近红外光谱测定NEPE胶组分含量

研究了近红外光谱测定NEPE胶中硝化甘油、丁三醇三硝酸酯、聚乙二醇含量的方法,采用相关系数、显变分析、间隔偏最小二乘法、遗传算法等方法选择波长,对相关实验条件和影响因素进行了研究.实验结果表明,数据平移处理可以有效改善样品吸光度与浓度之间的相关性;遗传算法选择波长优于相关系数法和间隔偏最小二乘法,显变分析不适合于本项研究;近红外光谱法快速测定NEPE胶组分含量的结果与标准分析方法的测定结果一致,两者相差小于1%;水分含量的变化、正常加热操作对NEPE胶组分含量测定的影响不明显.     

Characterization of lunar soils through spectral features extraction in the NIR

Recently launched hyper-spectral instrumentation with ever-increasing data return capabilities deliver the remote-sensing data to characterize planetary soils with increased precision, thus generating the need to classify the returned data in an efficient way for further specialized analysis and detection of features of interest. This paper investigates how lunar near-infrared spectra generated by the SIR-2 on Chandrayaan-1 can be classified into distinctive groups of similar spectra with automated feature extraction algorithms. As common spectral parameters for the SIR-2 spectra, two absorption features near 1300 nm and 2000 and their characteristics provide 10 variables which are used in two different unsupervised clustering methods, the mean-shift clustering algorithm and the recently developed graph cut-based clustering algorithm by Müller et al. (2012). The spectra used in this paper were taken on the lunar near side centering around the Imbrium region of the Moon. More than 100,000 spectra were analyzed.     

基于改进极限学习机的光谱定量建模方法

依据近红外光谱(NIR)产生原理,提出了粒子群优化极限学习机(PSO-ELM)算法,运用于小样本氨水浓度定量分析。通过优化极限学习机(ELM)隐藏节点参数,解决了极限学习机由于输入权值和隐含层偏差随机产生的建模结果具有随机性的问题,提高了预测模型的稳定性、精确度和泛化性能。经实验验证,优化后的PSO-ELM相比ELM,模型预测集均方根误差由0.01166减小至0.00322,预测集相关系数由0.9951提高至0.9979。将优化后的模型预测结果与支持向量机(SVM)、BP神经网络算法等传统方法的建模结果进行对比,优化后的PSO-ELM算法具有较高的精确度和良好的泛化性能,模型预测效果优于传统的定量回归分析算法。