Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Abstract

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

Impact of Ultraviolet Radiation on Human Skin Owing to Ozone Depletion

“…It measures the correlation of UVI and illuminance on natural light. The model works under mobile network and forecast the level of UVI in particular location in any condition (Oh ,2021).…”

A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images

“…In addition, the UVI is closely related to the altitude and azimuth angles of the sun, and the intensity of hourly UV radiation reaching the Earth's surface varies by month [35]. The information about the altitude and azimuth angles of the sun and month was selected as additional input elements.…”

“…In order to improve the performance of the proposed model, hyperparameter tuning was performed to optimize the number of hidden layers and the number of nodes. The hyperparameter tuning was performed using Grid Search functions of the sklearn library, which compares the performance of several deep learning models that combine properties within a predefined range [35]. For optimization, the number of nodes in the layer was set to 16, 32, 64, and 128, and the number of hidden layers was set to a range of 2 to 7.…”

Design and metrological studies of a reference UV- index detector

“…Therefore, public interest in ultraviolet danger increased, and the manufacturing of UVI radiometers increased. To inform the public about these hazards the World Meteorological Organization (WMO), the International Commission on Non-Ionizing Radiation Protection (ICNIPR), the World Health Organization (WHO), and the United Nations Environmental Programme (UNEP) all recommend using the UVI as long as they are exposed to Sunlight in their practical lives [1,6]. The most benefit of using UVI is determining the amount of UV radiation that reaches the Earth's surface.…”