Optimal location of supports in beam structures using genetic algorithms

Real structures can be thought as an assembly of components, as for instances plates, shells and beams. This later type of component is very commonly found in structures like frames which can involve a significant degree of complexity or as a reinforcement element of plates or shells. To obtain the desired mechanical behavior of these components or to improve their operating conditions when rehabilitating structures, one of the eventual parameters to consider for that purpose, when possible, is the location of the supports. In the present work, a beam-type structure is considered, and for a set of cases concerning different number and types of supports, as well as different load cases, the authors optimize the location of the supports in order to obtain minimum values of the maximum transverse deflection. The optimization processes are carried out using genetic algorithms. The results obtained, clearly show a good performance of the approach proposed. © 2014 IEEE.

Surgical correction of scoliosis: Numerical analysis and optimization of the procedure

A previously developed model is used to numerically simulate real clinical cases of the surgical correction of scoliosis. This model consists of one-dimensional finite elements with spatial deformation in which (i) the column is represented by its axis; (ii) the vertebrae are assumed to be rigid; and (iii) the deformability of the column is concentrated in springs that connect the successive rigid elements. The metallic rods used for the surgical correction are modeled by beam elements with linear elastic behavior. To obtain the forces at the connections between the metallic rods and the vertebrae geometrically, non-linear finite element analyses are performed. The tightening sequence determines the magnitude of the forces applied to the patient column, and it is desirable to keep those forces as small as possible. In this study, a Genetic Algorithm optimization is applied to this model in order to determine the sequence that minimizes the corrective forces applied during the surgery. This amounts to find the optimal permutation of integers 1, ... , n, n being the number of vertebrae involved. As such, we are faced with a combinatorial optimization problem isomorph to the Traveling Salesman Problem. The fitness evaluation requires one computing intensive Finite Element Analysis per candidate solution and, thus, a parallel implementation of the Genetic Algorithm is developed.

Optimal design of piezolaminated structures using B-spline strip finite element models

A package of B-spline finite strip models is developed for the linear analysis of piezolaminated plates and shells. This package is associated to a global optimization technique in order to enhance the performance of these types of structures, subjected to various types of objective functions and/or constraints, with discrete and continuous design variables. The models considered are based on a higher-order displacement field and one can apply them to the static, free vibration and buckling analyses of laminated adaptive structures with arbitrary lay-ups, loading and boundary conditions. Genetic algorithms, with either binary or floating point encoding of design variables, were considered to find optimal locations of piezoelectric actuators as well as to determine the best voltages applied to them in order to obtain a desired structure shape. These models provide an overall economy of computing effort for static and vibration problems.

GA topology optimization using random keys for tree encoding of structures

Topology optimization consists in finding the spatial distribution of a given total volume of material for the resulting structure to have some optimal property, for instance, maximization of structural stiffness or maximization of the fundamental eigenfrequency. In this paper a Genetic Algorithm (GA) employing a representation method based on trees is developed to generate initial feasible individuals that remain feasible upon crossover and mutation and as such do not require any repairing operator to ensure feasibility. Several application examples are studied involving the topology optimization of structures where the objective functions is the maximization of the stiffness and the maximization of the first and the second eigenfrequencies of a plate, all cases having a prescribed material volume constraint.

Localização ótima de apoios em estruturas do tipo viga utilizando o método dos algoritmos genéticos

Dissertação de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica Ramo de Manutenção e Produção

Algoritmos evolutivos na determinação das máximas injeções nodais em redes de energia elétrica

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica - Ramo de Energia

Sistema de visão para aterragem automática de UAV

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Automação e Electrónica Industrial

Automatic tunning of Okumura-Hata model on railway communications

This paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.

Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method

A multiobjective approach for optimization of passive damping for vibration reduction in sandwich structures is presented in this paper. Constrained optimization is conducted for maximization of modal loss factors and minimization of weight of sandwich beams and plates with elastic laminated constraining layers and a viscoelastic core, with layer thickness and material and laminate layer ply orientation angles as design variables. The problem is solved using the Direct MultiSearch (DMS) solver for derivative-free multiobjective optimization and solutions are compared with alternative ones obtained using genetic algorithms.

