• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.1-10
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• 2009

The safe and reliable performance of fusion and fission plants depends on the choice of suitable materials and an assessment of long-term materials degradation. These materials are degraded by their exposure to extreme conditions; it is necessary, therefore, to address the issue of long-term damage evolution of materials under service exposure in advanced plants. The empirical approach to the study of structural materials and fuels is reaching its limit when used to define and extrapolate new materials, new environments, or new operating conditions due to a lack of knowledge of the basic principles and mechanisms present. Materials designed for future Gen IV systems require significant innovation for the new environments that the materials will be exposed to. Thus, it is a challenge to understand the materials more precisely and to go far beyond the current empirical design methodology. Breakthrough technology is being achieved with the incorporation in design codes of a fundamental understanding of the properties of materials. This paper discusses the multi-scale, multi-code computations and multi-dimensional modelling undertaken to understand the mechanical properties of these materials. Such an approach is envisaged to probe beyond currently possible approaches to become a predictive tool in estimating the mechanical properties and lifetimes of materials.

• Earthquakes and Structures
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• v.14 no.2
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• pp.129-142
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• 2018

The current paper introduces a new approach for development of damage index to obtain the maximum damage in the reinforced concrete frames caused by as-recorded single and consecutive earthquakes. To do so, two sets of strong ground motions are selected based on maximum and approximately maximum peak ground acceleration (PGA) from "PEER" and "USGS" centers. Consecutive earthquakes in the first and second groups, not only occurred in similar directions and same stations, but also their real time gaps between successive shocks are less than 10 minutes and 10 days, respectively. In the following, a suite of six concrete moment resisting frames, including 3, 5, 7, 10, 12 and 15 stories, are designed in OpenSees software and analyzed for more than 850 times under two groups of as-recorded strong ground motion records with/without seismic sequences phenomena. The idealized multilayer artificial neural networks, with the least value of Mean Square Error (MSE) and maximum value of regression (R) between outputs and targets were then employed to generate the empirical charts and several correction equations for design utilization. To investigate the effectiveness of the proposed damage index, calibration of the new approach to existing real data (the result of Park-Ang damage index 1985), were conducted. The obtained results show good precision of the developed ANNs-based model in predicting the maximum damage of regular reinforced concrete frames.

• Korean Management Science Review
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• v.24 no.2
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• pp.157-176
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• 2007

Fuzzy cognitive map(FCM) has long been used as an effective way of constructing the human's decision making process explicitly. By taking advantage of this feature, FCM has been extensively used in providing what-if solutions to a wide variety of business decision making problems. In contrast, the goal-seeking analysis mechanism by using the FCM is rarely observed in literature, which remains a research void in the fields of FCM. In this sense, this study proposes a new type of the FCM-based goal-seeking analysis which is based on utilizing the genetic algorithm. Its main recipe lies in the fact that the what-if analysis as well as goal-seeking analysis are enabled very effectively by incorporating the genetic algorithm into the FCM-driven inference process. To prove the empirical validity of the proposed approach, valid questionnaires were gathered from a number of experts on software sales, and analyzed statistically. Results showed that the proposed approach is robust and significant.

• The Journal of Information Systems
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• v.28 no.2
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• pp.73-92
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• 2019

Purpose This study conducted an empirical study on the influence of institutional mechanisms on the formation of customer trust among leading online businesses. This study focused on the construct of PEEIM(Perceived Effectiveness of e-Commerce Institutional Mechanisms) which is the perceived recognition of institutional mechanisms for e-Commerce in general and the construct of PEIS which is the perceived recognition of institutional mechanisms that are implemented by vendors. Design/methodology/approach The online and offline surveys were conducted for the leading online shopping vendors in Korea and 292 data were used for the empirical analysis. The research model was tested using partial least squares structural equation modeling (PLS-SEM) in this study. The full measurement model including the formative second-order constructs was examined with the exploratory factor analysis. The structural model was analyzed via a two-stage approach. To analyze the research model this study used Smart PLS 2.0 program. Findings The findings showed that PEEIM negatively moderates the relationship between satisfaction in vender and trust in vender, but had no moderating effect between trust in vender and repurchase intention. In addition, the institutional mechanisms of vendors(PEIS) have been shown to have a direct impact on the vender's trust.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.241-254
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• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• The Journal of Industrial Distribution & Business
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• v.11 no.3
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• pp.39-50
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• 2020

