Integration of solar energy and optimized economic dispatch using genetic algorithm: A case-study of Abu Dhabi

© 2017 IEEE. The United Arab Emirates is focusing on cultivating Renewable Energy (RE) to meet its growing power demand. This also brings power planning to the forefront in regards to keen interests in renewable constrained economic dispatch. This paper takes note of UAE's vision in incorporating a better energy mix of Renewable Energy (RE), nuclear, hybrid system along with the existing power plants mostly utilizing natural gas; with further attention for a sound economic dispatch scenario. The paper describes economic dispatch and delves into the usage of Genetic Algorithm to optimize the proposed system of thermal plants and solar systems. The paper explains the problem formulation, describes the system used, and illustrates the results achieved. The aim of the research is in line with the objective function to minimize the total costs of production and to serve the purpose of integrating renewable energy into the traditional power production in UAE. The generation mix scenarios are assessed using genetic algorithm using MATLAB simulation for the optimization problem

Hidden Markov Models and their Extensions for Proportional Sequential Data

We are facing an all-time high in the worldwide generation of data. Machine learning techniques have proven successful in unveiling patterns within data to further human knowledge. This includes building systems with overall better prediction and accuracy levels. Nonetheless, many areas have yet to be studied which warrants further exploitation of these techniques. Hence, data modeling is one of the topics at the forefront of scientific research. A particularly interesting field of research is the appropriate choice of distribution that corresponds to the nature of the data. In this thesis, we focus on tackling challenges in the approximation of proportional Hidden Markov Models (HMM). We review the main concepts behind HMM; one of the cornerstone probabilistic graphical models for time series or sequential data. We also discuss various modern challenges that exist when training or using HMMs. Nonetheless, we primarily focus on the notorious model estimation process of HMMs as well as the appropriate choice of emission distribution based on the nature of the data. We have tackled these problems using variational inference and Maximum A Posteriori (MAP) approximation with the Dirichlet, the Generalized Dirichlet, and the Beta-Liouville (BL) distributions-based HMMs for proportional data. In this thesis, we develop frameworks for learning these proportional HMMs that have been proposed recently as an efficient way for modeling sequential proportional data. In contrast to the conventional Baum Welch algorithm, commonly used for learning HMMs, the proposed algorithms place priors for the learning of the desired parameters; hence, regularizing the estimation process. We also extend these models into infinity for a data-driven dynamically chosen structure of HMMs. Such a setup enables flexibility in the model structure with a lower computational cost for model selection. We also investigate the fusion of the trained classifiers and witness a consequent improved performance. Moreover, we incorporate a simultaneous feature selection paradigm as well as investigate online deployment. We present our recently proposed methodologies that address the aforementioned problems and discuss the achieved results across a variety of computer vision applications. We also present how a simple novel experimental setup can drastically improve the performance of HMMs in occupancy detection, and estimation by extension, in a smart building for an applied research contribution. Finally, we conclude and recommend potential future work

Determination of sparfloxacin and besifloxacin hydrochlorides using gold nanoparticles modified carbon paste electrode in micellar medium

A gold nanoparticles modified carbon paste electrode (AuCPE) was used to study the electrochemical behavior of sparfloxacin HCl (SPAR) and besifloxacin HCl (BESI) using cyclic and differential pulse voltammetry modes in the presence of micellar medium. Effect of different surfactants on peak current was studied in Britton–Robinson buffer solution of pH 2. Sodium dodecyl sulphate is the optimum surfactant based on the enhancement of the peak current. The modified electrode shows highly sensitive sensing giving an excellent response for SPAR and BESI. The peak current varied linearly over the concentration ranges from 1.1 10 7 mol L 1 to 3.3 10 6 mol L 1 and from 2.2 10 to 5.5 10 5 mol L 1 with determination coefficients of 0.9976 and 0.9984 in case of SPAR and BESI, respectively. The recoveries and the relative standard deviations were found in the following ranges: 99.97–101.4% and 0.63–1.48% for SPAR and 99.89–101.1% and 0.85–1.76% for BESI. The detections limits were 2.87 10 8 and 3.76 10 7 mol L 1 for SPAR and BESI, respectively. The proposed method has been successfully applied to determine SPAR and BESI in biological fluids

Determination of sparfloxacin and besifloxacin hydrochlorides using gold nanoparticles modified carbon paste electrode in micellar medium

A gold nanoparticles modified carbon paste electrode (AuCPE) was used to study the electrochemical behavior of sparfloxacin HCl (SPAR) and besifloxacin HCl (BESI) using cyclic and differential pulse voltammetry modes in the presence of micellar medium. Effect of different surfactants on peak current was studied in Britton–Robinson buffer solution of pH 2. Sodium dodecyl sulphate is the optimum surfactant based on the enhancement of the peak current. The modified electrode shows highly sensitive sensing giving an excellent response for SPAR and BESI. The peak current varied linearly over the concentration ranges from 1.1 × 10−7 mol L−1 to 3.3 × 10−6 mol L−1 and from 2.2 × 10−6 mol L−1 to 5.5 × 10−5 mol L−1 with determination coefficients of 0.9976 and 0.9984 in case of SPAR and BESI, respectively. The recoveries and the relative standard deviations were found in the following ranges: 99.97–101.4% and 0.63–1.48% for SPAR and 99.89–101.1% and 0.85–1.76% for BESI. The detections limits were 2.87 × 10−8 and 3.76 × 10−7 mol L−1 for SPAR and BESI, respectively. The proposed method has been successfully applied to determine SPAR and BESI in biological fluids