选取具有代表性的不同镀层质量及不同铅含量的电镀锡板样品为校准样品,采用欧盟BS EN 10333-2005（包装用钢与人和动物用食品、产品和饮料接触的扁平钢制品镀锡钢）标准要求的方法测定校准样品中镀锡层铅含量,解决了电镀锡板校准样品短...选取具有代表性的不同镀层质量及不同铅含量的电镀锡板样品为校准样品,采用欧盟BS EN 10333-2005（包装用钢与人和动物用食品、产品和饮料接触的扁平钢制品镀锡钢）标准要求的方法测定校准样品中镀锡层铅含量,解决了电镀锡板校准样品短缺的问题,并依此建立了X射线荧光光谱法直接测定电镀锡板样品中单位镀锡层质量中铅含量的分析方法.利用仪器自带的薄膜分析软件将样品分为两层（基板层和镀锡层）来逐层分析,校正了基板中铅造成的测量干扰.采用仪器自带的基本参数法（FP）软件,自动校正了单位镀锡层质量不同造成的锡元素干扰,使校准曲线的相关系数达到0.993.结果表明,方法适用于单位镀锡层质量为2.0 g/m2及以上,基板厚度大于0.2 mm样品的测定.采用方法对电镀锡板实际样品进行分析,测定结果与欧盟BS EN 10333标准方法吻合,相对标准偏差（n=6）在4％～20％之间.展开更多
We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 （SDSS DR2） Galaxy Sample using artificial neural networks （ANNs）. Different input sets based on various parameters （e.g. magnitu...We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 （SDSS DR2） Galaxy Sample using artificial neural networks （ANNs）. Different input sets based on various parameters （e.g. magnitude, color index, flux information） are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance resuits when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.展开更多
文摘选取具有代表性的不同镀层质量及不同铅含量的电镀锡板样品为校准样品,采用欧盟BS EN 10333-2005（包装用钢与人和动物用食品、产品和饮料接触的扁平钢制品镀锡钢）标准要求的方法测定校准样品中镀锡层铅含量,解决了电镀锡板校准样品短缺的问题,并依此建立了X射线荧光光谱法直接测定电镀锡板样品中单位镀锡层质量中铅含量的分析方法.利用仪器自带的薄膜分析软件将样品分为两层（基板层和镀锡层）来逐层分析,校正了基板中铅造成的测量干扰.采用仪器自带的基本参数法（FP）软件,自动校正了单位镀锡层质量不同造成的锡元素干扰,使校准曲线的相关系数达到0.993.结果表明,方法适用于单位镀锡层质量为2.0 g/m2及以上,基板厚度大于0.2 mm样品的测定.采用方法对电镀锡板实际样品进行分析,测定结果与欧盟BS EN 10333标准方法吻合,相对标准偏差（n=6）在4％～20％之间.
基金the National Natural Science Foundation of China
文摘We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 （SDSS DR2） Galaxy Sample using artificial neural networks （ANNs）. Different input sets based on various parameters （e.g. magnitude, color index, flux information） are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance resuits when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.
