PURPOSES : We propose a framework to evaluate the reliability of integrating homogeneous or heterogeneous mobility data to produce the various data required for greenhouse gas emission estimation. METHODS : The mobility data used in the framework were collected at a fixed time from a specific point and were based on raster data. In general, the traffic volume for all traffic measurement points over 24 h can be considered raster data. In the future, the proposed framework can be applied to specific road points or road sections, depending on the presence or absence of raster data. RESULTS : The activity data required to calculate greenhouse gas emissions were derived from the mobility data analysis. With recent developments in information, communication, and artificial intelligence technologies, mobility data collected from different sources with the same collection purpose can be integrated to increase the reliability and accuracy of previously unknown or inaccurate information. CONCLUSIONS : This study will help assess the reliability of mobility data fusion as it is collected on the road, and will ultimately lead to more accurate estimates of greenhouse gas emissions.

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.

우리나라에서 2012년부터 공식 적용되는 새주소 정보가 위치표시에 있어 핵심정보임에도 불구하고 국가기본공간정보와 연계되지 못하고 있으며, 행정권역 및 각종 권역정보는 체계적으로 구축되지 않아 국가기본공간정보 활용의 한계 및 예산중복과 효율성의 문제점이 대두되고 있다. 이에 따라 국가공간정보인프라로서 새주소 자료의 전략적 연계 및 각종 권역 정보의 체계적 구축이 필요하다. 본 연구에서는 첫째, 도로를 중심으로 주소정보와 각종 권역정보를 연계하고 있는 해외의 도로분야 기본공간정보 구축 현황을 분석하고, 둘째, 국내의 기본지리정보, 새주소 정보, 각종 권역정보 DB구축 현황을 분석한 후, 셋째, 도로를 중심으로 주소정보와 권역정보를 연계하는 DB 구축 방안을 제안하였다.

In medium-sized enterprises that comprise of multiple business branches and companies, various types of information systems are constructed and operated. One of the difficult problems these enterprises face is that integrated information cannot be deliver

복수 법인 또는 사업부문으로 구성된 중견기업의 경우, 정보화 과정에서 다양한 정보시스템이 구축되어 운영되고 있다. 이와 같은 중견기업의 정보화 과정에서 겪게 되는 문제 중 하나는 느슨한 마스터데이터관리로 인해 통합 정보를 적시에 확보할 수 없다는 점이다 본 논문은 복수 법인 또는 사업부문에서 각각 독자적인 정보시스템을 구축하여 운영하고 있는 중견기업을 위한 효과적인 마스터데이터관리 프레임웍을 제시하고자 한다. 프레임웍은 강력한 중앙통제식 모델과 중앙조정식 모델로 설계되어 장단점이 비교 되었다. 마스터데이터 중 가장 대표적인 거래처 마스터데이터의 통합 프로젝트 사례 연구를 통해 프레임웍의 구체적인 적용 가능성을 탐색하였다.

In this study, the bridge management system framework for the application of UAV collection data was proposed. The framework of UAV Bridge Management System(UBMS) is designed based on algorithm of Bridge Management System(BMS), administered by MOLIT. The UAV Bridge Management System will help facility managers to conduct safety diagnosis by providing rapid procedure.