A review of particle swarm optimization and its applications in Solar Photovoltaic system

Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.     

Comparison of Flux Pinning Mechanism in Laser Ablated YBCO and YBCO:BaZrO3 Nanocomposite Thin Films

Thin films of YBCO and YBCO:BaZrO₃ (BZO) nanocomposite have been deposited using the pulsed laser deposition technique. Substantial increase in critical current density (J _C ) and pinning force density (F _p ) of the nanocomposite thin films was observed. The possible pinning mechanism in YBCO:BZO nanocomposite thin films has been explored and compared with the pinning mechanism in pure YBCO thin film by studying the variation of J _C with magnetic field (B) and temperature. In the intermediate field regime (0.1–1 T), J _C follows B ^−α with nearly similar values of α for YBCO and YBCO:BZO nanocomposite thin films indicating similar pinning mechanism in both thin films. The variation of J _C with reduced temperature (t=T/T _C ) has been studied for both the films and it was observed that the mechanism of pinning in both YBCO and YBCO:BZO thin films is similar (δT _C pinning). The observed enhanced values of J _C and F _p of the nanocomposite thin film is attributed to the presence of BZO nanoparticles, which induces more defects due to lattice mismatch between YBCO and BZO leading to improved flux pinning properties of the nanocomposite thin film.     

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Machine Learning - Covariance estimation for high-dimensional datasets is a fundamental problem in machine learning, and has numerous applications. In these high-dimensional settings the number of...     

Leading One Detection Hyperbolic CORDIC with Enhanced Range of Convergence

This paper focuses on developing an area efficient hyperbolic Coordinate Rotation Digital Computer (CORDIC) algorithm with performance improvement. The algorithm eliminates the need of scale factor calculation in the Range of Convergence (ROC). At the same time the range of convergence offered is higher than the conventional CORDIC ROC in the hyperbolic rotation mode. Being the only kind of algorithm in hyperbolic rotation with sign sequence μ?=?1 always, one complete operation requires just 5 iterations. Thus the pipelined implementation has 5 stages which provides a 50% increase in throughput in comparison to conventional CORDIC. As far as the area improvement is considered, 16-bit processor can be realized using 56% less number of full adders required by Flat-CORDIC. The x and y datapath are based on series expansion of hyperbolic functions. The complete algorithm design along with pipelined architecture implementation is detailed.     

Melt‐mixed polypropylene/acrylonitrile‐butadiene‐styrene blends with multiwall carbon nanotubes: Effect of compatibilizer and modifier on morphology and electrical conductivity

Cocontinuous blends of 45/55 polypropylene (PP)/acrylonitrile‐butadiene‐styrene (ABS) with multiwall carbon nanotubes (MWNT) were prepared by melt‐mixing in a conical twin‐screw microcompounder. PP‐grafted‐maleic anhydride (PP‐g‐MA) and styrene MA were used as compatibilizers for PP/ABS blends. Scanning electron microscopic observations showed phase segregation of PP‐g‐MA in the blends. State of dispersion of MWNT in the presence or absence of the compatibilizers was assessed through AC electrical conductivity measurements and crystallization studies of the blends. An improvement in AC electrical conductivity was observed in blends in presence of either styrene MA or dual compatibilizers. The lowest electrical percolation threshold was achieved at 0.1 wt % of MWNT using sodium salt of 6‐amino hexanoic acid‐modified MWNT. Significant increase in crystallization temperature of PP phase of blends with MWNT was observed in the presence of compatibilizers as compared to blends without compatibilizers. An attempt has been made to address the complex issues of phase segregation, compatibilization, and dispersion of MWNT in cocontinuous blends of PP/ABS. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Combined computer-aided approach for the facilities design problem and estimation of the distribution parameter in the case of multigoal optimization

The multigoal facilities design problem, a case of quadratic assignment problem, deals with the problem of optimum assignment of n facilities to n locations, optimizing multigoal cost objectives. These objectives are classified into two categories, conflicting and congruent. Conflicting objectives aim at minimization of total flow cost and maximization of total closeness rating, whereas congruent objectives aim at maximization of distance weighted cost of several attributes, vis. flow, closeness rating, etc. The technique available for dealing with the congruent objective optimization does not consider the weightage to these attributes. The optimality of a suboptimal solution should also be checked at all levels of weightages in order to arrive at a correct solution, with the help of estimation of the distribution parameter, in the case of the multigoal facilities design problem.

This paper presents a combined computer-aided approach which determines the solution of the multigoal facilities design problem at different weightages to attributes, finds the parameter of distribution viz. mean, median, mode, standard deviation, checks the optimality of solution, plots the histogram and gives the nature of distribution.     

Fenton and Electro-Fenton Methods for Oxidation of H-Acid and Reactive Black 5

Oxidative treatment of H-acid (HA) and Reactive Black 5 (RB5) using Fenton reagent (Fe2+/H2O2) and the electro-Fenton (EF) method is reported. Optimization of doses of ferrous iron and hydrogen peroxide was carried out in each case using HA; and the oxidation of RB5 was also carried out under the optimized conditions. Approximately 71% chemical oxygen demand (COD) was removed in 2 h using the conventional Fenton method at optimized doses: Fe2+ = 0.3?g/L (5.37 mM), H2O2 = 6?mL/L (53.0 mM), H2O2/Fe2+ = 10. In contrast, more than 92% COD was removed in 15 min using the EF method with an optimized Fe2+ dose of 0.130?g/L (2.34 mM) and 8?ml/L (70.6 mM) of H2O2. The pseudo-first-order rate constants (k) for the Fenton reagent and EF method were 0.054 and 0.38?min?1. The COD removal through the EF method was seven times faster. The calculated energy requirement of the EF method was 0.82?kg?COD/kW?h at the minimum applied current (0.25 A) when approximately 92.5% COD was removed. In the case of RB5, about 67 and 87% COD was removed under optimized Fenton and electro-Fenton conditions, respectively. The higher efficiency of the EF method was attributed to incremental addition of Fe2+ and accompanying higher H2O2/Fe2+ molar ratio. The results are discussed in the light of the mechanism for Fenton’s oxidation.     

Design of inferential sensors in the process industry: A review of Bayesian methods

In many industrial plants, development and implementation of advanced monitoring and control techniques require real-time measurement of process quality variables. However, on-line acquisition of such data may involve difficulties due to inadequacy of measurement techniques or low reliability of measuring devices. To overcome the shortcomings of traditional instrumentation, inferential sensors have been designed to infer process quality indicators from real-time measurable process variables. In recent years, due to the demonstrated advantages of Bayesian methods, interest in investigating the application of these methods for design of inferential sensors has grown. However, the potential of Bayesian methods for inferential modeling practices in the process industry has not yet been fully realized. This paper provides a general introduction to the main steps involved in development and implementation of industrial inferential sensors, and presents an overview of the relevant Bayesian methods for inferential modeling.