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基于CO2波动的存在检测的隐藏马尔科夫模型

Christos Karasoulas¹, Christoforos Keroglou¹, Eleftheria Katsiri¹

¹Department of Electrical Computer Engineering, Democritus University of Thrace, Xanthi, Greece.

Frontiers in robotics and AI

|November 1, 2023

概括

这项研究引入了一种使用二氧化碳 (CO2) 检测的新型存在感应系统. 二氧化碳监测方法准确地识别室内人员,为传统传感器提供可靠的替代方案.

科学领域:

环境监测环境监测环境监测
传感器技术传感器技术
人工智能的人工智能是人工智能.

背景情况:

传统的存在感应系统 (运动,摄像头) 有限制,例如视野受限.
在现有的传感器技术中,监控覆盖范围的差距可能会出现.
人的呼吸释放二氧化碳 (CO2),改变室内度.

研究的目的:

调查使用环境二氧化碳 (CO2) 水平用于室内存在检测.
开发和评估基于二氧化碳度分析的存在感应方法.
为传统传感器提供一种替代方案,这些传感器可能存在监测缺口.

主要方法:

使用简单的马尔科夫链模型来分析二氧化碳度数据.
从实验设置中收集和分析二氧化碳数据.
使用现实环境数据验证了该方法.

主要成果:

在检测室内存在时,达到高精度,高达97%.
证明了CO2传感器在存在检测中的有效性.
展示了系统在受控和实际环境中的有效性.

结论:

使用环境二氧化碳分析进行存在检测是一种可行且准确的方法.
拟议的马尔科夫链模型方法为室内监控提供了一个强大的解决方案.
这项技术在智能家居,辅助生活和监控等领域有着潜在的应用.

关键词:

马尔科夫链式算法马尔科夫链式算法二氧化碳监测监测二氧化碳的监测隐藏的马尔科夫模型运动传感器运动传感器存在检测检测器存在检测器

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