• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.319-332
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• 2004

Due to their high strength to weight ratios and excellent durability, fiber reinforced polymer(FRP) is widely used in construction industries. In this paper, a shape optimum design of FRP bridge decks haying pultruded cellular cross-section is presented. In the problem formulation, an objective function is selected to minimize the volumes. The cross-sectional dimensions and material properties of the deck of FRP bridges are used as the design variables. On the other hand, deflection limits in the design code, material failure criteria, buckling load, minimum height, and stress are selected as the design constraints to enhance the structural performance of FRP decks. In order to efficiently treat the optimization process, the cross-sectional shape of bridge decks is assumed to be a tube shape. The optimization process utilizes an improved Genetic Algorithms incorporating indexing technique. For the structural analysis using a three-dimensional finite element, a commercial package(ABAQUS) is used. Using a computer program coded for this study, an example problem is solved and the results are presented with sensitivity analysis. The bridge consists of a deck width of 12.14m and is supported by five 40m long steel girders spaced at 2.5m. The bridge is designed to carry a standard DB-24 truck loading according to the Standard Specifications for Highway Bridges in Korea. Based on the optimum design, viable cross-sectional dimensions for FRP decks, suitable for pultrusion process are proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.