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.

Automobiles are largely divided into passenger cars with less than five passengers and commercial vehicles such as construction machines, trucks, mixers, special lorries, and large buses. Automobile maintenance companies are also divided into passenger cars and commercial vehicles. Because commercial vehicles are the livelihood vehicles of individual carriers engaged in cargo transportation, passenger transportation, and construction, fast and accurate maintenance is important. Access to the expressway is also important because the car is large and uses highways. In addition, the time required for troubleshooting is long, so sufficient space must be secured for parking the vehicle, and ease of entry and exit of the vehicle in the maintenance shop should be fully considered. For this reason, commercial vehicle maintenance companies have higher initial investment costs than car maintenance companies, and it is difficult to supply and maintain maintenance personnel. Therefore, it is necessary to carefully examine and analyze various related factors for successful commercial vehicle maintenance company selection. However, most existing commercial vehicle maintenance companies often choose their location based on the empirical judgment of the founder, the customs and the case, without a clear analysis. In this study, we show how to derive the location selection factors to be considered when establishing professional maintenance company for commercial vehicles and to select the optimal location by using AHP (Analytic Hierarchy Process) method.