• Journal of Climate Change Research
• /
• v.9 no.1
• /
• pp.13-29
• /
• 2018

This study presented evaluation procedure for selecting appropriate GCMs and downscaling method by focusing on the climate extreme indices suitable for climate change adaptation. The procedure includes six stages of processes as follows: 1) exclusion of unsuitable GCM through raw GCM analysis before bias correction; 2) calculation of the climate extreme indices and selection of downscaling method by evaluating reproducibility for the past and distortion rate for the future period; 3) selection of downscaling method based on evaluation of reproducibility of spatial correlation among weather stations; and 4) MME calculation using weight factors and evaluation of uncertainty range depending on number of GCMs. The presented procedure was applied to 60 weather stations where there are observed data for the past 30 year period on Korea Peninsula. First, 22 GCMs were selected through the evaluation of the spatio-temporal reproducibility of 29 GCMs. Between Simple Quantile Mapping (SQM) and Spatial Disaggregation Quantile Delta Mapping (SDQDM) methods, SQM was selected based on the reproducibility of 27 climate extreme indices for the past and reproducibility evaluation of spatial correlation in precipitation and temperature. Total precipitation (prcptot) and annual 1-day maximum precipitation (rx1day), which is respectively related to water supply and floods, were selected and MME-based future projections were estimated for near-future (2010-2039), the mid-future (2040-2069), and the far-future (2070-2099) based on the weight factors by GCM. The prcptot and rx1day increased as time goes farther from the near-future to the far-future and RCP 8.5 showed a higher rate of increase in both indices compared to RCP 4.5 scenario. It was also found that use of 20 GCM out of 22 explains 80% of the overall variation in all combinations of RCP scenarios and future periods. The result of this study is an example of an application in Korea Peninsula and APCC Integrated Modeling Solution (AIMS) can be utilized in various areas and fields if users want to apply the proposed procedure directly to a target area.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.238-238
• /
• 2015

There are demands for water environmental analysis of discharge processes in paddy fields, however, it is not fully understood in nutrients discharge process for watershed modeling. As hydrological processes both surface and ground water and agricultural water managements are so complex in paddy fields, the development of lowland paddy fields watershed model is more difficult than upland watershed model. In this research, the improvement of SWAT (Soil and Water Assessment Tool) model for a paddy watershed was conducted. First, modification of surface inundated process was developed in improved pot hole option. Those modification was evaluated by monitoring data. Second, the monitoring data in river and drainage channel in lowland paddy fields from 2012 to 2014 were analyzed to understand discharge characteristics. As a case study, Imbanuma basin, Japan, was chosen as typical land and water use in Asian countries. In this basin, lowland paddy fields are irrigated from river water using small pumps that were located in distribution within the watershed. Daily hydrological fluctuation was too complex to estimate. Then, to understand surface and ground water discharge characteristics in irrigation (Apr-Aug) and non-irrigation (Sep-Mar) period, the water and material balance analysis was conducted. The analysis was composed two parts, watershed and river channel blocks. As results of model simulation, output was satisfactory in NSE, but uncertainty was large. It would be coming from discharge process in return water. The river water and ground water in paddy fields were exchanged each other in 5.7% and 10.8% to river discharge in irrigation and non-irrigation periods, respectively. Through this exchange, nutrient loads were exchanged between river and paddy fields components. It suggested that discharge from paddy fields was not only responded to rainfall but dynamically related with river water table. In general, hydrological models is assumed that a discharge process is one way from watershed to river. However, in lowland paddy fields, discharge process is dynamically changed. This function of paddy fields showed that flood was mitigated and temporally held as storage in ground water. Then, it showed that water quality was changed in mitigated function in the water exchange process in lowland paddy fields. In future, it was expected that hydrological models for lowland paddy fields would be developed with this mitigation function.

• Journal of Korea Water Resources Association
• /
• v.52 no.10
• /
• pp.681-695
• /
• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.1-20
• /
• 2021

As the Great Reset is discussed at the World Economic Forum due to the COVID-19 pandemic, artificial intelligence, the driving force of the 4th industrial revolution, is also in the spotlight. However, corporate research in the field of artificial intelligence is still scarce. Since 2000, related research has focused on how to create value by applying artificial intelligence to existing companies, and research on how startups seize opportunities and enter among existing businesses to create new value can hardly be found. Therefore, this study analyzed the cases of startups using the comprehensive framework of the multi-level perspective with the research question of how artificial intelligence based startups, a sub-industry of software, have different innovation patterns from the existing software industry. The target firms are gazelle firms that have been certified as venture firms in South Korea, as start-ups within 7 years of age, specializing in machine learning modeling purposively sampled in the medical, finance, marketing/advertising, e-commerce, and manufacturing fields. As a result of the analysis, existing software companies have achieved process innovation from an enterprise-wide integration perspective, in contrast machine learning technology based startups identified unit processes that were difficult to automate or create value by dismantling existing processes, and automate and optimize those processes based on data. The contribution of this study is to analyse the birth of artificial intelligence-based startups and their innovation patterns while validating the framework of an integrated multi-level perspective. In addition, since innovation is driven based on data, the ability to respond to data-related regulations is emphasized even for start-ups, and the government needs to eliminate the uncertainty in related systems to create a predictable and flexible business environment.

