Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1772-1778

Experimental research on intelligent prospecting prediction based on multi-scale feature and meta-learning

Huang Yongjie
Gao Le
Yang Tian
Zhang Xin
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen Guangdong 529020, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0625

Abstract

At present, most prediction methods of prospecting prediction rely on manual sampling and experts' knowledge and experience. However, these methods face great challenges in the real world with small areas and less numbers of mines. In order to meet this challenge, this paper proposed a novel depth prospecting prediction framework: multi-scale feature interaction framework. Firstly, this paper defined two networks, the multi-scale feature mapping net and the multi-scale feature classification net. On this basis, it captured the features of different geochemical elements in the multi-scale feature mapping net by dilated convolution, and used the multi-scale feature classification net to process these features. Secondly, it used meta network to generate convolutional weights for multi-scale classification networks. Finally, it used self-distillation to exploit implicit knowledge in multi-scale classification networks for prediction. The whole model adopted end-to-end training mode. A large number of experimental results show that multi-scale feature interaction framework is significantly competitive with the most advanced methods.

Foundation Support

广东省教育厅教学改革项目五邑大学2021年研究生暑期学校项目(2022SQXX040)
五邑大学青年科研基金资助项目(2019td10)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0625
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1772-1778
Serial Number: 1001-3695(2022)06-029-1772-07

Publish History

[2022-01-24] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

黄勇杰, 高乐, 杨田, 等. 基于多尺度特征和元学习的智能预测找矿靶区实验研究 [J]. 计算机应用研究, 2022, 39 (6): 1772-1778. (Huang Yongjie, Gao Le, Yang Tian, et al. Experimental research on intelligent prospecting prediction based on multi-scale feature and meta-learning [J]. Application Research of Computers, 2022, 39 (6): 1772-1778. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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