基于MLP的气体和煤灰爆炸的声音识别方法
1School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种使用多层感知器 (MLP) 的新声音识别方法,以准确检测矿山中的气体和煤尘爆炸. 先进的算法达到95%以上的准确性,增强实时安全监控.
科学领域:
- 矿山安全工程 矿山安全工程
- 声学信号处理 声学信号处理
- 人工智能的人工智能
背景情况:
- 矿山当前的气体和煤尘爆炸报警技术存在精度的限制,依赖于单一的监测方法.
- 需要改进的识别系统,以防止灾难性的矿山事故.
研究的目的:
- 开发和验证一种用于检测煤矿中气体和煤尘爆炸的新型声音识别方法.
- 提高实时安全监控和报警系统的准确性和可靠性.
主要方法:
- 分析各种声音特征,包括短时间能量,零穿越率,光谱特征,MFCC,GFCC和短时间里埃系数.
- 利用Relief算法进行最佳特征提取,并使用多层感知器 (MLP) 进行声音识别模型开发.
- 选择了顶部的35维特征值作为描述声音信号的最佳特征向量.
主要成果:
- 开发的基于MLP的声音识别模型实现了高计算效率,满足实时监控要求.
- 该算法在区分气体爆炸,煤尘爆炸和其他地下声音方面表现出卓越的准确性.
- 识别实验的平均识别率为95%,回忆率为95%,准确率为95.8%.
结论:
- 拟议的声音识别方法显著提高了煤矿中气体和煤尘爆炸检测的准确性.
- 该算法的高性能和效率使其适合实时安全监控和报警系统.
- 这种方法为提高矿山安全和防止爆炸提供了强大的解决方案.
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