The influence of genetic polymorphisms in XRCC3 and ADH5 genes on the frequency of genotoxicity biomarkers in workers exposed to formaldehyde

The International Agency for Research on Cancer classified formaldehyde as carcinogenic to humans because there is “sufficient epidemiological evidence that it causes nasopharyngeal cancer in humans”. Genes involved in DNA repair and maintenance of genome integrity are critically involved in protecting against mutations that lead to cancer and/or inherited genetic disease. Association studies have recently provided evidence for a link between DNA repair polymorphisms and micronucleus (MN) induction. We used the cytokinesis-block micronucleus (CBMN assay) in peripheral lymphocytes and MN test in buccal cells to investigate the effects of XRCC3 Thr241Met, ADH5 Val309Ile, and Asp353Glu polymorphisms on the frequency of genotoxicity biomarkers in individuals occupationally exposed to formaldehyde (n = 54) and unexposed workers (n = 82). XRCC3 participates in DNA double-strand break/recombination repair, while ADH5 is an important component of cellular metabolism for the elimination of formaldehyde. Exposed workers had significantly higher frequencies (P < 0.01) than controls for all genotoxicity biomarkers evaluated in this study. Moreover, there were significant associations between XRCC3 genotypes and nuclear buds, namely XRCC3 Met/Met (OR = 3.975, CI 1.053–14.998, P = 0.042) and XRCC3 Thr/Met (OR = 5.632, CI 1.673–18.961, P = 0.005) in comparison with XRCC3 Thr/Thr. ADH5 polymorphisms did not show significant effects. This study highlights the importance of integrating genotoxicity biomarkers and genetic polymorphisms in human biomonitoring studies.

Comparison of different breast planning techniques and algorithms for radiation therapy treatment

This work aims at investigating the impact of treating breast cancer using different radiation therapy (RT) techniques – forwardly-planned intensity-modulated, f-IMRT, inversely-planned IMRT and dynamic conformal arc (DCART) RT – and their effects on the whole-breast irradiation and in the undesirable irradiation of the surrounding healthy tissues. Two algorithms of iPlan BrainLAB treatment planning system were compared: Pencil Beam Convolution (PBC) and commercial Monte Carlo (iMC). Seven left-sided breast patients submitted to breast-conserving surgery were enrolled in the study. For each patient, four RT techniques – f-IMRT, IMRT using 2-fields and 5-fields (IMRT2 and IMRT5, respectively) and DCART – were applied. The dose distributions in the planned target volume (PTV) and the dose to the organs at risk (OAR) were compared analyzing dose–volume histograms; further statistical analysis was performed using IBM SPSS v20 software. For PBC, all techniques provided adequate coverage of the PTV. However, statistically significant dose differences were observed between the techniques, in the PTV, OAR and also in the pattern of dose distribution spreading into normal tissues. IMRT5 and DCART spread low doses into greater volumes of normal tissue, right breast, right lung and heart than tangential techniques. However, IMRT5 plans improved distributions for the PTV, exhibiting better conformity and homogeneity in target and reduced high dose percentages in ipsilateral OAR. DCART did not present advantages over any of the techniques investigated. Differences were also found comparing the calculation algorithms: PBC estimated higher doses for the PTV, ipsilateral lung and heart than the iMC algorithm predicted.

Clinical and genetic factors predicting response to therapy in patients with Crohn’s disease

Aim - To identify clinical and/or genetic predictors of response to several therapies in Crohn’s disease (CD) patients. Methods - We included 242 patients with CD (133 females) aged (mean ± standard deviation) 39 ± 12 years and a disease duration of 12 ± 8 years. The single-nucleotide polymorphisms (SNPs) studied were ABCB1 C3435T and G2677T/A, IL23R G1142A, C2370A, and G9T, CASP9 C93T, Fas G670A and LgC844T, and ATG16L1 A898G. Genotyping was performed with real-time PCR with Taqman probes. Results - Older patients responded better to 5-aminosalicylic acid (5-ASA) and to azathioprine (OR 1.07, p = 0.003 and OR 1.03, p = 0.01, respectively) while younger ones responded better to biologicals (OR 0.95, p = 0.06). Previous surgery negatively influenced response to 5-ASA compounds (OR 0.25, p = 0.05), but favoured response to azathioprine (OR 2.1, p = 0.04). In respect to genetic predictors, we observed that heterozygotes for ATGL16L1 SNP had a significantly higher chance of responding to corticosteroids (OR 2.51, p = 0.04), while homozygotes for Casp9 C93T SNP had a lower chance of responding both to corticosteroids and to azathioprine (OR 0.23, p = 0.03 and OR 0.08, p = 0.02,). TT carriers of ABCB1 C3435T SNP had a higher chance of responding to azathioprine (OR 2.38, p = 0.01), while carriers of ABCB1 G2677T/A SNP, as well as responding better to azathioprine (OR 1.89, p = 0.07), had a lower chance of responding to biologicals (OR 0.31, p = 0.07), which became significant after adjusting for gender (OR 0.75, p = 0.005). Conclusions - In the present study, we were able to identify a number of clinical and genetic predictors of response to several therapies which may become of potential utility in clinical practice. These are preliminary results that need to be replicated in future pharmacogenomic studies.

Detecção de sectores cruzados e optimização de cobertura em redes LTE

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.