In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real- world scenarios.

Quality design methodologies have received constituent attention from a number of researchers and practitioners for more than twenty years. Specially, the quality design for drug products must be carefully considered because of the hazards involved in the

A number of problems associated with environmental noises in urban areas have significantly been considered. Specific measurement and estimation of the environmental noise became a primary issue in local governments. Environmental noise monitoring system is required in order to estimate and verify the a city noise map. However, current monitoring positions may not perfectly represent and incorporate many different view points, such as districts of a city, different utilizations of a city by the law, populations, and classifications and traffics of roads. In addition, scientific method to provide specific noise monitoring positions my not be avaliable in current literature. For this reason, the primary objective of this paper is to propose a new method for introducing a number of monitoring positions in the entire city. First, the quality function deployment (QFD) method was utilized to simultaneously represent both districts and utilizations of a city. Second, a new algorithm to find a number of monitoring positions was proposed by compromising many different view points: populations, classifications of roads and areas, and traffics of roads. Finally, the proposed monitoring positions and a sample noise map was provided for verification purposes. Based on these results, the proposed algorithm including the QFD concept may successfully provide specific noise monitoring positions by simultaneously consider may different view points and requirements of a city.