一种基于IBWO-CNN-LSTM的多模式聚变煤识别方法
Wenchao Hao1, Haiyan Jiang1, Qinghui Song1
1College of Intelligent Equipment, Shandong University of Science and Technology, Taian, Shandong, 271000, China.
Scientific reports
|December 5, 2024
概括
这项研究引入了一种改进的白优化 (IBWO) 算法,与CNN-LSTM模型相结合,用于准确的煤识别. 这种先进的方法实现了95.238%的准确率,提高了采矿安全和效率.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 准确识别煤炭和矿对于高效和安全的采矿作业至关重要.
- 目前的方法可能缺乏复杂的采矿环境所需的精度.
研究的目的:
- 开发一种使用多模式聚变模型的新型煤识别方法.
- 为了提高采矿中煤炭带识别的准确性和稳定性.
主要方法:
- 一个改进的白优化 (IBWO) 算法与突变和内存库机制一起开发.
- 卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络模型用于特征提取和分类.
- 从音频和振动信号中提取了Mel-Frequency Cepstral Coefficients (MFCC),并将多头注意力机制集成到CNN-LSTM模型中.
主要成果:
- 与其他优化算法相比,IBWO算法在基准测试中表现出优异的性能.
- 拟议的IBWO-CNN-LSTM模型在煤识别方面实现了95.238%的高准确率.
- 多模式融合战略显著提高了识别系统的准确性和稳定性.
结论:
- 开发的IBWO-CNN-LSTM模型提供了一种有效的解决方案,用于自动识别煤.
- 集成先进的AI算法和信号处理技术可以提高采矿安全性和效率.
- 多模式方法为在具有挑战性的采矿条件下实时识别提供了强大的框架.
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