中文摘要：

提出了利用高光谱成像技术结合化学计量学方法实现在线无损预测光伏电池中乙烯-醋酸乙烯共聚物（EVA）胶膜层压温度的方法。四类EVA胶膜层压的温度控制在128,132,142和148℃。采集的高光谱波段范围在904.58~1 700.01nm之间。每类样本建模集包含90个样本,预测集包含10个样本。从获得的EVA胶膜高光谱图像中选取150×150像素大小的感兴趣区域,并以该区域内所有的像素点包含的光谱反射率平均值作为该样本的光谱特征曲线。分别采用偏最小二乘回归法、多分类支持向量机法和大间隔最邻近法对高光谱薄膜样本层压温度进行建模和预测。权重回归系数图表明短波和长波近红外波段高光谱数据对层压温度预测都有贡献。由于EVA高分子材料反射高光谱数据表现出了较强的非线性性,偏最小二乘法预报性能受到较大影响,为95%,而基于核方法的预测模型在高维特征空间一定程度消除了EVA高分子材料测量光谱非线性特性的影响,较为准确地反映原始EVA高分子材料光谱数据与层压温度之间的关系,比较上述三种模型的预测精度可知,大间隔最邻近模型对EVA胶膜层压温度的预测精度率最高,达到100%。结果表明,应用高光谱成像技术在线无损预测EVA胶膜层压温度是可行的,为实现光伏电池夹层中EVA高分子材料封装温度自动监测与控制创造了条件。

英文摘要：

A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer（EVA）films in photovoltaic cells during the thermal encapsulation process.Four varieties of the EVA films which had been heated at the temperatures of 128,132,142 and 148℃ during the photovoltaic cells production process were used for investigation in this paper.These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment（Spectral Imaging Ltd.Oulu,Finland）.The scanning band range of hyperspectral equipemnt was set between 904.58 and 1 700.01 nm.The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose.Each type of the training set and test set contained 90 and 10instances,respectively.The obtained hyperspectral images of EVA films were dealt with by using the ENVI（Exelis Visual Information Solutions,USA）software.The size of region of interest（ROI）of each obtained hyperspectral image of EVA film was set as 150×150pixels.The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance.There kinds of chemometrics methods including partial least squares regression（PLSR）,multi-class support vector machine（SVM）and large margin nearest neighbor（LMNN）were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films.The plot of weighted regression coefficients illustrated that both bands of shortand long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model.Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity,the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%.The kernel-based forecast models were introduced to eliminate the impact of nonlinear hyperspect