Application of AI in Energy and Mining Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 28 October 2024 | Viewed by 619

Special Issue Editors


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Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
Interests: control theory; network control; AI+
Special Issues, Collections and Topics in MDPI journals
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
Interests: unconventional oil and gas geology; AI+ energy technology; sedimentary petrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Presently, global energy and resources research is driven by many new technologies. Combinations of artificial intelligence and other disciplines in the field of energy and resources research have greatly promoted its progress. In this context, it is of great significance to take an up-to-date survey of the new advances in AI-based energy and mining research.

Areas relevant to advances in AI-based energy and resource research include, but are not limited to, new understandings of intelligent mining, mathematical geology, big data for energy and resources, and AI-based applied technologies.

This Special Issue will publish high-quality, original research papers from the following fields: intelligent mining; AI-based geological surveys; new applications of AI and technologies; intelligent equipment; macro research on energy and resource systems; energy security; energy- and resource-related simulation studies

Prof. Dr. Xiaoping Ma
Dr. Difei Zhao
Guest Editors

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Keywords

  • AI
  • energy and resources
  • AI-based geology
  • intelligent mining
  • AI+ applied sciences
  • intelligent building

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Published Papers (1 paper)

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Research

16 pages, 3091 KiB  
Article
Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
by Qinxia Wang, Dandan Liu, Hao Tian, Yongpeng Qin and Difei Zhao
Appl. Sci. 2024, 14(13), 5589; https://doi.org/10.3390/app14135589 - 27 Jun 2024
Viewed by 434
Abstract
For vibration signals, this paper proposes an ensemble classification method based on stochastic configuration networks (SCNs). Firstly, the time–frequency analysis methods are used to obtain the frequency spectrum signal and time–frequency images. The sample data in the frequency domain and the time–frequency domain [...] Read more.
For vibration signals, this paper proposes an ensemble classification method based on stochastic configuration networks (SCNs). Firstly, the time–frequency analysis methods are used to obtain the frequency spectrum signal and time–frequency images. The sample data in the frequency domain and the time–frequency domain can characterize fault information from different perspectives. The hybrid data that consist of the sample data from the two domains are used to build a SCN model. Moreover, a SCNs ensemble method is proposed to solve the fault classification problem, and the sub-classifiers are built to extract fault features from different training data. In the experiment, the bearing and gear fault datasets are used for performance comparison. The experimental results show that the proposed SCNs ensemble model obtains good classification results, and compared with the deep learning methods, the SCN modeling process is more simple and effective for industrial data classification. Full article
(This article belongs to the Special Issue Application of AI in Energy and Mining Research)
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