使用香农和雷尼来检测微磨机中的聊的特征的提取和评估
Zehui Zheng1, Xiubing Jing1, Bowen Song1
1Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin 300072, China.
Micromachines
|March 6, 2025
概括
在微加工中检测聊天对于质量和可持续性至关重要. 这项研究发现,与概率相关的特征,特别是来自香农的特征,对于识别各种严重程度的聊天最有效.
科学领域:
- 制造业 工程 制造工程
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
背景情况:
- 微加工中的喋喋不休会降低产品质量,缩短工具寿命,并造成噪音污染.
- 早期检测聊对于可持续的制造实践和流程优化至关重要.
- 现有的聊天检测方法可能缺乏对微妙或变化的聊天条件所需的灵敏度.
研究的目的:
- 调查与概率相关的和与相关的特征在微研磨中用于聊天检测的有效性.
- 为了比较Shannon和Rényi算法的性能在提取敏感的聊天特征.
- 通过评估变化速率,值和计算时间来评估拟议功能的实际适用性.
主要方法:
- 提取四个聊天特征,分类为与概率相关的和与相关的,使用香农和雷尼算法.
- 在稳定,弱噪声和严重噪声条件下进行实际微型磨试验,以收集加工数据.
- 通过分析特征变化速率,定义值间隔和测量计算时间,对提取的特征进行系统评估.
主要成果:
- 拟议的聊天功能可以有效地检测到不同严重程度 (稳定,弱,严重) 的聊天事件.
- 与相关的特征相比,与概率相关的特征在检测聊天时表现出更高的灵敏度.
- 来自Shannon热算法的特征显示出比Rényi热算法的特征更高的灵敏度.
结论:
- 开发的特征提取方法为微型削中实时聊天检测提供了强大的方法.
- 对于增强的聊天监控系统,建议使用与概率相关的特征,特别是来自Shannon的特征.
- 这项研究有助于提高加工质量,延长工具寿命,减少微加工对环境的影响.
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