Purpose: Agriculture, which is heavily influenced by climate conditions, is one of the industries most affected by climate change. In this respect, various studies on the impact of climate change on the agricultural market have been conducted. Since climate change is a long-term phenomenon for more than a decade, long-term projections of agricultural prices as well as climate variables are needed to properly analyze the impact of climate change on the agricultural market. However, these long-term price projections are often major constraints on studies of climate changes. The purpose of this study is to analyze the impacts of climate changes on the Korean onion market using ex-post analysis approach in order to avoid the difficulties of long-term price projections. Research design, data and methodology: This study develops an annual dynamic partial equilibrium model of Korean onion market. The behavioral equations of the model were estimated by OLS based on the annual data from 1988 to 2018. The modelling system is first simulated to have actual onion market conditions from 2014 to 2018 as a baseline and then compared it to the scenario assuming the climatic conditions under RCP8.5 over the same period. Scenario analyses were simulated by both comparative static and dynamic approach to evaluate the differences between the two approaches. Results: According to the empirical results, if the climate conditions under RCP8.5 were applied from 2014 to 2018, the yield of onion would increase by about 4%, and the price of onion would decrease from 3.7% to 17.4%. In addition, the average price fluctuation rate over the five years under RCP8.5 climate conditions is 56%, which is more volatile than 46% under actual climate conditions. Empirical results also show that the price decreases have been alleviated in dynamic model compared with comparative static model. Conclusions: Empirical results show that climate change is expected to increase onion yields and reduce onion prices. Therefore, the appropriate countermeasures against climate change in Korean onion market should be found in the stabilization of supply and demand for price stabilization rather than technical aspects such as the development of new varieties to increase productivity.

• Journal of Ginseng Research
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• v.40 no.1
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• pp.90-96
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• 2016

Background: Knowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. Methods: Weather data were measured under the shade and outside the shade at two fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012 seasons to assess temperature and humidity conditions under the shade. An empirical approach was developed and validated for the estimation of leaf wetness duration (LWD) using weather measurements outside the shade as inputs to the model. Results: Air temperature and relative humidity were similar between under the shade and outside the shade. For example, temperature conditions favorable for ginseng growth, e.g., between $8^{\circ}C$ and $27^{\circ}C$, occurred slightly less frequently in hours during night times under the shade (91%) than outside (92%). Humidity conditions favorable for development of a foliar disease, e.g., relative humidity > 70%, occurred slightly more frequently under the shade (84%) than outside (82%). Effectiveness of correction schemes to an empirical LWD model differed by rainfall conditions for the estimation of LWD under the shade using weather measurements outside the shade as inputs to the model. During dew eligible days, a correction scheme to an empirical LWD model was slightly effective (10%) in reducing estimation errors under the shade. However, another correction approach during rainfall eligible days reduced errors of LWD estimation by 17%. Conclusion: Weather measurements outside the shade and LWD estimates derived from these measurements would be useful as inputs for decision support systems to predict ginseng growth and disease development.

• Journal of the Korean Society for Railway
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• v.18 no.2
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• pp.166-174
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• 2015

A direct demand model requires relatively little analysis time and incurs a low cost. It is also known to be useful for the preliminary screening of promising configurations or concepts. This study reviews direct demand models of 12 existing urban railways using demographic data based on a block group level which is approximately 1/24 of a traditional zone area. However, direct demand models are limited. Therefore, a new approach is suggested. The proposed method is based on a field study and an empirical analysis. The study finds factors that affect ridership at the station level. As a case study, the proposed approach is tested using 54 light railway transit stations. The results of this empirical study demonstrate its applicability to improve the error rates of the predicted ridership at the station level.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.300-308
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• 2009

This article analyzed the crisis and emergency management research trend in the field of public administration in Korea from 1987 to 2007. The research purpose of this paper is to empirically evaluate the state of art in the crisis and emergency management research trend and explore research topics and methodology for future studies. According to empirical analysis, the main research results are as follows. First, almost all research papers over 80% have been written after 1998 and, especially, 67.6% of all papers have been published from 2003-2007. Second, recently lots of scholars are more interested in the problem solving prescriptive topics than normative and theory orientation. Third, in the Korean public administration, 74.6% of research papers which dealt with crisis, have investigated the disaster crisis including natural and man-made disasters, among conventional security crisis, disaster crisis, critical infrastructure crisis, and living safety crisis. Finally, so far, crisis and emergency management research trend in Korean public administration have consisted mainly of the papers with more descriptive approach and literature survey than empirical approach and survey research.