• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.428-428
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• 2017

In this study, three optimization techniques efficiency is assessed for calibration of the GR4J model for streamflow simulation in Selmacheon, Boryeong Dam and Kyeongancheon watersheds located in South Korea. The Penman-Monteith equation is applied to estimate the potential evapotranspiration, model calibration, and validation is carried out using the readily available daily hydro-meteorological data. The Shuffled Complex Evolution-University of Arizona(SCE-UA), Uniform Adaptive Monte Carlo (UAMC), and Coupled Latin Hypercube and Rosenbrock (CLHR) optimization techniques has been used to evaluate the robustness, performance and optimized parameters of the three catchments. The result of the three algorithms performances and optimized parameters are within the recommended ranges in the tested watersheds. The SCE-UA and CLHR outputs are found to be similar both in efficiency and model parameters. However, the UAMC algorithms performances differently in the three tested watersheds.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.67-86
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• 2018

This paper attempted to analyze impacts of transition in electricity generation system on a national economy and environmental level in Korea using a recursive computable general equilibrium(CGE) model. In particular, the paper presented a hybrid model combining the top-down CGE model with the bottom-up model which describes the structure of electricity production in detail. The impacts were analyzed by two policy scenarios base on the basic plan for electricity supply and demand proposed by the Korean government. As a result, the paper specifically showed that there exists a trade-off relationship in the policy-making between economic efficiency and environmental level. The paper also suggested that the transition in electricity generation system should be done more gradually and carefully.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.35-42
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• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• Journal of Korean Association for Spatial Structures
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• v.22 no.1
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• pp.51-57
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• 2022

In this paper, based on the finite element analysis model verified in previous studies, a new model of a buckling restrained brace reinforced with a steel plate was proposed. A design formula was proposed for the new model to dissipate energy without buckling the steel core under load protocol, and the performance of the model satisfying the design formula was evaluated by comparing it with the previous model through the results of hysteresis loop, bi-linear curve, cumulative energy dissipation capacity, and equivalent viscous damping.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.75-82
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• 2022

Automated vehicles (AVs) are coming to our roadways. In practice, there are still several challenges that can impede the AV sensors are polluted on various road conditions. In this paper, we propose a strategy for V2E performance assurance technology using Kano model. We are developing the vehicle sensor cleaning system about the three types of commonly used sensors: camera, radar, and LiDAR. Surveys were carried out in 30 AV's experts on quality characteristics about V2E performance assurance technology. As a result, the Kano model developed to verify a major requirement of autonomous vehicle's sensor cleaning system. It is expected that the Kano model will be actively used to verify the importance of V2E development strategy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.413-413
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• 2023

Wetlands are a critical component of the global carbon cycle and are essential in mitigating climate change. Accurately quantifying wetland carbon emissions is crucial for understanding and predicting the impact of wetlands on the global carbon budget. The uncertainty quantifying carbon in wetlands may comes from the ecosystem's hydrological, biochemical, and microbiological variability. The Community Land Model is a sophisticated and flexible land surface model that offers several configuration options such as energy and water fluxes, vegetation dynamics, and biogeochemical cycling, necessitating careful consideration for the alternative configurations before model implementation to develop a practical model framework. We conducted a systematic literature review, analyzing the alternatives, focusing on the carbon stock pools configurations and the parameters with significant sensitivity for carbon quantification in wetlands. In addition, we evaluated the feasibility and availability of in situ observation data necessary for validating the different alternatives. This analysis identified the most suitable option for our study site, the Binbong Wetland, in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• Journal of The Korean Association For Science Education
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• v.27 no.2
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• pp.120-125
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• 2007

The purpose of the present study was to test the validity of the confirming abduction model (CAM). CAM is a process model which explains how reasoners confirm their hypothetical explicans. To test this model, 154 8th grade students were sampled from one middle school in Korea. Three types of vapor condensation hypothesis confirming tests were developed and administered to the subjects. The results of this study revealed that student confidence increased when hypothetical explicans were borrowed into experienced phenomena from questioning phenomena. These results validated CAM. According to CAM, the process. of confirming hypothetical explican is as follows: representing a questioning phenomenon, representing an experienced phenomenon that is similar to the questioning phenomenon, representing the hypothetical explican of the questioning phenomenon, comparing the questioning phenomenon with the experienced phenomenon, and borrowing the hypothetical explican as the hypothetical explican of the experienced phenomenon from the hypothetical explican of the questioning phenomenon. This study also discussed the implications of these findings for teaching and learning in science education.

• Journal of The Korean Association For Science Education
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• v.28 no.7
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• pp.759-767
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• 2008

This article arose from the previous studies, which suggested a synthetic list for the nature of science (NOS), discussed the relationship between the NOS and scientific inquiry and the development of the NOS in the context of scientific inquiry. In this article, for teaching scientific inquiry through the NOS, I proposed three teaching models - reflection, interaction, and the direct model -. Within these teaching models, understanding the NOS is viewed as a prerequisite condition for the improved performance of scientific inquiry. In the reflection model, the NOS is embedded and reflected in scientific inquiry without explicit introduction or direct explanation of the NOS. In the interaction model, concrete interaction between scientific inquiry and the NOS is encouraged during the process of scientific inquiry. In the direct model, subsequent to directly comprehending the NOS at the first stage of activity, students conduct scientific inquiry based on their understanding of the NOS. The intention of this present article is to facilitate the use of these models to develop teaching materials for more authentic scientific inquiry.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.