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首页> 外文期刊>International Journal of Computer Science and Technology >Sensor Occupancy Detection Using XG Boost Algorithm
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Sensor Occupancy Detection Using XG Boost Algorithm

机译：使用XG Boost算法的传感器占用率检测

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A room in a smart home is fixed with environmental sensors for sensing of the indoor air quality. Environmental sensors can be any sensor from simple air temperature sensor to an indoor air quality measurement system, which holds different types of sensors or a networked sensor. Purpose of these sensors is to determine the indoor air quality and their potential in incorporating occupancy detection is largely unused. Occupancy detection is a technique used to detect the presence of living and non-living things. There are many environmental sensors which are used to detect different kinds of gases, namely CO2 (carbon dioxide) and TVOC (total volatile organic compounds) sensors which are used here to detect the gases that resides in a room. By detecting the indoor gases we can improve the quality of the air. CO2 sensor is used for detection of carbon dioxide composition, where as TVOC is internally built with CO2 sensor and it will detect other gases too. There are many machine learning algorithms that are used to classify the occupancy detection. In previous studies, naive Bayes classifier is used for detecting occupants using Weka tool. Now In this paper XGBoost, a machine learning algorithm is used for detecting occupants.
机译：智能家居中的房间固定有环境传感器，用于感应室内空气质量。环境传感器可以是任何传感器，从简单的空气温度传感器到室内空气质量测量系统，都可以容纳不同类型的传感器或联网传感器。这些传感器的目的是确定室内空气质量，并且它们在结合占用检测方面的潜力尚未得到充分利用。占用检测是一种用于检测生物和非生物存在的技术。有许多环境传感器用于检测不同种类的气体，即CO2（二氧化碳）和TVOC（总挥发性有机化合物）传感器，在这里用于检测驻留在房间中的气体。通过检测室内气体，我们可以改善空气质量。 CO2传感器用于检测二氧化碳成分，因为TVOC内置有CO2传感器，它也可以检测其他气体。有许多机器学习算法可用于对占用检测进行分类。在以前的研究中，朴素的贝叶斯分类器用于使用Weka工具检测乘员。现在在本文XGBoost中，使用机器学习算法来检测乘员。

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