无机化学多元信息化教学模式的构建与实践研究

针对现代大学生的移动阅读习惯和信息化学习需求,构建了无机化学多元信息化教学模式。介绍了构建这种教学模式所需微信公众号、微助教和QQ群作业的使用方法。初步探讨了无机化学多元信息化教学模式在教学中的实践效果,弥补了传统教学中的不足。     

Lunar terrain and mineral's abundance automatic analysis

The image is the important data in the lunar exploration. The main objective of this work is to detection lunar craters with the multi-information fuzzy logic method, and to quantify the image's terrain and the abundance of lunar surface minerals based on crater distribution law and soil characterization consortium data set with lunar surface reflectance. We implement image processing to recognize lunar crater and analysis lunar terrain; use lunar reflectance model to solve mineral reflectance question; joint commonly used look-up lunar reflectance tables and least squares to solve lunar surface reflectance questions; and calculate the minerals reflectance, region reflectance, and then estimate abundance of lunar surface soil minerals. An actual lunar image (Apollo 15 landsite, Clementine mission) of Mare region as an example, this method analysis results of the terrain and mineral's abundance are basically same with published literature. In the future, this method can be simple rapid in-time implemented in real-time lunar exploration.     

多信息融合的冬小麦地上鲜生物量检测研究

将小麦冠层光谱与小麦冠层图像或者侧面图像进行多信息融合,对冬小麦地上鲜生物量进行预测,提高了冬小麦地上鲜生物量无损检测精度,试验获取苗期93个样本的冠层光谱和冠层图像,中后期(拔节期、抽穗期、开花期、灌浆期)200个样本的冠层光谱和侧面图像。将光谱反射率作为光谱特征参数,并通过图像处理提取小麦覆盖度作为图像特征参数,建立苗期和中后期基于光谱特征参数和图像特征参数的地上生物量检测模型,将冠层光谱反射率和小麦覆盖度作为多信息融合的输入,利用多元回归分析(MRA)和偏最小二乘法(PLS)建立地上鲜生物量预测模型并进行验证。结果表明,在苗期和中后期,将光谱信息和图像信息融合,采用PLS所建立的预测模型与单独的图像模型和光谱模型相比精度最高。苗期基于信息融合所建立的PLS模型验证集R2为0.881,其RMSE为0.015 kg;中后期基于信息融合所建立的PLS模型验证集R2为0.791,RMSE为0.059 kg。由此可见,相比单一的光谱模型和图像模型,图像信息和光谱信息融合之后,充分提高了光谱信息和图像信息的利用率,使模型的精度得以提高。