Department of Information and Communication Engineering, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung 413310, Taiwan.
Department of Computer Science and Information Engineering, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung 413310, Taiwan.
Sensors (Basel). 2020 Apr 24;20(8):2423. doi: 10.3390/s20082423.
Fine aerosols with a diameter of less than 2.5 microns (PM) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan's government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.
细颗粒物（PM）的直径小于 2.5 微米，对人体健康有重大负面影响。然而，其测量设备或仪器通常昂贵且操作复杂，因此需要一种简单有效的方法来测量 PM 浓度。为了解决这个问题，本文尝试提供一种简单的 PM 浓度估计替代方法。所提出的方法基于图像处理方案和简单的线性回归模型。它使用高 PM 浓度和低 PM 浓度的图像来获取这些图像之间的差异。该差异用于找到受影响最大的区域。该方法分两个阶段描述。首先，采用一系列图像处理方案自动选择 PM 浓度估计的感兴趣区域（ROI）。通过所选 ROI，获得单个特征。其次，通过使用单个特征，采用简单的线性回归模型并应用于 PM 浓度估计。通过台湾政府发布的真实世界公开数据验证了所提出的方法。所提出的方案预计不会取代使用物理或化学技术的成分分析。我们试图提供一种更便宜、更容易的方法，以更高效地进行 PM 估计，同时具有可接受的性能。为此，将进行进一步的工作，并在本文结尾处进行总结。
