Modeling random corrosion processes via polynomial chaos expansion

Polynomial Chaos Expansion (PCE) is widely recognized as a flexible tool to represent different types of random variables/processes. However, applications to real, experimental data are still limited. In this article, PCE is used to represent the random time-evolution of metal corrosion growth in marine environments. The PCE coefficients are determined in order to represent data of 45 corrosion coupons tested by Jeffrey and Melchers (2001) at Taylors Beach, Australia. Accuracy of the representation and possibilities for model extrapolation are considered in the study. Results show that reasonably accurate smooth representations of the corrosion process can be obtained. The representation is not better because a smooth model is used to represent non-smooth corrosion data. Random corrosion leads to time-variant reliability problems, due to resistance degradation over time. Time variant reliability problems are not trivial to solve, especially under random process loading. Two example problems are solved herein, showing how the developed PCE representations can be employed in reliability analysis of structures subject to marine corrosion. Monte Carlo Simulation is used to solve the resulting time-variant reliability problems. However, an accurate and more computationally efficient solution is also presented.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Improvement of metabolic parameters and vascular function by metformin in obese non-diabetic rats

Aims: Metformin is an insulin sensitizing agent with beneficial effects in diabetic patients on glycemic levels and in the cardiovascular system. We examined whether the metabolic changes and the vascular dysfunction in monosodium glutamate-induced obese non-diabetic (MSG) rats might be improved by metformin. Main methods: 16 week-old MSG rats were treated with metformin for 15 days and compared with age-matched untreated MSG and non-obese non-diabetic rats (control). Blood pressure, insulin sensitivity, vascular reactivity and prostanoid release in the perfused mesenteric arteriolar bed as well as nitric oxide production and reactive oxygen species generation in isolated mesenteric arteries were analyzed. Key findings: 18-week-old MSG rats displayed higher Lee index, fat accumulation, dyslipidemia, insulin resistance and hyperinsulinemia. Metformin treatment improved these alterations. The norepinephrine-induced response, increased in the mesenteric arteriolar bed from MSG rats, was corrected by metformin. Indomethacin corrected the enhanced contractile response in MSG rats but did not affect metformin effects. The sensitivity to acetylcholine, reduced in MSG rats, was also corrected by metformin. Indomethacin corrected the reduced sensitivity to acetylcholine in MSG rats but did not affect metformin effects. The sensitivity to sodium nitroprusside was increased in preparations from metformin-treated rats. Metformin treatment restored both the reduced PGI2/TXA2 ratio and the increased reactive oxygen species generation in preparations from MSG rats. Significance: Metformin improved the vascular function in MSG rats through reduction in reactive oxygen species generation, modulation of membrane hyperpolarization. correction of the unbalanced prostanoids release and increase in the sensitivity of the smooth muscle to nitric oxide. (c) 2011 Elsevier Inc. All rights reserved.

Prevalence of non-organ-specific autoantibodies in a rural community from northeastern Brazil: a population-based study

Non-organ-specific autoantibodies (NOSA) are well-recognized diagnostic markers of autoimmune hepatitis (All-l) and primary biliary cirrhosis (PBC), but can also be observed in patients with viral hepatitis as well as in healthy subjects. The aim of this study was to evaluate the prevalence of NOSA in subjects living in a rural community in Brazil and to correlate their occurrence with the presence of liver disease. Seven hundred twenty-five apparently healthy subjects were randomly selected for assessment of antinuclear (ANA), anti-smooth muscle (SMA), antimitochondrial (AMA), anti-liver/kidney microsome type 1, and anti-liver cytosol type 1 antibodies. Subjects with those NOSA were evaluated for the presence of AIH, PBC, and viral hepatitis. Reactivities for all NOSA, SMA, ANA, and AMA were detected, respectively, in 14, 10, 4, and 0.1% of subjects, with a mean titer of 1:40. NOSA-positive subjects were significantly older and more frequently females. No correlation was observed between the occurrence of NOSA and PBC. AIH, or viral hepatitis. The prevalence of NOSA in Brazilians was 14%. They were usually low titer. NOSA were more frequently observed in females and older subjects and their presence was not correlated with the presence of AIH, PBC, or viral hepatitis. (C) 2012 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

Analog circuit synthesis performing fast Pareto frontier exploration and analysis through 3D graphs

This paper presents a technique for performing analog design synthesis at circuit level providing feedback to the designer through the exploration of the Pareto frontier. A modified simulated annealing which is able to perform crossover with past anchor points when a local minimum is found which is used as the optimization algorithm on the initial synthesis procedure. After all specifications are met, the algorithm searches for the extreme points of the Pareto frontier in order to obtain a non-exhaustive exploration of the Pareto front. Finally, multi-objective particle swarm optimization is used to spread the results and to find a more accurate frontier. Piecewise linear functions are used as single-objective cost functions to produce a smooth and equal convergence of all measurements to the desired specifications during the composition of the aggregate objective function. To verify the presented technique two circuits were designed, which are: a Miller amplifier with 96 dB Voltage gain, 15.48 MHz unity gain frequency, slew rate of 19.2 V/mu s with a current supply of 385.15 mu A, and a complementary folded cascode with 104.25 dB Voltage gain, 18.15 MHz of unity gain frequency and a slew rate of 13.370 MV/mu s. These circuits were synthesized using a 0.35 mu m technology. The results show that the method provides a fast approach for good solutions using the modified SA and further good Pareto front exploration through its connection to the particle swarm optimization algorithm.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

Design methodology of piezoelectric energy-harvesting skin using topology optimization

Abstract This paper describes a design methodology for piezoelectric energy harvester s that thinly encapsulate the mechanical devices and expl oit resonances from higher- order vibrational modes. The direction of polarization determines the sign of the pi ezoelectric tensor to avoid cancellations of electric fields from opposite polarizations in the same circuit. The resultant modified equations of state are solved by finite element method (FEM). Com- bining this method with the solid isotropic material with penalization (SIMP) method for piezoelectric material, we have developed an optimization methodology that optimizes the piezoelectric material layout and polarization direc- tion. Updating the density function of the SIMP method is performed based on sensitivity analysis, the sequen- tial linear programming on the early stage of the opti- mization, and the phase field method on the latter stage

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.