Coal identification based on a deep network and reflectance spectroscopy.

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra.

Iron ore identification method using reflectance spectrometer and a deep neural network framework.

In the first selection stage of iron ore, the ore classification accuracy plays a decisive role in subsequent work. Therefore, how to identify iron ore quickly and accurately is an important task. Traditional chemical, physical and manual identification methods have the disadvantages of high costs and high time consumption.

An Ore Image Segmentation Method Based on RDU-Net Model.

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation.

Hyperspectral remote sensing image classification based on random average band selection and an ensemble kernel extreme learning machine.

Hyperspectral remote sensing technology can explore a lot of information about ground objects, and the information is not explored in multispectral technology. This study proposes a hyperspectral remote sensing image classification method. First, we preprocess the hyperspectral data to obtain the average spectral information of the pixels; the average spectral information contains spectral-spatial features.

[Research in Magnesite Grade Classification Based on Near Infrared Spectroscopy and ELM Algorithm].

Due to the needs of industrial development, the different content and uncertain distribution of magnesite mineral lead to great difficulties in o determining its grade, therefore, we propose a combination of near-infrared spectroscopy and the ELM magnesite grade classification model. The model can achieve rapid classification of magnesite grade. Near infrared spectroscopy, considering that different types of H group in magnesite have different absorption degrees to near-infrared spectroscopy, is used to determine the composition and content of magnesite.