Study of Methods for Element Data Service for Electronic Documents Related to National R&D Projects

Major countries are supporting new knowledge creation and innovative activities by opening data so that the research results carried out by government budgets can serve as public goods. The Republic of Korea has also made research and development(R&D) reports and papers available in electronic file format, which is the result of national R&D programs for the general public. However, the extraction of meaningful information among unstructured data in text format has not satisfied researchers’ expectations. In order to evolve into a customized service reflecting the opinions of researchers, we investigated the demand for necessary contents and services at the planning stage of R&D projects. This study attempts to propose a method to offer significant information which shows a bigger unit than objects based on trend information with extraction and processing demands focusing on R&D reports based on the results of questionnaire survey and interviews with researchers. This study aims to provide the integration service, tentatively named ‘element data service’, of key sentences and table/figure images with a high demand for the utilization of researchers. The main procedure of the proposed method consists of the subject classification of the R&D report, the extraction of table/figure image, and the extraction of main sentence. We used public reports of the same classification published from 2012 to 2016 for the experiment and utilized the extractive summarization method for the copyright protection of report original text. After realizing the simple prototype, we examined the service possibility through the researcher target reviews

A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors

Wave Run-up Modeling with Adaptive Mesh Refinement (AMR) Method in the Busan Marine City during Typhoon Chaba (1618)

The accuracy of numerical calculations is highly dependent on the grid spacing, and the smaller the grid spacing the better the accuracy. However, the portion requiring high accuracy often corresponds to only a part of the entire model domain. Setting a small grid interval for the entire model domain to study the calculation region is undesirable from the viewpoint of computation load and computation time. In this study, we illustrate the efficiency of the adaptive mesh refinement method for wave run-up modelling in terms of computational time and accuracy using numerical experiments. In this study, we conducted numerical experiments of wave run-up to verify the efficacy of the Adaptive Mesh Refinement (AMR) method in terms of accuracy and computation load for typhoon Chaba (1618). We determined that wave propagation modelling with storm surge height using the AMR method might be a good alternative to calculated wave run-up height. The difference between the observed and AMR model values for wave run-up height was calculated to be less than 1 m. Compared to the results of non-adaptive experiments with fixed spatial resolutions, those with the AMR method produced highly accurate results, while requiring only 75% of the computation time. The estimation of wave run-up height using AMR is expected to allow the accurate estimation of coastal inundation if more accurate topography and input conditions are given