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Number of results: 3
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Abstract

The mean-reversion model is introduced into the study of mineral product price prediction. The gold price data from January 2018 to December 2021 are selected, and a mean-reverting stochastic process simulation of the gold price was carried out using Monte Carlo simulation (MCS) method. By comparing the statistical results and trend curves of the mean-reversion (MR) model, geometric Brownian motion (GBM) model, time series model and actual price, it is proved that the mean-reversion process is valid in describing the price fluctuation of mineral product. At the same time, by comparing with the traditional prediction methods, the mean-reversion model can quantitatively assess the uncertainty of the predicted price through a set of equal probability stochastic simulation results, so as to provide data support and decision-making basis for the risk analysis of future economy.
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Authors and Affiliations

Shuwei Huang
1 2 3
ORCID: ORCID
Zhaoyang Ma
1
Feng Jin
1
ORCID: ORCID
Yuansheng Zhang
1

  1. BGRIMM Technology Group, China
  2. Beijing Key Laboratory of Nonferrous Intelligent Mining Technology, China
  3. BGRIMM Intelligent Technology Co. Ltd, China
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Abstract

Based on the theory of computer vision, a new method for extracting ore from underground mines is proposed. This is based on a combination of RGB images collected by a color industrial camera and a point cloud generated by a 3D ToF camera. Firstly, the mean-shift algorithm combined with the embedded confidence edge detection algorithm is used to segment the RGB ore image into different regions. Secondly, the effective ore regions are classified into large pieces of ore and ore piles consisting of a number of small pieces of ore. The method applied in the classification process is to embed the confidence into the edge detection algorithm which calculates edge distribution around ore regions. Finally, the RGB camera and the 3D ToF camera are calibrated and the camera matrix transformation of the two cameras is obtained. Point cloud fragments are then extracted according to the cross-calibration result. The geometric properties of the ore point cloud are then analysed in the subsequent procedure.
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Authors and Affiliations

Feng Jin
1
ORCID: ORCID
Kai Zhan
2
Shengjie Chen
2
Shuwei Huang
2
ORCID: ORCID
Yuansheng Zhang
2

  1. BGRIMM Technology Group University of Science and Technology Beijing, China
  2. BGRIMM Technology Group, China
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Abstract

In the execution of edge detection algorithms and clustering algorithms to segment image containing ore and soil, ore images with very similar textural features cannot be segmented effectively when the two algorithms are used alone. This paper proposes a novel image segmentation method based on the fusion of a confidence edge detection algorithm and a mean shift algorithm, which integrates image color, texture and spatial features. On the basis of the initial segmentation results obtained by the mean shift segmentation algorithm, the edge information of the image is extracted by using the edge detection algorithm based on the confidence degree, and the edge detection results are applied to the initial segmentation region results to optimize and merge the ore or pile belonging to the same region. The experimental results show that this method can successfully overcome the shortcomings of the respective algorithm and has a better segmentation results for the ore, which effectively solves the problem of over segmentation.
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Authors and Affiliations

Feng Jin
1 2
ORCID: ORCID
Kai Zhan
1
Shengjie Chen
1
Shuwei Huang
1
ORCID: ORCID
Yuansheng Zhang
1

  1. BGRIMM Technology Group, China
  2. University of Science and Technology Beijing, China

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