• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.347-356
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• 2001

Even though the increase of greenhouse gases such as $CO_2$ is thought to be the main cause for global warming, its impact on global climate has not been revealed clearly in rather quantitative manners. However, researches using Genral Circulation Model(GCM) has shown that the accumulation of greenhouse gases increases the global mean temperature, which in turn impacts on the global water circulation pattern. A climate predictive capability is limited by lack of understanding of the different process governing the climate and hydrologic systems. The prediction of the complex responses of the fully coupled climate and hydrologic systems can be achieved only through development of models that adequately describe the relevant process at a wide range of spatial and temporal scales. These models must ultimately couple the atmospheres, oceans, and lad and will involve many submodels that properly represent the individual processes at work within the coupled components of systems. So far, there are no climate and related hydrologic models except local rainfall-runoff models in Korea. The purpose of this research is to predict the change of temperature in Korean Peninsula using regional scale model(IRSHAM96 model) and GCM data obtained from the increasing scenarios of $CO_2$ Korean Peninsula increased by $2.5^{\circ}C$ and the duration of Winter in $lxCO_2$ condition would be shorter the $2xCo_2$ condition due to global warming.

• Smart Structures and Systems
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• v.26 no.1
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• pp.89-102
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• 2020

Shape memory alloy (SMA)-based Superelasticity-assisted Slider (SSS) is proposed as an engineering solution to practically exploit the well-accepted advantages of both sliding isolation and SMA-based recentering. Self-centering capability in SSS is provided by austenitic SMA cables (or wire ropes), recently attracting a lot of interest and attention in earthquake engineering and seismic isolation. The cables are arranged in various novel and conventional configurations to make SSS versatile for aseismic design and retrofit of structures. All the configurations are detailed with thorough technical drawings. It is shown that SSS is applicable without the need for Isolation Units (IUs). IUs, at the same time, are devised for industrialized applications. The proof-of-concept study is carried out through the examination of mechanical behavior in all the alternative configurations. Force-displacement relations are determined. Isolation capabilities are predicted based on the decreases in seismic demands, estimated by the increases in effective periods and equivalent damping ratios. Restoring forces normalized relative to resisting forces are assessed as the criteria for self-centering capabilities. Lengths of SMA cables required in each configuration are calculated to assess the cost and practicality. Practical implementation is realized by setting up a small-scale IU. The effectiveness of SSS under seismic actions is evaluated using an innovative computer model and compared to those of well-known Isolation Systems (ISs) protecting a reference building. Comparisons show that SSS seems to be an effective IS and suitable for earthquake protection of both structural and non-structural elements. Further research aimed at additional validation of the system are outlined.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.30-38
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• 2015

IT security service provides customers with the capability of protecting the networked information asset and infrastructures, and the scope of security service is expanding from a technology-intensive task to a comprehensive protection system for IT environment. To improve the quality of this service, a research model which help assess the quality is required. Several research models have been proposed and used in various service areas, but few cases are found for IT security service. In this work, a research model for the IT security quality has been proposed, based on research models such as SERVQUAL and E-S-QUAL. With the proposed model, factors which affect the service quality and the best quality measure have been identified. And the feasibility of using quantitative measures for quality has been examined. For analysis, structural equation modeling and various statistical methods such as principal component analysis were used. The result shows that satisfaction is the most significant measure affected by the proposed quality factors. Two quality factors, fulfillment and empathy, are the main determinants of the service quality. This leads to a strategy of quality improvement based on factors of emotion and perception, not of technology. The quantitative measures are considered as promising alternative measures, when combined with other measures. In order to design reliable quantitative measures, more work should be done on target processing time and users' expectation. It is hoped that work of this research will provide efficient tools and methods to improve the quality of IT security service and help future research works for other IT service areas.

