I. Elamvazuthi

Hybrid neuro-swarm optimization approach for design of distributed generation power systems

The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this sort of power source was done by integrating (or hybridizing) multiple different alternative energy sources (e.g., wind turbine generators, photovoltaic cell panels, and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability, and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system were carried out successfully in the previous work using particle swarm optimization (PSO). The goal was to minimize cost, maximize reliability, and minimize emissions (multi-objective function) subject to the requirements of the power balance and design constraints. In this work, the optimization was performed further using Hopfield neural networks (HNN), PSO, and HNN-PSO techniques. Comparative studies and analysis were then carried out on the optimized results.

Despeckling of ultrasound images of bone fracture using multiple filtering algorithms

Abstract Ultrasound images are popularly known to contain speckle noise that degrades the quality of the images for good and fast interpretation in many areas of medicine, especially for bone fracture detection. This necessitates the need for robust despeckling techniques for clinical practice. Therefore, a study was carried out to reduce speckle using filtering algorithms such as Wiener, Average, Median, Anisotropic Diffusion and Wavelets. This paper discusses the level of improvement obtained through these filtering algorithms using the peak signal-to-noise ratio (PSNR) as a measurement tool. The results of our work presented in this paper suggest that the combination of Daubechies–Wiener which we call as a hybrid technique with Anisotropic Diffusion, gave the best performance, which is a new contribution in this field. This despeckling algorithm can be further developed and evaluated at a larger scale.

Interline Power Flow Controller (IPFC) based damping controllers for damping low frequency oscillations in a power system

The interline power flow controller (IPFC) is a voltage-source-converter (VSC)-based flexible AC transmission system (FACTS) controller which can inject a voltage with controllable magnitude and phase angle at the line-frequency thereby providing compensation among multiple transmission lines. In this paper, the use of the IPFC based controller in damping of low frequency oscillations is investigated. An extended Heffron-Phillips model of a single machine infinite bus (SMIB) system installed with IPFC is established and used to analyze the damping torque contribution of the IPFC damping control to the power system. The potential of various IPFC control signals upon the power system oscillation stability is investigated a using controllability index. Simulation results using Matlab Simulink demonstrate the effectiveness of IPFC damping controllers on damping low frequency oscillations.

Vocal fold disorder detection based on continuous speech by using MFCC and GMM

Vocal fold voice disorder detection with a sustained vowel is well investigated by research community during recent years. The detection of voice disorder with a sustained vowel is a comparatively easier task than detection with continuous speech. The speech signal remains stationary in case of sustained vowel but it varies over time in continuous time. This is the reason; voice detection by using continuous speech is challenging and demands more investigation. Moreover, detection with continuous speech is more realistic because people use it in their daily conversation but sustained vowel is not used in everyday talks. An accurate voice assessment can provide unique and complementary information for the diagnosis, and can be used in the treatment plan. In this paper, vocal fold disorders, cyst, polyp, nodules, paralysis, and sulcus, are detected using continuous speech. Mel-frequency cepstral coefficients (MFCC) are used with Gaussian mixture model (GMM) to build an automatic detection system capable of differentiating normal and pathological voices. The detection rate of the developed detection system with continuous speech is 91.66%.

Damping of inter area oscillations using Interline Power Flow Controller based damping controllers

This paper investigates the effect of interline power flow controller (IPFC), an advanced flexible AC transmission system (FACTS) controller, in damping low frequency oscillations via supplementary control. For this purpose, a modified linearised Phillips-Heffron model for a single machine infinite bus (SMIB) system installed with IPFC is established, and the power oscillation damping controller is designed. The effect of this damping controller on the system, subjected to wide variations in loading conditions and system parameters, is investigated. Results of simulation investigations in Matlab are presented to validate the proposed approach.

Hybrid Optimization Techniques for Optimization in a Fuzzy Environment

The fuzzy technology reveals that everything is a matter of degree. At the moment, many industrial production problems are solved by operational research optimization techniques. These techniques are performed under the consideration of some real assumptions. In current studies, we still have several problems that require the application of fuzzy linear, non-linear, non-continues and other mathematical programming techniques. The prime objective of this chapter is to investigate a new application to the literature and to solve the crude oil refinery production problem by using the hybrid optimization techniques such as; Tabu Search (TS), Hopfield Recurrent Artificial Neural Network (HRANN) and fuzzy approaches. In this work, the real-world problem of the refinery model (which has been developed in (Gunes, 2000)) was solved using various optimization techniques. Thorough comparative studies and results analysis was carried out as well. The final findings reveal that the hybrid optimization technique provides better, robust, efficient, flexible and stable solutions.

Evolutionary normal-boundary intersection (ENBI) method for multi-objective optimization of green sand mould system

In multi-objective engineering optimization, it is very rare that there exists a unique ideal solution that covers every aspect of the problem. Thus, it is very useful for the decision maker to have multiple options prior to the selection of the best solution. In this work, a novel evolutionary normal boundary intersection (ENBI) method is introduced to identify optimal solutions to the green sand mould system problem. The ENBI method provides a uniform spread of the Pareto frontier in which multiple solutions with gradual trade-offs in the objectives are obtained. Some comparative studies were then carried out with the previous work. Analysis on the performance of the algorithm as well as the solution spread was done.

Fuzzy logic control for IPFC for damping low frequency oscillations

This paper presents the application of fuzzy logic based supplementary controller, for Interline Power Flow Controller (IPFC), to enhance the damping of low frequency oscillations in the Single Machine-Infinite Bus (SMIB) power system installed with IPFC. The fuzzy logic based IPFC controller using Mamdani type inference system is designed with generator speed and rotor angle as its input signals. The proposed method is applied to control the input signal of IPFC and thus improving the power system stability. The effectiveness of the controller in damping the power system oscillations is demonstrated with varying operating conditions.

Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques

Wheeled Mobile Robot (WMR) is extremely important for active target tracking control and the reactive obstacle avoidance in an unstructured environment. Fuzzy control provides a mechanism for incorporating human-like reasoning capabilities and computationally in control systems. In this paper, a new type of controller based on Fuzzy Logic for differential drive WMR is proposed. The paper begins with design of the WMR which involves the kinematics model and fuzzy controller. Then, simulation and research findings are provided in detail. It was found that the Fuzzy Logic Controller with five membership functions provides better performance compared to that of a seven membership function controller.

3D reconstruction of ATFL ligament using ultrasound images

Among all existing medical imaging modalities such as MRI, CT, PET and SPECT, ultrasound (US) imaging is one of the most widely used in various applications such as ligament injuries, tendon injuries, bone injuries, cardiac imaging, blood flow estimation and obstetrics. Although Three-Dimensional Ultrasound (3D-US) has many benefits such as non-invasive, low cost, portable, real-time and non-ionizing, however, it is limited in diagnosis and management of obstetrics and atherosclerosis, thus far. Since the current Two-Dimensional Ultrasound (2D-US) has many limitations in the diagnosis of ligament injuries and abnormalities. This study represents the 3D Ultrasound Reconstruction of Anterior talofibular Ligament (ATL) for obtaining the fruitful information for disease diagnosis and progression.