Journal of Artificial Intelligence in Electrical Engineering
Volume:9 Issue: 35, Autumn 2020

A computational study based on first-principles calculations by supercomputers for nanoelectronic devices sometimes leads to results that can rarely be obtained in experimental laboratories with measuring tolerances. In this paper, therefore, we obtained the electronic, electrical, and optoelectrical properties of silicon p-n junction nanostructures by solving Non-Equilibrium Green’s Function (NEGF) using the first-order perturbation theory. We extracted the density of states (DOS), quantum carrier transport coefficient, IV-curve, and optoelectrical behavior by calculating the photocurrent and then plotted the light absorption spectrum. In the studied silicon nanostructure, light absorption is negligible for incident photon energy below 1 eV, and peak absorption occurs at 4 eV. In this research, the developed computational model paves the way for the study of nano-opto-electronic devices.

Electric motors are controlled according to different aspects depending on the specific applications they have in different systems. Start-up, speed control, position control and braking are the control aspects of an electric motor, for which a control method is considered depending on the intended application. A three-phase induction motor is essentially a constant speed motor. However, due to changes the mechanical load, changes in rotor rotation speed are inevitable. Therefore, it is somewhat difficult to control its speed. Induction motor speed control is accomplished at the cost of reduced efficiency and low power factor. In this paper, all methods of speed control of induction motors are examined in detail. By examining conventional methods, their advantages and disadvantages are reviewed.

Today, three-phase induction motors are widely used in industry due to advantages such as lower maintenance requirements, higher reliability, lower design costs, and the ability to operate in dusty and explosive environments. Vector control and direct torque control are two common methods for controlling induction motors. The control algorithm of the two methods is almost the same in some cases and completely different in others. Each of the control methods has advantages due to their general structure, which lead to a better response compared to other methods. The vector control method contains a good steady state response however offers relatively less speed dynamic responses and steady state time. Several methods have been proposed to solve this problem. In this paper, a switching table is used to select the appropriate switching modes for the induction motor drive to improve the output dynamic response. Comparison of the results obtained from the performance of the drive under test with the vector control and direct torque control methods illustrates that the response of the drive system to the vector control includes faster dynamics with less constant state fluctuations. In order to validate the performance of the drive system, MATLAB software has been utilized for modeling and simulation.

We live in a new world in which technology has a permanent place. In this new world, we encounter new terms and words every day, what is the first thing that comes to your mind when you hear about smart home? The latest idea for energy conservation is the use of new equipment and systems designed for this purpose. These include building energy management systems. Using modern and electronic methods to make users comfortable, reduce costs and save energy in order to optimize energy consumption and prevent waste. A smart building is a building that "includes a dynamic and cost-effective environment by integrating the four core elements of systems, service structures, and management and the relationship between them." An intelligent building offers these benefits through intelligent control systems. In this article, while introducing energy management systems in buildings, we examine its types, implementation methods, and applications.

Optical frequency shift keying demodulator is an optical structure that can generate logic 0 and 1 codes based on the frequency of the input signal. In this paper we proposed and designed a new structure for realizing frequency shift keying demodulator based on photonic crystals. The propose structure was designed using resonant cavities. The simulation results show that by setting the frequency of the input signal at 191 THz and 192.3 THz the proposed structure can generate log 0 and 1 bits. The rise time of the proposed structure is about 2 ps. Keywords: Optical Communications, Photonic crystal, FSK demodulator, Resonant cavity.

The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic areas and near faults, can provide valuable information on the geodynamic structure as well as how faults operate. Different solutions can be offered to solve such a problem. Paying attention to the reliability of the solution, its accuracy and efficiency, how to do it and most importantly the discussion of time and cost can be important and fundamental factors in this work. The purpose of this paper is to use modern and accurate methods to estimate and determine the velocity field and displacement field as well as strain tensor parameters in 3D. Artificial neural network (ANN) method with particle mass optimization training (PSO) algorithm for spatial estimation of crustal velocity changes in Iran has been studied. GPS measurements of Central Alborz network stations have been used to evaluate the method.The average relative error calculated in 4 test stations for the permanent base network in the VE component of the velocity field is 13%, in the VN component of the velocity field is 10/10% and in the Vz component of the velocity field is 15.18% of the artificial neural networks. For Central Alborz network, these values have been set as 18.41, 5.45 and 21.20% for VE, VN and Vz components, respectively. The results of this study show the high capability and efficiency of artificial neural network method in spatial estimation of the Earth's crust velocity field in this region.