Fitting Isochrones to Open Cluster photometric data: A new global optimization tool

We present a new technique to fit color-magnitude diagrams of open clusters based on the Cross-Entropy global optimization algorithm. The method uses theoretical isochrones available in the literature and maximizes a weighted likelihood function based on distances measured in the color-magnitude space. The weights are obtained through a non parametric technique that takes into account the star distance to the observed center of the cluster, observed magnitude uncertainties, the stellar density profile of the cluster among others. The parameters determined simultaneously are distance, reddening, age and metallicity. The method takes binary fraction into account and uses a Monte-Carlo approach to obtain uncertainties on the determined parameters for the cluster by running the fitting algorithm many times with a re-sampled data set through a bootstrapping procedure. We present results for 9 well studied open clusters, based on 15 distinct data sets, and show that the results are consistent with previous studies. The method is shown to be reliable and free of the subjectivity of most previous visual isochrone fitting techniques.Comment: 19 pages, 25 figures, accepted for publication in Astronomy&Astrophysic

Interpolation of nonstationary high frequency spatial-temporal temperature data

The Atmospheric Radiation Measurement program is a U.S. Department of Energy project that collects meteorological observations at several locations around the world in order to study how weather processes affect global climate change. As one of its initiatives, it operates a set of fixed but irregularly-spaced monitoring facilities in the Southern Great Plains region of the U.S. We describe methods for interpolating temperature records from these fixed facilities to locations at which no observations were made, which can be useful when values are required on a spatial grid. We interpolate by conditionally simulating from a fitted nonstationary Gaussian process model that accounts for the time-varying statistical characteristics of the temperatures, as well as the dependence on solar radiation. The model is fit by maximizing an approximate likelihood, and the conditional simulations result in well-calibrated confidence intervals for the predicted temperatures. We also describe methods for handling spatial-temporal jumps in the data to interpolate a slow-moving cold front.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS633 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Tracking moving optima using Kalman-based predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison

Comparison of Gaussian process modeling software

Gaussian process fitting, or kriging, is often used to create a model from a set of data. Many available software packages do this, but we show that very different results can be obtained from different packages even when using the same data and model. We describe the parameterization, features, and optimization used by eight different fitting packages that run on four different platforms. We then compare these eight packages using various data functions and data sets, revealing that there are stark differences between the packages. In addition to comparing the prediction accuracy, the predictive variance--which is important for evaluating precision of predictions and is often used in stopping criteria--is also evaluated

A Prediction Modeling Framework For Noisy Welding Quality Data

Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction modeling framework, which employs bootstrap aggregating (bagging) with support vector regression (SVR) as the base learning algorithm to improve the prediction accuracy on such noisy data. Optimal hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling. Constructing bagging models require 114 more computational costs than a single model. Also, evolutionary computation algorithms, such as PSO, generally require a large number of candidate solution evaluations to achieve quality solutions. These two requirements greatly increase the overall computational cost in constructing effective bagging SVR models. Meta-modeling can be employed to reduce the computational cost when the fitness or constraints functions are associated with computationally expensive tasks or analyses. In our case, the objective function is associated with constructing bagging SVR models with candidate sets of hyper-parameters. Therefore, in regards to PSO, a large number of bagging SVR models have to be constructed and evaluated, which is computationally expensive. The meta-modeling approach, called MUGPSO, developed in this research assists PSO in evaluating these candidate solutions (i.e., sets of hyper-parameters). MUGPSO approximates the fitness function of candidate solutions. Through this method, the numbers of real fitness function evaluations (i.e., constructing bagging SVR models) are reduced, which also reduces the overall computational costs. Using the Meta2 framework, one can expect an improvement in the prediction accuracy with reduced computational time. Experiments are conducted on three artificially generated noisy datasets and a real RSW quality dataset. The results indicate that Meta2 is capable of providing promising solutions with noticeably reduced computational costs

