Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Correcting systematic effects in a large set of photometric light curves

We suggest a new algorithm to remove systematic effects in a large set of light curves obtained by a photometric survey. The algorithm can remove systematic effects, such as those associated with atmospheric extinction, detector efficiency, or point spread function changes over the detector. The algorithm works without any prior knowledge of the effects, as long as they linearly appear in many stars of the sample. The approach, which was originally developed to remove atmospheric extinction effects, is based on a lower rank approximation of matrices, an approach which has already been suggested and used in chemometrics, for example. The proposed algorithm is especially useful in cases where the uncertainties of the measurements are unequal. For equal uncertainties, the algorithm reduces to the Principal Component Analysis (PCA) algorithm. We present a simulation to demonstrate the effectiveness of the proposed algorithm and we point out its potential, in the search for transit candidates in particular.     

Real-time difference imaging analysis of MOA Galactic bulge observations during 2000

We describe observations carried out by the MOA group of the Galactic bulge during 2000 that were designed to detect efficiently gravitational microlensing of faint stars in which the magnification is high and/or of short duration. These events are particularly useful for studies of extrasolar planets and faint stars. Approximately 17 deg² were monitored at a sampling rate of up to six times per night. The images were analysed in real time using a difference imaging technique. 20 microlensing candidates were detected, of which eight were alerted to the microlensing community whilst in progress. Approximately half of the candidates had high magnifications (≳10), at least one had very high magnification (≳50), and one exhibited a clear parallax effect. The details of these events are reported here, together with details of the on-line difference imaging technique. Some nova-like events were also observed and these are described, together with one asteroid.     

Rotational broadening and Doppler tomography of the quiescent X-ray nova Centaurus X-4

We present a library of 47 open-cluster integrated spectra, mostly obtained at CASLEO (San Juan, Argentina) in the range  3600 < λ < 7400 Å  , which are made available at CDS. The data are combined with previous spectra to obtain 10 high signal-to-noise ratio basic templates in the young and intermediate-age domains, which are also provided in the library. These Galactic disc templates represent the increased time resolution spectral evolution of a stellar population unit around the Solar metallicity level. The improved signal-to-noise ratio of the present templates with respect to previous template lists, together with their increased time resolution, allowed us to improve the fundamental parameters of some open clusters. The present spectral library will be useful for several astrophysical applications, particularly for population syntheses of star-forming giant galaxies.     

Readout Techniques for Drift and Low Frequency Noise Rejection in Infrared Arrays

Three different methods are presented to subtract thermal drifts and low-frequency noise from the signal of infrared array. The first is dead pixels with open Indium bumps, the second is reference output as implemented on the Hawaii2 multiplexer, and the third is dark pixels to emulate reference cells having a capacity connected to the gate of the unit cell field-effect transistor (FET). The third method is the most effective and yields a reduction in readout noise from15.4–9.4 erms. A novel method will be described to extend this readout technique to the Aladdin 1 K × 1 K InSb array. This revised version was published online in July 2006 with corrections to the Cover Date.     

雾化进样石墨炉原子吸收光谱法的研究 Ⅰ.直接雾化进样装置及其性能

介绍一种自行设计和加工的直接雾化石墨炉进样装置的结构、工作原理和操作方法。该装置由微机控制与石墨炉加热程序同步工作,可以实现自动控制和自动测量。对雾化进样石墨炉分析的仪器参数和实验条件进行了研究和选择。结果表明,该装置与石墨炉结合具有自动化程度高、重现性好、样品利用率高和分析速度快等特点。