Research on the Health Assessment Method of the Safety Retaining Wall in a Dump Based on UAV Point-Cloud Data.

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel.

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data.

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry.

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

Copper Content Inversion of Copper Ore Based on Reflectance Spectra and the VTELM Algorithm.

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately.

Precise Topographic Model Assisted Slope Displacement Retrieval from Small Baseline Subsets Results: Case Study over a High and Steep Mining Slope.

Due to the intrinsic side-looking geometry of synthetic aperture radar (SAR), time series interferometric SAR is only able to monitor displacements in line-of-sight (LOS) direction, which limits the accuracy of displacement measurement in landslide monitoring. This is because the LOS displacement is only a three dimensional projection of real displacement of a certain ground object. Targeting at this problem, a precise digital elevation model (DEM) assisted slope displacement retrieval method is proposed and applied to a case study over the high and steep slope of the Dagushan open pit mine. In the case study, the precise DEM generated by laser scanning is first used to minimize topographic residuals in small baseline subsets analysis. Then, the LOS displacements are converted to slope direction with assistance of the precise DEM. By comparing with ground measurements, relative root mean square errors (RMSE) of the estimated slope displacements reach approximately 12-13% for the ascending orbit, and 5.4-9.2% for the descending orbit in our study area. In order to validate the experimental results, comparison with microseism monitoring results is also conducted. Moreover, both results have found that the largest slope displacements occur on the slope part, with elevations varying from -138 m to -210 m, which corresponds to the landslide area. Moreover, there is a certain correlation with precipitation, as revealed by the displacement time series. The outcome of this article shows that rock mass structure, lithology, and precipitation are main factors affecting the stability of high and steep mining slopes.

[A Classification Method Based on the Combination of Visible, Near-Infrared and Thermal Infrared Spectrum for Coal and Gangue Distinguishment].

Coals and gangues are the main surface dump in the coal mining process. Dynamic monitoring of those dumps using remote sensing technique is of great importance for mine environmental protection. In the traditional classification of visible and near-infrared remote sensing, part of the gangues might be misclassified as coal, due to the phenomenon of "different objects with the same spectrum", resulting in the decrease of classification accuracy. Thus, this study firstly acquired visible and near-infrared spectrums of 12 coal samples and 115 gangue samples from Tiefa mining area in China. Most of the gangue samples' spectrums are different from those of the coals, which can be easily distinguished. While, part of the gangues has the similar spectrum with coal which results in misclassification. With an effort to improve image classification accuracy, furthermore, we acquired the thermal infrared spectrum of the misclassified gangue and the coal samples. The results indicate that there are different spectral characteristics in thermal infrared band between coal and gangue samples, which can be identified easily. Therefore, we proposed a method to separate coal from gangue based on the combination of visible, near-infrared and thermal infrared spectrum. In the first palace, the method conducts measurement on the visible and near-infrared spectrums of all samples for the rough classification recurring to the MAO model. Next, the thermal infrared spectrums of the samples, mixed with gangue and coal are acquired, and the Spectral Absorption Ratio(SAR) is utilized as the evaluation index for the second classification. The fused result of classification originates in the two steps above. The method is further verified by using external samples from Tiefa, Yanzhou, Shendong and Jiangcang mining areas in China, whose results have demonstrated that the method has higher accuracy than that of the traditional classification method based on visible and near-infrared spectrum features. The research results indicates that the conjoint analytical method involving multiple spectrums can solve the phenomenon of "different objects with the same spectrum" in a single band, effectively, which will be of great referential significance in the field of terrain classification based on remote sensing technique.

[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. It is simple, fast, accurate and efficient without destroying the sample. In this paper, we take magnesite 30 group from Yingkou City, Liaoning Province Dashiqiao for the study, collecting their magnesite NIR data samples at 30×973, using principal component analysis (PCA) for data dimensionality reduction process. The main element contribution rate is greater than 99.99% obtained characteristic variables of 10, established quantitative analysis ELM algorithm mathematical model, take 20 groups of samples as the training samples (including 6 super group, 14 groups non), 10 groups of samples for testing samples (including super grade4 groups, 6 groups non), ELM algorithm model hidden layer nodes selection 20. In order to further improve the classification performance, two kinds improved ELM algorithm models are proposed: conduct optimization selection ELM for the traditional ELM input weights and threshold using the circulation patterns and integrate integration-Featured ELM based on Featured ELM. And compare to which use the artificial method, chemical method and BP neural network model approach. The results showed that magnesite grade classification with the near-infrared spectroscopy and ELM model have a distinct advantage regardless of cost or time, and the accuracy rate can reach over 90%, which provides a new way to classify magnesite grade.