• Journal of Korean Society of Disaster and Security
• /
• v.13 no.4
• /
• pp.25-36
• /
• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.77-86
• /
• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Journal of Korea Water Resources Association
• /
• v.55 no.12
• /
• pp.1115-1124
• /
• 2022

The impact of climate change on water resources was evaluated for Chungju Dam and Soyang-gang Dam basins by constructing an integrated modeling framework consisting of a dam inflow prediction model based on the Variable Infiltration Capacity (VIC) model, a distributed hydrologic model, and an LSTM based dam outflow prediction model. Considering the uncertainty of future climate data, four models of CMIP6 GCM were used as input data of VIC model for future period (2021-2100). As a result of applying future climate data, the average inflow for period increased as the future progressed, and the inflow in the far future (2070-2100) increased by up to 22% compared to that of the observation period (1986-2020). The minimum value of dam discharge lasting 4~50 days was significantly lower than the observed value. This indicates that droughts may occur over a longer period than observed in the past, meaning that citizens of Seoul metropolitan areas may experience severe water shortages due to future droughts. In addition, compared to the near and middle futures, the change in water storage has occurred rapidly in the far future, suggesting that the difficulties of water resource management may increase.

• Asia Marketing Journal
• /
• v.8 no.1
• /
• pp.105-132
• /
• 2006

The major purpose of this study is to investigate the impact of environmental uncertainties and logistics resources capabilities mediated by relational norms and logistics services on logistics performance in the industrial products. The 272 data were collected from the key informants who were working at the logistics-related departments in the H Heavy Industries & Construction and HSD Engine. The following results were verified using structural equation modeling. First, environmental uncertainties such as dynamism and heterogeneity unexpectedly had insignificant effects on relational norms such as information exchange and flexibility and logistics services such as product availability and on-time delivery. Second, logistics resource capabilities showed unique effects based upon its component's characteristics. For example, Logistics Information Systems did not have direct impact on logistics services but had indirect effect on logistics services via relational norms. On the other hand, logistics resources such as logistics specific assets and transportation service competencies had direct impact on logistics services but not on relational norms. Third, relational norms between transaction partners significantly affected logistics services but had insignificant effects on logistics performance such as logistics costs reduction and delivery qualities. Fourth, consistent with several studies, excellent logistics services between industrial purchaser and suppliers based upon relational norms did have significant effect on logistics performance such as delivery consistency and delivery qualities. Finally, the empirical results in this study could be strategic logistics management guidelines based upon the theoretical relationships among the environmental uncertainties, logistics information systems, logistics resources, relational norms, logistics services, and logistics performance.

• Journal of Venture Innovation
• /
• v.7 no.1
• /
• pp.201-223
• /
• 2024

This study undertook an empirical analysis to examine the impact of various factors on entrepreneurial intention among young people, with a particular focus on the role of startup mentoring. Employing a survey distributed nationwide, data from 250 valid respondents were subjected to structural equation modeling to investigate these dynamics. The analysis uncovered that workplace stress, subjective norms, and perceived behavioral control positively influence the entrepreneurial intentions of youth. Meanwhile, technological constraints negatively affected these intentions. The study did not explore the potential effects of future uncertainty and the burden of failure. Significantly, it was found that startup mentoring plays a crucial role in mitigating the negative impacts that may deter young individuals from pursuing entrepreneurship. Mentoring was instrumental in reducing negative influences, thereby fostering a more conducive environment for entrepreneurial ambition. By integrating the Push-Pull-Mooring (PPM) and Theory of Planned Behavior (TPB) models, this research not only validates these frameworks within the context of youth entrepreneurship but also underscores the essential function of startup mentoring in enhancing entrepreneurial intentions. The insights from this study highlight the importance of mentoring programs in nurturing the entrepreneurial spirit among the youth, suggesting that targeted mentoring support can play a pivotal role in overcoming barriers to entrepreneurship.

• Journal of Radiation Protection and Research
• /
• v.40 no.4
• /
• pp.252-260
• /
• 2015

This paper describes the technical background for the Korean wildlife radiation dose assessment code, K-BIOTA, and the summary of its application. The K-BIOTA applies the graded approaches of 3 levels including the screening assessment (Level 1 & 2), and the detailed assessment based on the site specific data (Level 3). The screening level assessment is a preliminary step to determine whether the detailed assessment is needed, and calculates the dose rate for the grouped organisms, rather than an individual biota. In the Level 1 assessment, the risk quotient (RQ) is calculated by comparing the actual media concentration with the environmental media concentration limit (EMCL) derived from a bench-mark screening reference dose rate. If RQ for the Level 1 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 2 assessment, which calculates RQ using the average value of the concentration ratio (CR) and equilibrium distribution coefficient (Kd) for the grouped organisms, is carried out for the more realistic assessment. Thus, the Level 2 assessment is less conservative than the Level 1 assessment. If RQ for the Level 2 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 3 assessment is performed for the detailed assessment. In the Level 3 assessment, the radiation dose for the representative organism of a site is calculated by using the site specific data of occupancy factor, CR and Kd. In addition, the K-BIOTA allows the uncertainty analysis of the dose rate on CR, Kd and environmental medium concentration among input parameters optionally in the Level 3 assessment. The four probability density functions of normal, lognormal, uniform and exponential distribution can be applied.The applicability of the code was tested through the participation of IAEA EMRAS II (Environmental Modeling for Radiation Safety) for the comparison study of environmental models comparison, and as the result, it was proved that the K-BIOTA would be very useful to assess the radiation risk of the wildlife living in the various contaminated environment.