• Ocean Systems Engineering
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• v.10 no.3
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• pp.243-266
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• 2020

In-place analysis for offshore platforms is required to make proper design for new structures and true assessment for existing structures. In addition, ensure the structural integrity of platforms components under the maximum and minimum operating loads and environmental conditions. In-place analysis was carried out to verify the robustness and capability of structural members with all appurtenances to support the applied loads in either operating condition or storm conditions. A nonlinear finite element analysis is adopted for the platform structure above the seabed and the pile-soil interaction to estimate the in-place behavior of a typical fixed offshore platform. The SACS software is utilized to calculate the natural frequencies of the model and to obtain the response of platform joints according to in-place analysis then the stresses at selected members, as well as their nodal displacements. The directions of environmental loads and water depth variations have an important effect on the results of the in-place analysis behavior. The influence of the soil-structure interaction on the response of the jacket foundation predicts is necessary to estimate the loads of the offshore platform well and real simulation of offshore foundation for the in-place analysis. The result of the study shows that the in-place response investigation is quite crucial for safe design and operation of offshore platform against the variation of environmental loads.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Nuclear Engineering and Technology
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• v.37 no.6
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• pp.587-594
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• 2005

In an effort to develop a thermal-hydraulic (TH) safety analysis code for Gas-cooled Reactors (GCRs), the MARS code, which was primarily developed for TH analysis of water reactor systems, has been extended here for application to GCRs. The modeling requirements of the system code were derived from a review of major processes and phenomena that are expected to occur during normal and accident conditions of GCRs. Models fur code improvement were then identified through a review of existing MARS code capability. Among these, the following priority models necessary fur the analysis of limiting high and low pressure conduction cooling events were evaluated and incorporated in MARS-GCR/V1 : 1) Helium (He) and Carbon Dioxide ($CO_2$) as main system fluids, 2) gas convection heat transfer, 3) radiation heat transfer, and 4) contact heat transfer models. Each model has been assessed using various conceptual problems for code-to-code benchmarks and it was demonstrated that MARS-GCR/V1 is capable of capturing the relevant phenomena. This paper describes the models implemented in MARS-GCR/V1 and their verification and validation results.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.137-144
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• 2019

Testing maturity is critical to the system under development with lack of experience and skills in the acquisition of the weapon systems. Defects have a huge impact on important system operations. Sharing real-time information will lead to rapid command and mission capability in submarine. DDS(Data Distribution System) is a very important information sharing system and interface between various manufacturers or data formats. In this paper, we analyze data distribution characteristics of distributed data system to group data-specific systems and proposes a test case-generation method using path search of postorder and preorder which is a tree traversal in path testing method. The proposed method reduces 73.7.% testing resource compare to existing methods.

• Information Systems Review
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• v.21 no.4
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• pp.31-45
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• 2019

Despite the expansion of the market of performing arts and culture, small and medium size theaters are still experiencing difficulties due to poor accessibility of information by consumers. This study proposes a machine learning based genre recommendation system as an alternative to enhance the marketing capability of small and medium sized theaters. We developed five recommendation systems that recommend three genres per customer using customer master DB and transaction history DB of domestic venues. We propose an optimal recommendation system by comparing performances of recommendation system. As a result, the recommendation system based on the ensemble model showed better performance than the single predictive model. This study applied the personalized recommendation technique which was scarce in the field of performing arts and culture, and suggests that it is worthy enough to use it in the field of performing arts and culture.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.431-440
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• 2012

Purpose: In this paper we propose modified Notz designs which are useful to experimenters who want to adopt the sequential experimentation strategy and to estimate second-order regression model with as few experimental points as possible. Methods: We first present the design matrices and design points in two or three dimensional spaces for such small sized second-order designs as small composite, hybrid, and Notz designs. Modified Notz designs are proposed and compared with some response surface designs in terms of the total number of experimental points, the estimation capability criteria such as D- and A-optimality. Results: When sequential experimentation is necessary, the modified Notz designs are recommendable. Conclusion: The result of this paper will be beneficial to experimenters who need to do experiments more efficiently, especially for those who want to reduce the time of experimentation as much as possible to develop cutting-edge products.