Proportional Topology Optimization: A new non-gradient method for solving stress constrained and minimum compliance problems and its implementation in MATLAB

A new topology optimization method called the Proportional Topology Optimization (PTO) is presented. As a non-gradient method, PTO is simple to understand, easy to implement, and is also efficient and accurate at the same time. It is implemented into two MATLAB programs to solve the stress constrained and minimum compliance problems. Descriptions of the algorithm and computer programs are provided in detail. The method is applied to solve three numerical examples for both types of problems. The method shows comparable efficiency and accuracy with an existing gradient optimality criteria method. Also, the PTO stress constrained algorithm and minimum compliance algorithm are compared by feeding output from one algorithm to the other in an alternative manner, where the former yields lower maximum stress and volume fraction but higher compliance compared to the latter. Advantages and disadvantages of the proposed method and future works are discussed. The computer programs are self-contained and publicly shared in the website www.ptomethod.org.Comment: 18 pages, 8 figures, and 2 appendices (MATLAB codes

Solar-like oscillations in the G9.5 subgiant beta Aquilae

An interesting asteroseismic target is the G9.5 IV solar-like star beta Aql. This is an ideal target for asteroseismic investigations, because precise astrometric measurements are available from Hipparcos that greatly help in constraining the theoretical interpretation of the results. The star was observed during six nights in August 2009 by means of the high-resolution \'echelle spectrograph SARG operating with the TNG 3.58 m Italian telescope on the Canary Islands, exploiting the iodine cell technique. We present the result and the detailed analysis of high-precision radial velocity measurements, where the possibility of detecting time individual p-mode frequencies for the first and deriving their corresponding asymptotic values will be discussed. The time-series analysis carried out from \sim 800 collected spectra shows the typical p-mode frequency pattern with a maximum centered at 416 \muHz. In the frequency range 300 - 600 \muHz we identified for the first time six high S/N (\gtrsim 3.5) modes with l = 0,2 and 11 < n < 16 and three possible candidates for mixed modes (l = 1), although the p-mode identification for this type of star appears to be quite difficult owing to a substantial presence of avoided crossings. The large frequency separation and the surface term from the set of identified modes by means of the asymptotic relation were derived for the first time. Their values are \Delta \nu = 29.56 \pm 0.10 \muHz and \epsilon = 1.29 \pm 0.04, consistent with expectations. The most likely value for the small separation is \delta\nu_{02} = 2.55 \pm 0.71 \muHz.Comment: 8 pages, 8 figures, 3 tables, accepted by A&

ACOUSTIC DESIGN OPTIMIZATION WITH ISOGEOMETRIC ANALYSIS AND DIFFERENTIAL EVOLUTION

The objective of this study is to utilize shape optimization to enhance the performance of devices relying on acoustic wave propagation. Particularly, the shape of a horn speaker and an acoustic energy harvester were optimized to enhance their performance at targeted frequencies. High order Isogeometric Analysis (IGA) was performed to estimate the acoustic pressure with minimum geometry and pollution errors [1]. The analysis platform was then combined with Differential Evolution (DE) to optimize the geometry of the horn speaker and energy harvester at a given frequency. These cases effectively demonstrate two applications of Isogeomtric shape optimization for devices relying on acoustic wave propagation. The horn shape was previously optimized using conventional FEA [2]. The study performed to optimize the sound energy harvester demonstrates the effectiveness of isogeometric shape optimization for novel applications. It was shown that the proposed platform can generate tunable designs that reach their optimum performance at the desired frequencies. The back-reflection of the horn speaker was reduced considerably by optimizing the shape of the horn boundary. Tikhonov regularization was used to avoid finding wiggly solutions and ensure ease of manufacturing. The geometry of the energy harvester was optimized and tuned for a range of targeted frequencies by optimizing its defining parameters, its placement angle, and developing an optimized variable channel width. The DE algorithm, which is known for finding the global minimum, successfully updated the design geometries and identified the global minimum in most cases studied in this thesis