在移动人群采购/传感平台上监测环境噪音水平的连续聚类阶段
1Department of Computer & Networking Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
移动人群源/传感 (MCS) 噪音监测可能不准确. 本研究提出了一种两相聚类方法,通过消除空间和声学异常值来提高噪声水平的准确性,从而提高数据可靠性.
科学领域:
- 环境科学 环境科学
- 声学 声学 在声学方面
- 计算机科学 计算机科学
背景情况:
- 移动人群采集/传感 (MCS) 用于噪音监控.
- 目前用于MCS噪声监测的招聘方法可能会导致错误的声音水平报告.
- 现有的方法,如全人口选择,随机选择和基于参数的聚类,显示结果的差异.
研究的目的:
- 在MCS.中识别噪音水平报告不相似的原因.
- 提出一个改进的双相聚类方法,用于在MCS平台中准确监测噪声水平.
- 提高通过MCS.收集的噪音水平数据的可靠性.
主要方法:
- 为噪音水平监测提出了一种两阶段的集群方法.
- 第一个阶段:空间聚类以形成聚焦的集群,并消除空间异常值.
- 第二阶段:噪音水平聚类以消除声学异常值.
- 通过对25部手机进行现实实验来验证该方法.
- 进行统计 t 测试以评估选择方法,并将拟议的方法与现有的噪音水平集群进行比较.
主要成果:
- 统计学t测试证实了不同选择方法之间的差异.
- 拟议的双相聚类方法有效地消除了空间和噪声水平的异常值.
- 与单独的噪音水平集群相比,新方法在异常结果的检测和消除方面表现高出4%至12%.
结论:
- 移动电话的声学特征和异常值显著影响到MCS中的噪音水平报告.
- 拟议的双相聚类方法提高了使用MCS监控噪声的准确性和可靠性.
- 这种方法为环境噪声评估提供了更强大的解决方案.
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