FBG optimization using spline encoded evolution strategy

ABSTRACT: This paper presents an encoding scheme adapted for Fiber Bragg Grating (FBG) optimization using metaheuristics. The proposed encoding scheme uses spline approximations in order to build softened refractive index profiles from few encoded parameters. This approach is suitable for Fiber Bragg Grating (FBG) synthesis because it ensures both the reduction of the problem dimensionality and the respect of important restrictions associated to the FBG manufacture. Simulations are shown where an ES using the spline encoding was able to converge faster and produce more interesting filters, when compared with conventional encoding schemes.

Optimization of annatto (Bixa orellana L.) drying in fixed bed

ABSTRACT: The drying of annatto seeds (Bixa orellana L.), red piave cultivate, was studied in a fixed bed dryer. The best conditions were estimated to minimize the loss of coloring and to obtain final moisture of the seeds in appropriate levels to its conservation and maintenance of quality. The quantification of the influence of entrance variables in the final contents of bixin and moisture seeds and the identification of the optimal point was performed through the techniques of factorial design, response surfaces methodology, canonical analysis and desirability function. It was verified that the final moisture of the seeds may be estimated by a second-order polynomial model and that the final content of bixin is only significantly influenced by the time of drying being described properly by a linear model, for the seeds used in this study.

Imputação de dados baseado em otimização por enxame de partículas considerando os principais mecanismos de ausência de dados

Durante o processo de extração do conhecimento em bases de dados, alguns problemas podem ser encontrados como por exemplo, a ausência de determinada instância de um atributo. A ocorrência de tal problemática pode causar efeitos danosos nos resultados finais do processo, pois afeta diretamente a qualidade dos dados a ser submetido a um algoritmo de aprendizado de máquina. Na literatura, diversas propostas são apresentadas a fim de contornar tal dano, dentre eles está a de imputação de dados, a qual estima um valor plausível para substituir o ausente. Seguindo essa área de solução para o problema de valores ausentes, diversos trabalhos foram analisados e algumas observações foram realizadas como, a pouca utilização de bases sintéticas que simulem os principais mecanismos de ausência de dados e uma recente tendência a utilização de algoritmos bio-inspirados como tratamento do problema. Com base nesse cenário, esta dissertação apresenta um método de imputação de dados baseado em otimização por enxame de partículas, pouco explorado na área, e o aplica para o tratamento de bases sinteticamente geradas, as quais consideram os principais mecanismos de ausência de dados, MAR, MCAR e NMAR. Os resultados obtidos ao comprar diferentes configurações do método à outros dois conhecidos na área (KNNImpute e SVMImpute) são promissores para sua utilização na área de tratamento de valores ausentes uma vez que alcançou os melhores valores na maioria dos experimentos realizados.

Depósito da pulverização e controle de Tetranychus urticae Koch em crisântemo de corte com pulverizador assistido por ar e eletrostático

Pós-graduação em Agronomia (Proteção de Plantas) - FCA

Caracterização da qualidade e previsão dos parâmetros físico-químicos de gasolinas comerciais brasileiras através da aplicação de métodos quimiométricos em perfis (fingerprintings) espectroscópicos de ressonância magnética nuclear

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da ocorrência de tetraciclinas e estrógenos em água superficial, subterrânea e esgoto tratado na cidade de Campo Grande (MS)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Particle swarm optimization aplicada ao planejamento da expansão de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

The influence of self-weight of elastic 2D structures in topology optimization via numerical technique Smooth Evolutionary Structural Optimization (SESO)

This paper deals with topology optimization in plane elastic-linear problems considering the influence of the self weight in efforts in structural elements. For this purpose it is used a numerical technique called SESO (Smooth ESO), which is based on the procedure for progressive decrease of the inefficient stiffness element contribution at lower stresses until he has no more influence. The SESO is applied with the finite element method and is utilized a triangular finite element and high order. This paper extends the technique SESO for application its self weight where the program, in computing the volume and specific weight, automatically generates a concentrated equivalent force to each node of the element. The evaluation is finalized with the definition of a model of strut-and-tie resulting in regions of stress concentration. Examples are presented with optimum topology structures obtaining optimal settings. (C) 2012 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L.U. All rights reserved.

