A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

Optimization of algorithm to identification of duplicate tuples through similarity phonetic based on multithreading

Aiming to ensure greater reliability and consistency of data stored in the database, the data cleaning stage is set early in the process of Knowledge Discovery in Databases (KDD) and is responsible for eliminating problems and adjust the data for the later stages, especially for the stage of data mining. Such problems occur in the instance level and schema, namely, missing values, null values, duplicate tuples, values outside the domain, among others. Several algorithms were developed to perform the cleaning step in databases, some of them were developed specifically to work with the phonetics of words, since a word can be written in different ways. Within this perspective, this work presents as original contribution an optimization of algorithm for the detection of duplicate tuples in databases through phonetic based on multithreading without the need for trained data, as well as an independent environment of language to be supported for this. © 2011 IEEE.

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

Crop optimization and pre-steps standardization to get a Bipolaris euphorbiae-based bioherbicide

Making bioproducts available to the market requires finding appropriate processes for mass production and formulation of biological agents. This study aimed at evaluating the Bipolaris euphorbiae production in a solid medium (fermentation in solid substrate) and in a biphasic system (growth in a liquid medium followed by growth in a solid medium), as well as determining the processes for collecting and drying conidia, under laboratory conditions. The influence of the incubation period and inoculum quantity were also investigated. The conidia were dried by using an oven (30ºC, 35ºC, 40ºC, 45ºC, 50ºC, 55ºC and 60ºC), and laminar flow, continuous air flow and aseptic chamber at room temperature. Dry conidia were obtained by sieving and grinding in a ball mill, hammer mill or grain grinder. The conidia viability and sporulation efficiency were evaluated in the solid medium and in the biphasic system. For growth period, the best sporulation on solid medium was obtained after 10 days of incubation, reaching 8.3 x 10(7) conidia g-1 of substrate. The biphasic system did not increase the B. euphorbiae sporulation (4.5 x 10(7) conidia g-1 of substrate), after 14 days, and the amount of liquid inoculum used in this system was not an important factor for increasing its production. The continuous air flow and laminar flow preserved the conidial viability (94.6% and 99.1%, respectively), while promoting a great moisture loss (62.6% and 54.0%, respectively). All the grinding processes reduced the conidia germination (86.2%, 10.5% and 12%, respectively), while sieving allowed the collecting of powdered conidia with high viability (94.8%).

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Inclusion complex of piroxicam with beta-cyclodextrin and incorporation in hexadecyltrimethylammonium bromide based microemulsion

The interaction of piroxicam with beta-cyclodextrin (beta-CD), hexadecyltrimethylammonium bromide-based microemulsion (ME), and ME in the presence of beta-CD aimed at the optimization of topical drug delivery was studied. UV-VIS absorption spectra at pH 5.5 were obtained with and without beta-CD and ME. The stability constant (K) values for the piroxicam/beta-CD complex in the pH range 4.5-6.0 varied from 87 to 29 M-1. The cationic microemulsion was characterized by pseudo-ternary phase diagram. The association constant (K-s) of piroxicam/ME was determined using the framework of the pseudophase model. The value of K-s obtained for piroxicam at pH 5.5 was 132 M-1. At the same pH, the value of K-s for the incorporation of piroxicam/beta-CD complex in the ME was 150 M-1. (C) 1999 Elsevier B.V. B.V. All rights reserved.

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

Thermoeconomic analysis method for optimization of combined heat and power systems

This article presents an thermoeconomic analysis of cogeneration plants, applied as a rational technique to produce electric power and saturated steam. The aim of this new methodology is the minimum exergetic manufacturing cost (EMC), based on the Second Law of Thermodynamics. The decision variables selected for the optimization are the pressure and the temperature of the steam leaving the boiler in the case of using steam turbine, and the pressure ratio, turbine exhaust temperature and mass flow in the case of using gas turbines. The equations for calculating the capital costs of the components and products are formulated as a function of these decision variables. An application of the method using real data of a multinational chemical industry located in São Paulo state is presented. The conditions which establish the minimum cost are presented as finals conclusions.

Optimization of acid hydrolysis from the hemicellulosic fraction of Eucalyptus grandis residue using response surface methodology

Biotechnological conversion of biomass into fuels and chemicals requires hydrolysis of the polysaccharide fraction into monomeric sugars. Hydrolysis can be performed enzymatically and with dilute or concentrate mineral acids. The present study used dilute sulfuric acid as a catalyst for hydrolysis of Eucalyptus grandis residue. The purpose of this paper was to optimize the hydrolysis process in a 1.41 pilot-scale reactor and investigate the effects of the acid concentration, temperature and residue/acid solution ratio on the hemicellulose removal and consequently on the production of sugars (xylose, glucose and arabinose) as well as on the formation of by-products (furfural, 5-hydroxymethylfurfural and acetic acid). This study was based on a model composition corresponding to a 2 3 orthogonal factorial design and employed the response surface methodology (RSM) to optimize the hydrolysis conditions, aiming to attain maximum xylose extraction from hemicellulose of residue. The considered optimum conditions were: H2SO4 concentration of 0.65%, temperature of 157 degrees C and residue/acid solution ratio of 1/8.6 with a reaction time of 20 min. Under these conditions, 79.6% of the total xylose was removed and the hydrolysate contained 1.65 g/l glucose, 13.65 g/l xylose, 1.55 g/l arabinose, 3.10 g/l acetic acid, 1.23 g/l furfural and 0.20 g/l 5-hydroxymethylfurfural. (c) 2006 Published by Elsevier Ltd.

Thermoeconomic analysis method for optimization of combined heat and power systems - part II

In this paper, a thermoeconomic functional analysis method based on the Second Law of Thermodynamics and applied to analyze four cogeneration systems is presented. The objective of the developed technique is to minimize the operating costs of the cogeneration plant, namely exergetic production cost (EPC), assuming fixed rates of electricity production and process steam in exergy base. In this study a comparison is made between the same four configurations of part I. The cogeneration system consisting of a gas turbine with a heat recovery steam generator, without supplementary firing, has the lowest EPC. (C) 2004 Published by Elsevier Ltd.