Majlesi Journal of Electrical Engineering
Volume:6 Issue: 1, Mar 2012

Development in power electronic derives, moreover simplicity and robustness of Switched Reluctance Motor (SRM) besides their ability to work in harsh conditions, makes them appealing to industrial applications. Structurally, SRM has ability to conform to different configurations to make it suited for purposed application. In this paper, an 8/6 two layers Switched Reluctance Motor is introduced. This rotor consists of two magnetically dependent sets which each set contains 8 stator poles and 6 rotor poles with windings wrapped around them. The torque ripple reduction is done by a novel introduced method using Rotor Shifting Method (RST). Furthermore, motor operations as SRM are modeled and simulated by 3D Finite Element Method (FEM). Finally, prototype of motor is fabricated and experimental analysis is carried out to confirm validation of modeling and simulation results.

Active queue control aims to improve the overall communication network throughput, while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperforms Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.

In this paper, a simplified control algorithm based on unit vector template generation (UVTG) is proposed for a star-delta supported three-phase four-wire (3P-4W) unified power quality conditioner (UPQC) topology for the improvement of different power quality problems. Different topologies reported in literature for 3P-4W UPQC use active compensation for the mitigation of source neutral current along with other power quality (PQ) problems, while the uses of passive elements for the mitigation of source neutral current are advantageous over the active compensation due to ruggedness and less complexity of control. Hence, in this paper a star-delta transformer is connected in shunt near the load for mitigation of source neutral current, while three-leg voltage source inverters (VSIs) based shunt and series active power filters (APFs) of 3P-4W UPQC mitigate the current and voltage based distortions, respectively. A simple control algorithm based on Unit Vector Template Generation (UVTG) is used as a control strategy of UPQC for mitigation of different PQ problems. In this control scheme, the current/voltage control is applied over the fundamental supply currents/load voltages instead of fast changing APFs currents/voltages, thereby reducing the effects of computational delay and the required sensors. The performance of the proposed topology of UPQC is analyzed through simulations results using MATLAB software with its Simulink and Power System Block set toolboxes.

Accurate liver segmentation on Magnetic Resonance Images (MRI) is a challenging task especially at sites where surrounding tissues such as spleen and kidney have densities similar to that of the liver and lesions reside at the liver edges. The first and essential step for computer aided diagnosis (CAD) is the automatic liver segmentation that is still an open problem. Extensive research has been performed for liver segmentation; however it is still challenging to distinguish which algorithm produces more precise segmentation results to various medical images. Accordingly, in this paper, we have presented a new automatic system for liver segmentation in abdominal MRI images. Our method extracts liver regions based on several successive steps. The preprocessing stage is applied for image enhancement such as edge preserved and noise reduction. The proposed algorithm for liver segmentation is a combined algorithm which utilizes a contour algorithm with a Vector Field Convolution (VFC) field as its external force and perceptron neural network. By convolving the edge map generated from the image with the user-defined vector field kernel, VFC is calculated. We use trained neural networks to extract some features from liver region. The extracted features are used to find initial point for starting VFC algorithm. This system was applied to a series of test images to extract liver region. Experimental results showed the promise of the proposed algorithm.

Most information retrieval systems make indirect use of human knowledge in their retrieval process. The novel systems presented here uses the human knowledge directly to retrieve the soccer events. The proposed system is an extended method in three aspects as follows: in the query model, the query by example and query by keywords are applied to retrieve the semantics. In feature extraction, we propose novel methods for extracting the caption as important cinematic feature and for detecting the player gathering. And in the retrieval process, a novel method based on fractal coding is proposed here. The first phase consists of extracting suitable features and key frames from video shots. Then, using a fractal coding, soccer shots are retrieved, and using a fuzzy rule base system, shots that do not include significant soccer events are removed. Experimental results show high accuracy of proposed caption extraction and player gathering detection algorithm and satisfying retrieval process.

Biogeography based optimization (BBO) is a new stochastic force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO utilizes migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this paper, BBO algorithm is developed to optimize the current excitations of concentric circular antenna arrays (CCAA). Concentric Circular Antenna Array (CCAA) has numerous attractive features that make it essential in mobile and communication applications. The goal of the optimization is to reduce the side lobe levels and the primary lobe beam width as much as possible. To confirm the capabilities of BBO, three different CCAA antennas of different sizes are taken. The results obtained by BBO are compared with the Real coded Genetic Algorithm (RGA), Craziness based Particle Swarm Optimization (CRPSO) and Hybrid Evolutionary Programming (HEP).

In this paper, distributed generations for risk management of a distribution company (DisCo) in the competitive market environment are introduced. The proposed model for this problem considers a stochastic programming framework in the yearly horizon that is to maximize the expected profit considering the uncertainties of the retail market such as the end user demand and the electricity pool price. In this study, the key point is modeling the uncertainties of distributed generations as a reliable source for DisCos. Finally, the approach suggests the optimal resources for procuring the customer's load. Also a basic carbon market is modeled to support the role of renewable energies.

In recent years, on the one hand with increasing application of nonlinear loads in power systems and no sinusoidal currents that extracted from system and on the other hand increasing loads sensitive to power quality and destructive effects of nonlinear loads on power quality of power systems, compensation these loads has been converted to one of the main issues in power systems. The use of flexible ac transmission system (FACTS) devices is one of the most progressive methods which are used for improving power quality. In this paper, series and parallel compensators of static synchronous compensator (STATCOM) and dynamic voltage restorer (DVR) are introduced and the comparison of these two in compensating of power quality phenomena from electric arc furnace is provided. STATCOM is a shunt active filter and DVR is a series active filter. Method which has been selected for control of every one of these devices is an optimal control way that it minimizes power losses.

The purpose of this study is to investigation of microelectromechanical behavior of smart piezoelectric actuators using Artificial Neural Networks due to simple, multi harmonic and dynamic pulse excitations. Regarding to complexity and time-consuming analyses of vibration of smart structures, existing classical models are often insufficient. Nowadays, artificial intelligence tools are used for modeling such complex phenomena. The theoretical model is a three-layer piezoelectric composite beam that behaves as an axial actuating mechanism. This actuator consists of an elastic core sandwiched between two piezoelectric active outer layers. The piezoelectric layers are polarized transversely, i.e., the polarization vector is parallel to the applied electric field intensity vector. For initializing the electromechanical effect, an electric field is applied to the piezoelectric layers. The finite element modeling is constructed using ANSYS. Then, harmonic and dynamic vibration analyses are performed and the responses of smart beam are calculated. The required data used for artificial intelligence were collected from vibration analyses. Obtained results demonstrate that artificial neural network is in good agreement with observed values.