Switch allocation problems in power distribution systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Assessment of the effects of a parental intervention with mothers of children with internalizing problems

The present study assesses the effects of a semi-structured intervention held exclusively with mothers and its effects on internalizing problems, social skills of children, and positive and negative parenting practices. The single subject experimental design with three participants was adopted. The three mothers had, in baseline, children diagnosed with internalizing and externalizing problems. The instruments used were CBCL, RE-HSE-P, QRSH-Pais and PHQ-9, they were performed in baseline, pre-test, post-test, and follow-up assessments. The intervention held is characterized as semi-structured for it promotes the development of parental practices that are considered positive by the literature on behavior problems, however, contingently to the difficulties and demands of each case. The number of sessions performed for each case was 14, 15 and 17, which lasted about two hours each. The data were analyzed according to the instruments' norms and under the perspective of each singular case. The results found include remission of internalizing problems, increase in frequency of the children's social skills, increase in frequency of positive parental practices, and decrease in variability of negative parental practices. All the improvements were maintained on the six months follow-up, with the exception of variability on the negative parental practices of one client. Results are discussed in a context of mental health promotion and indicate the need for strategies to prevent internalizing problems in children.

Máximos e mínimos com proposta de aplicação para funções quadráticas

Pós-graduação em Matemática em Rede Nacional - IBILCE

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.

Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization

At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constrained optimization problem with some prescribed tolerance. In the continuous world, using exact arithmetic, this subproblem is always solvable. Therefore, the possibility of finishing the subproblem resolution without satisfying the theoretical stopping conditions is not contemplated in usual convergence theories. However, in practice, one might not be able to solve the subproblem up to the required precision. This may be due to different reasons. One of them is that the presence of an excessively large penalty parameter could impair the performance of the box-constraint optimization solver. In this paper a practical strategy for decreasing the penalty parameter in situations like the one mentioned above is proposed. More generally, the different decisions that may be taken when, in practice, one is not able to solve the Augmented Lagrangian subproblem will be discussed. As a result, an improved Augmented Lagrangian method is presented, which takes into account numerical difficulties in a satisfactory way, preserving suitable convergence theory. Numerical experiments are presented involving all the CUTEr collection test problems.

New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

Are mental health problems and depression associated with bruxism in children?

Renner AC, da Silva AAM, Rodriguez JDM, Simoes VMF, Barbieri MA, Bettiol H, Thomaz EBAF, Saraiva MC. Are mental health problems and depression associated with bruxism in children? Community Dent Oral Epidemiol 2011. (C) 2011 John Wiley & Sons A/S Abstract Objectives: Previous studies have found an association between bruxism and emotional and behavioral problems in children, but reported data are inconsistent. The objective of this study was to estimate the prevalence of bruxism, and of its components clenching and grinding, and its associations with mental problems and depression. Methods: Data from two Brazilian birth cohorts were analyzed: one from 869 children in Ribeirao Preto RP (Sao Paulo), a more developed city, and the other from 805 children in Sao Luis SL (Maranhao). Current bruxism evaluated by means of a questionnaire applied to the parents/persons responsible for the children was defined when the habit of tooth clenching during daytime and/or tooth grinding at night still persisted until the time of the assessment. Additionally, the lifetime prevalence of clenching during daytime only and grinding at night only was also evaluated. Mental health problems were investigated using the Strength and Difficulties Questionnaire (SDQ) and depression using the Childrens Depression Inventory (CDI). Analyses were carried out for each city: with the SDQ subscales (emotional symptoms, conduct problems, peer problems, attention/hyperactivity disorder), with the total score (sum of the subscales), and with the CDI. These analyses were performed considering different response variables: bruxism, clenching only, and grinding only. The risks were estimated using a Poisson regression model. Statistical inferences were based on 95% confidence intervals (95% CI). Results: There was a high prevalence of current bruxism: 28.7% in RP and 30.0% in SL. The prevalence of clenching was 20.3% in RP and 18.8% in SL, and grinding was found in 35.7% of the children in RP and 39.1% in SL. Multivariable analysis showed a significant association of bruxism with emotional symptoms and total SDQ score in both cities. When analyzed separately, teeth clenching was associated with emotional symptoms, peer problems, and total SDQ score; grinding was significantly associated with emotional symptoms and total SDQ score in RP and SL. Female sex appeared as a protective factor for bruxism, and for clenching and grinding in RP. Furthermore, maternal employment outside the home and white skin color of children were associated with increased prevalence of teeth clenching in SL. Conclusions: Mental health problems were associated with bruxism, with teeth clenching only and grinding at night only. No association was detected between depression and bruxism, neither clenching nor grinding. But it is necessary to be cautious regarding the inferences from some of our results.