Coal and Gangue Classification Based on Laser-Induced Breakdown Spectroscopy and Deep Learning.

During the extraction and processing of coal, a large amount of solid waste, collectively known as gangue, is produced. This gangue has a low carbon content but a high ash content, accounting for approximately 15 to 20% of the total coal yield. Before coal is used, coal and gangue must be effectively separated to reduce the gangue content in the raw coal and improve the efficiency of coal utilization. This study introduces a classification method for coal and gangue based on a combination of laser-induced breakdown spectroscopy (LIBS) and deep learning. The method employs Gramian angular summation fields (GASF) to convert 1D spectral data into 2D time-series data, visualizing them as 2D images, before employing a novel deep learning model-GASF-CNN-for coal and gangue classification. GASF-CNN enhances model focus on critical features by introducing the SimAM attention mechanism, and additionally, the fusion of various levels of spectral features is achieved through the introduction of residual connectivity. GASF-CNN was trained and tested using a spectral data set containing coal and gangue. Comparative experimental results demonstrate that GASF-CNN outperforms other machine learning and deep learning models across four evaluation metrics. Specifically, it achieves 98.33, 97.06, 100, and 98.51% in the accuracy, recall, precision, and F1 score metrics, respectively, thereby achieving an accurate classification of coal and gangue.

Intercalation Pseudocapacitive Zn2+ Storage with Hydrated Vanadium Dioxide toward Ultrahigh Rate Performance.

The weak van der Waals interactions enable ion-intercalation-type hosts to be ideal pseudocapacitive materials for energy storage. Here, a methodology for the preparation of hydrated vanadium dioxide nanoribbon (HVO) with moderate transport pathways is proposed. Out of the ordinary, the intercalation pseudocapacitive reaction mechanism is discovered for HVO, which powers high-rate capacitive charge storage compared with the battery-type intercalation reaction. The main factor is that the defective crystalline structure provides suitable ambient spacing for rapidly accommodating and transporting cations. As a result, the HVO delivers a fast Zn2+ ion diffusion coefficient and a low Zn2+ diffusion barrier. The electrochemical results with intercalation pseudocapacitance demonstrate a high reversible capacity of 396 mAh g-1 at 0.05 A g-1 , and even maintain 88 mAh g-1 at a high current density of 50 A g-1 .

Building High Rate Capability and Ultrastable Dendrite-Free Organic Anode for Rechargeable Aqueous Zinc Batteries.

Aqueous zinc-ion batteries (ZIBs) are an alternative energy storage system for large-scale grid applications compared with lithium-ion batteries, when the low cost, safety, and durability are taken into consideration. However, the reliability of the battery systems always suffers from the serious challenge of the large Zn dendrite formation and "dead Zn," thus bringing out the inferior cycling stability, and even cell shorting. Herein, a dendrite-free organic anode, perylene-3,4,9,10-tetracarboxylic diimide (PTCDI) polymerized on the surface of reduced graphene oxide (PTCDI/rGO) utilized in ZIBs is reported. Moreover, the theoretical calculations prove the reason for the low redox potential. Due to the protons and zinc ions coparticipant phase transfer mechanism and the high charge transfer capability, the PTCDI/rGO electrode provides superior rate capability (121 mA h g-1 at 5000 mA g-1, retaining the 95% capacity of that compared with 50 mA g-1) and a long cycling life span (96% capacity retention after 1500 cycles at 3000 mA g-1). In addition, the proton coparticipation energy storage mechanism of active materials is elucidated by various ex-situ methods.

MXene-supported Co3O4 quantum dots for superior lithium storage and oxygen evolution activities.

A novel hybrid, composed of Co3O4 quantum dots supported on Ti3C2Tx (MXene) nanosheets, exhibits a strong synergetic effect, and shows superior lithium storage (capacity = 766.5 mA h g-1 at 2 A g-1 after 400 cycles) and oxygen evolution (overpotential = 340 mV at 10 mA cm-2) activities.