• Asia pacific journal of information systems
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• v.16 no.2
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.230-230
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• 2016

Hydrological modeling is a very complex task dealing with multi-source of data, but it can be potentially benefited from recent improvements and developments in remote sensing. The estimation of actual land surface evapotranspiration (ET), an important variable in water management, has become possible based entirely on satellite data. This study adopted a Surface Energy Balance Algorithm for Land (SEBAL) with the use of MODerate Resolution Imaging Spectrometer (MODIS) satellite products. The SEBAL model is one of the commonly used approach for the ET estimation. A primary advantage of the SEBAL model is rather its minimum requirement for ground-based weather data. The MODIS provides ET (MOD16) product that is based on the Penman-Monteith equation. This study aims to further develop the SEBAL model by employing a more rigorous parameterization scheme including the estimation of uncertainty associated with parameter and model selection in regression model. Finally, the proposed model is compared with the existing approaches and comprehensive discussion is then provided.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.9
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• pp.59-70
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• 2020

Three-dimensional (3D) design data is used for various purposes throughout the life cycle of a plant construction project. Plant 3D CAD systems support 3D modeling based on specs-catalogs, which contain data that are used for different purposes such as design, procurement, production, and handover. Therefore, it is important to share the spec-catalog data in the 3D design model with other application systems. Sharing this data thus requires a system that extracts spec-catalog data from plant 3D CAD systems and converts them into neutral model data. In this paper, we analyze equipment spec-catalog data of plant 3D CAD systems and, based on these analyses, define the data structure for neutral spec-catalog data. We subsequently propose a procedure that translates native spec-catalog data to neutral model data and develop a prototype system that performs this operation. The proposed method is then experimentally validated for the test spec-catalog data.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Atmosphere
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• v.29 no.4
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• pp.417-427
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• 2019

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s^-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.407-414
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• 2009

The foundation fieldbus(FF) is one of the fieldbuses most widely used for process control and automation, In order for system designer to optimize medium management, it is imperative to predict transmission delay time of data. In a former research, mathematical modeling to analyze transmission delay of FF token-passing system has been developed based on the assumption that a device node has only one priority data(1Q model), From 1Q model, all of the device nodes, which are connected on the FF system, are defined priority level in advance, and as system operates, data are generated based on given priority level. However, in practice, some non-periodic data can have different priority levels from one device. Therefore, new mathematical model is necessary for the case where different priority levels of data are created under one device node(2Q model). In this research, the mathematical model for 2Q model is developed using the equivalent queue model. Furthermore, the characteristics of transmission delay of 2Q model which is presented in this paper were compared with 1Q model. The validity of the analytical model was verified by using a simulation experiment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.165-176
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• 2018

To solve the inconsistencies between realistic boundaries and the cadastral record boundaries, the cadastral resurvey project has been funded by a large budget since 2012 and executed over a long period of time until 2030. However, if the causes of inconsistencies are not analyzed and addressed, these inconsistencies could possibly reoccur. Even though the causes of inconsistencies can be defined in several aspects, including regulations, surveying methods, and management of cadastre maps or survey results, and so on, this study focuses on analyzing the inconsistency problems in the management of cadastre maps or survey results. In order to resolve the problems in inconsistencies between the cadastre maps and survey results, the study proposes to develop the cadastre data model based on LADM (Land Administration Domain Model) to manage the cadastre maps and survey results in better ways. In order to proposed the Cadastre Data Model, we analyzed the cadastre management system implemented in Korea and identified requirements to resolve the problems in inconsistencies, which are considered in the proposed data model as follows: 1) cadastral management system based on individual parcels, 2) synthesis of a realistic boundary and cadastral record boundary, 3) management of official and sharing reference data, 4) consistent management of survey results and parcel boundaries, 5) temporal managements of parcel boundaries. In the end, this study proposes a cadastral data model based on the LADM to integrate and manage the cadastral surveying results of the new cadastral management system.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.575-584
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• 2004

We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.