The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson–Kendall–Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters’ range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.

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The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures.

Hyperspectral LiDAR (HSL) has been widely discussed in recent years, which attracts increasing attention of the researchers in the field of electronic information technology. With the application of supercontinuum laser source, it is now possible to develop an HSL system, which can collect spectral and spatial information of targets simultaneously. Meanwhile, eye-safety and miniature HSL device with multiple spectral bands are given more priorities in on-site applications. In this paper, we tempt to investigate how to select spectral bands with a selection method. The proposed method consists of three steps: first, the variances among the classes based on hyperspectral feature parameters, termed inter-class variances, are calculated; second, the channels are sorted based on corresponding variances in descending order, and those with the two highest values are adopted as the initial input of classification; finally, the channels are selected successively from the rest of the sorted sequence until the classification accuracy reaches 100%. To test the performance of the proposed method, we collect 91/71-channel hyperspectral measurements of four different categories of materials with 5 nm spectral resolution using an acousto-optic tunable filter (AOTF) based HSL. Experimental results demonstrate that the proposed method could achieve higher classification accuracy than a random band selection method with different classifiers (naïve Bayes (NB) and support vector machine (SVM)) regardless of classification feature parameters (echo maximum and reflectance). To reach 100% accuracy, it demands 8–9 channels on average by echo maximum and 4–5 channels on average by reflectance based on NB classifier; these figures are 3–4 by echo maximum and 2–3 by reflectance with SVM classifier. The proposed method can complete classification task much faster than the random selection method. We further confirm the specific channels for the classification of different materials, and find that the optimal channels vary with different materials. The experimental results prove that the optimal band selection of HSL system for classification is reliable.

The coupling between optically active substances of algae particles and inorganic suspended solids of water makes the characteristics of reflection spectra of water complex and changeable. This makes modeling and inversion of polarization remote sensing in class II water difficult. In our study, considering the influence of the mixing ratio of algae particles and inorganic suspended solids, the sensor incidence angle, and the solar zenith angle on the polarization reflection spectrum, we analyzed the coupling characteristics of the polarized bidirectional reflectance of particulate matter through control experiments of mixed components of water particles in the laboratory. With Chaohu Lake in China as an example, the polarized reflectance coupling characteristics of water particles was investigated by the water-leaving radiation. The results showed that in the characteristic bands of 570, 675, and 705 nm, the degree of linear polarization (DOLP) was sensitive to the water-leaving radiation of the particles rather than to the reflectance. With the variation of observation angle, the reflection spectra were strongly interfered with by solar flare when the sensor zenith angle was close to 50° on the meridian plane with an azimuth angle of 180°, but DOLP was less affected, while also having a low correlation in the high concentration region. Combined with the coupling characteristics of particles at 675 and 705 nm, the model of DOLP ratio was established by partial least squares regression (PLSR) with a determination coefficient (R2) of 0.91, root mean square error (RMSE) 0.035, and a verification accuracy of 0.959. This shows that the model has better prediction ability for the coupling characteristics of water particles by the polarization reflection spectra and provides good support for mixed spectral unmixing of class II water.