of environmental disturbances is crucial. However, MPC as one of the advanced control methods in the field of robotics is increasingly developing. This control... 

The use of distributed generation (DG) units provides several advantages for the utility distribution grid. Mostly power electronics converter is used as an interface to connect a DG unit to a utility grid. A DG unit normally operates in a grid-connected mode. In this mode, the host grid dominantly dictates the voltage amplitude and frequency of the local load at the point of common coupling (PCC) and the DG unit controls its real/reactive power components. Separation of the DG unit and its dedicated load from the host grid is called islanding. Subsequent to the formation of an island, due to the lack of control over the voltage magnitude and frequency, the islanded DG system becomes... 

One of the issues in the design and synthesis of the state or output feedback controller is the issue of fragility as the existence of disturbance in the coefficients of the designed controller causes instability in the controlled closed-loop system or leads to deterioration of its performance. The need to consider this point results in some novel approaches for designing the non-fragile controllers. Accordingly, defining criteria for measuring fragility is crucial.As well, due to the considered practical problems in the implementation of ordinary designed robust and optimal controllers, non-reachability of all the states of dynamical systems, and the boundedness for the quantity of the... 

One of the crucial ways to reduce the energy costs of buildings is to improve the energy efficiency of HVAC systems. Heating, Ventilation, and Air Conditioning (HVAC) refer to the systems used to adjust the heating and cooling within a building. Control of these systems has great importance due to their excessive energy usage. Various control methods have shown a potential for considerable savings in building operation costs. In this research, a Model Predictive Controller (MPC) is applied to perform optimal control actions. MPC opens up numerous opportunities to improve energy efficiency in the operation of HVAC systems because of their capability to consider constraints, prediction of... 

In today's world, mobile robots have many applications in defense, transportation and industry. A robot may not be able to handle a mission alone or there may be different roles to perform a mission that a robot alone cannot perform, so the need to work with team robots is felt to the Necessary that by development Robot Technology and Advances in Communication, Microelectronics, Computing Technology, and Multi - Factor Expansion, robotic systems are widely used in theoretical research due to their flexibility, robustness, and scalability. In this research, robots are controlled in a coordinated manner as a team. In this research, algorithms were developed so that in a two and... 

Buildings account for more than 30% of the world’s total energy consumption. Among building end-uses, air conditioning and in particular cooling systems have a major share of more than 50%. Therefore, design of optimal controllers for AC systems has become increasingly important. Classical and model-free control methods typically lack the ability to optimize energy consumption. On the other hand, model-based optimal control methods rely on precise modeling, which is difficult to acquire due to the complexity of the AC system dynamics.In recent years, deep reinforcement learning has become a popular choice for optimal control of systems with complex dynamics. In this thesis, a deep... 

Heating, Ventilation and Air-Conditioning (HVAC) systems of buildings consume an excessive amount of energy and emit even more amounts of carbon, all around the world. Rule-Based Control (RBC) algorithms, which switch the facilities on and off according to the measurements of the building’s sensors, are the most frequently utilized controllers in HVAC systems. Due to the conservative settings of the comfort-zone in RBC strategies, energy consumption increases by a large amount. One of the most conventional ways to improve the energy efficiency along with providing the thermal comfort of the occupants of the building, is model predictive control (MPC) algorithms. In order for MPC to work... 

Energy management in buildings play an important role in minimizing the global energy consumption. Providing an optimal temperature in buildings and workplaces is a significant step toward the energy management, regardless of the local climate. Buildings account for 20-40% of the world’s total energy consumption, and this consumption is increasing in developed countries at 0.5-5% per annum.In various studies, energy consumption potential has been investigated using model predictive control (MPC) and system identification.In this paper, the building system is modeled as a gray-box model in which the formulas are developed using the heat transfer equation and the solar radiation heat is... 

Most of the robust or optimal controllers can produce extremely fragile controllers [1], in the sense that vanishingly small perturbations of the coefficients of the designed controller destabilize the closed-loop control system. Modern control methods, including robust control, lead to high order controllers.Often times a reduced order controller is implemented. As a result, need for low order control design reducing controller’s ensitivity is tangible. Based on studies carried out, properties of normal matrices can be employed to define a fragility measure. In this thesis, a new convex criterion measuring controller fragility is defined. Then, a more resilient reduced order controller can... 

Order reduction is a very important issue in Control Theory. A growing need for order reduction models in different fields such as simulation, identification, and design of control system shows this significance. Actually, a high-order system makes a great deal of complexity in designing hardware of control system, debugging, and implementation. Till nowadays, many repetitive as well as nonrepetitive methods with various criteria have been introduced to find low-order models. In this research, order reduction of linear time invariant system models is analyzed. The selected criterion for measuring error between original system and reduced order system is the norm of H1 because it is not only... 

In new method of performance-based design, the behavior of structure during earthquake is considered more realistically, and applying damage parameters like plastic ductility and inter-story drift to determine the seismic performance of structures is suggested. Damage indexes, the contribution of damage parameters to damage capacity of structural members, are defined to specify damage level of each element. In addition, damage spectrum which is the diagram of values of certain damage index versus natural period of single-degree-of-freedom system will be useful to seismic assessment of existing structures and seismic design of new ones. By the way, endurance time (ET) method can be... 

Eigenvalue analysis is a fundamental tool in control issues, however, in some abnormal matrixes it can lead to an incorrect judgment of the matrix dynamic behavior. Pesudospectra, as an eigenvalue generalization, can provide more extensive information of the system dynamic behavior, whereas, the obtained data from eigenvalue analysis can be incomplete. In applying robust controllers, the concept of uncertainty has been attracted researcher interest in system modelling, nevertheless, the controller implementation may not be fault free or the parameters may vary over time. In this project the aforementioned issue will be studied and additionally, the pseudospectra will be employed to design a...