Application of Deep Learning Algorithms in Determination of Trace Rare Earth Elements of Cerium Group in Rocks and Minerals

Sumin Ma,Wenhui Huang,Wenqing Wu

doi:10.1155/2021/9945141

Abstract

Since the breakthrough of deep learning in object classification in 2012, extraordinary achievements have been made in the field of target detection, but the high time and space complexity of the target detection network based on deep learning has hindered the technology from application in actual product. To solve this problem, first of all, this paper uses the MobileNet classification network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the MobileNet classification network is not suitable for optimizing the Faster R-CNN network. After that, this paper proposes a classification network that combines VGG16 and MobileNet, and uses the fusion network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the Faster R-CNN target detection network optimized by the fusion classification network has the advantages of using VGG16 and MobileNet’s Faster R-CNN target detection network to detect rare earth elements. The innovation of this article is that the results on 5 time series data sets show that CDA-WR has better predictive performance than other ELM variant models. The effect of determining trace cerium elements in rocks and minerals is increased by more than 50%, based on deep learning. The algorithm studies the methods of target detection and recognition and integrates it into the intelligent robot used in this subject, giving the robot the ability to accurately detect the target object in real time.

Highlights

Adding trace amounts of cerium to steel for offshore platforms is expected to replace the more expensive titanium and vanadium elements and reduce costs
This article focuses on deep learning algorithms and points out that most deep learning algorithms use the same feature map to predict the two positions of prediction and category prediction
In-depth target-based target research shows that the location prediction and category prediction in the target task put forward different requirements for the feature map function

Summary

Introduction

Adding trace amounts of cerium to steel for offshore platforms is expected to replace the more expensive titanium and vanadium elements and reduce costs. This is in line with my country’s resource characteristics (the properties and functions of cerium in the smelting, processing, and alloying of steel are relatively similar), cerium reserves are abundant, and the price is relatively low. To study the effect of trace rare earth element cerium (ppm level) on the structure and properties of steel, the steel used for offshore platforms has higher requirements for impact toughness in addition to the conventional mechanical performance test. Due to the reduced potential difference and the reduction of the galvanic effect, the presence of rare earth sulfide in the grain boundary is beneficial to improve the steel corrosion resistance of the substrate. The corrosion resistance of the high-strength steel used in seawater solution was analyzed [5]

Deep Learning Algorithms Are Measuring

Experimental Analysis of Trace Rare Earth Elements in the Cerium Group

Findings

Conclusions