ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

With more and more types of near infrared spectroscopy detectors, the inability to share calibration models between different instruments has become the main problem that limits its application, and calibration transfer has become the key to solve this problem. Taking edible oil as the research object, the extreme learning machine model of its acid value on the master instrument was established. And the TrAdaBoost algorithm in transfer learning was used to transfer the master model to the slave model, and the dependence of calibration transfer on the number of standardization samples was explored. It was also compared with the direct standardized, missing data recovery and transfer via extreme learning machine autoencoder method. The results showed that the predictive power of the slave samples after the TrAdaBoost calibration transfer algorithm was most effective and very close to the predictive value of the master sample-master model. The R2 of the validation set was increased from 0.489 to 0.892, the root mean square error of prediction (RMSEP) was reduced from 4.824mg/g to 0.267mg/g. Specifically, the model effect was almost independent of the number of standardized samples. The next degree of effect was the transfer via extreme learning machine auto-encoder method algorithm (TEAM), the missing data recovery algorithm (MDR) and direct standardized algorithm (DS) in decreasing order, respectively. It was shown that the TrAdaBoost can be effectively applied to the calibration transfer, and it can realize the communication between different spectroscopic instruments, which provided an idea for the calibration transfer.