• Journal of The Korean Association For Science Education
• /
• v.33 no.2
• /
• pp.522-537
• /
• 2013

This study analyzed the relationship among the construct level of analogical reasoning, prediction and uncertainty, and the construction of an explanatory model that were produced during small group discussions for scientific problem solving. This study was participated in by 8 students of K University divided into 2 teams conducting scientific problem solving. The participants took part in discussions in groups after achieving scientific problem solving individually. Through individual interviews afterwards, changes in their thinking through discussion activities were looked into. The results are as follows: The analogy at the Entities/Attributes level was used to make people clearly understand the characteristics of certain objects or entities in the discussions. The analogy at the Configuration/Motion level that was produced during the discussions ensured other participants to predict the results of problem solving. The analogy at the Mechanism/Causation level changed the structure of problem situations either to help other participants to reconstruct the explanatory model or to come up with a new situation that was never been through before to justify the created mechanism and through this, the case of creating Thought Experiments during the discussions were observed. if looking into the changes of analogies, each individual's analogic paradigm during the discussions were shown as production paradigm, reception-production paradigm, production-reception paradigm, and reception paradigm. The construction and reconstruction of the explanatory model were shown in analogic production paradigm, and in the reception paradigm of an analogy, participants changed their predictions or their certainty.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2002.03a
• /
• pp.197-203
• /
• 2002

The estimation of pile bearing capacity is important since the design details are determined from the result. There are numerous ways of determining the pile design load, but only few of them are chosen in the actual design. According to the recent investigation in Korea, the formulas proposed by Meyerhof based on the SPT N values are most frequently chosen in the design stage. In the study, various static and dynamic formulas have been used in predicting the allowable bearing capacity of a pile. Further, the reliability of these formulas has been verified by comparing the perdicted values with the static and dynamic load test measurements. Also in cases, these methods of pile bearing capacity determination do not take the time effect consideration, the actual allowable load as determined from pile load test indicates severe deviation from the design value. The principle results of this study are summarized as follows : A a result of estimate the reliability in criterion of the Davisson method, in was showed that Terzaghi & Peck > Chin > Meyerhof > Modified Meyerhof method was the most reliable method for the prediction of bearing capacity. Comparisons of the various pile-driving formulas showed that Modified Engineering News was the most reliable method. However, a significant error happened between dynamic bearing capacity equation was judged that uncertainty of hammer efficiency, characteristics of variable , time effect etc... was not considered. As a result of considering time effect increased skin friction capacity higher than end bearing capacity. It was found out that it would be possible to increase the skin friction capacity 1.99 times higher than a driving. As a result of considering 7 day's time effect, it was obtained that Engineering News. Modified Engineering News. Hiley, Danish, Gates, CAPWAP(CAse Pile Wave Analysis Program ) analysis for relation, respectively, $Q_{u(Restrike)}$ $Q_{u(EOID)}$ = 0.971 $t_{0.1}$, 0.968 $t_{0.1}$, 1.192 $t_{0.1}$, 0.88 $t_{0.1}$, 0.889 $t_{0.1}$, 0.966 $t_{0.1}$, 0.889 $t_{0.1}$, 0.966 $t_{0.1}$

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.25 no.4
• /
• pp.355-362
• /
• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• Economic and Environmental Geology
• /
• v.50 no.3
• /
• pp.239-250
• /
• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.2
• /
• pp.79-91
• /
• 2020

Accurate assessment of greenhouse gas emissions is a cornerstone of every climate change response study, and reliable assessment of greenhouse gas emission data is being used as a practical basis for the entire climate change prediction and modeling studies. Essential, fundamental technologies for estimating greenhouse gas emissions include an on-site monitoring technology, an evaluation methodology of uncertainty in emission factors, and a verification technology for reductions. The closed chamber method is being commonly used to measure gas fluxes between soil-vegetation and atmosphere. This method has the advantages of being simple, easily available and economical. This study presented the technical bases of the closed chamber method for measuring methane fluxes from a rice paddy. The methane fluxes from rice paddies occupy the largest portion of a single source of greenhouse gas in the agricultural field. We reviewed the international and the domestic studies on automated chamber monitoring systems that have been developed from manually operated chambers. Based on this review, we discussed scientific concerns on chamber methods with a particular focus on quality control for improving measurement reliability of field data.

• Journal of Science Education
• /
• v.45 no.2
• /
• pp.156-171
• /
• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Journal of the Korean earth science society
• /
• v.39 no.6
• /
• pp.533-544
• /
• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1101-1117
• /
• 2018

The clear sky radiance (CSR) is one of the baseline products of the Himawari-8 which was launched on October, 2014. The CSR contributes to numerical weather prediction (NWP) accuracy through the data assimilation; especially water vapor channel CSR has good impact on the forecast in high level atmosphere. The focus of this study is the quality analysis of the CSR of the Himawari-8 geostationary satellite. We used the operational CSR (or clear sky brightness temperature) products in JMA (Japan Meteorological Agency) as observation data; for a background field, we employed the CSR simulated using the Radiative Transfer for TOVS (RTTOV) with the atmospheric state from the global model of KMA (Korea Meteorological Administration). We investigated data characteristics and analyzed observation minus background statistics of each channel with respect to regional and seasonal variability. Overall results for the analysis period showed that the water vapor channels (6.2, 6.9, and $7.3{\mu}m$) had a positive mean bias where as the window channels(10.4, 11.2, and $12.4{\mu}m$) had a negative mean bias. The magnitude of biases and Uncertainty result varied with the regional and the seasonal conditions, thus these should be taken into account when using CSR data. This study is helpful for the pre-processing of Himawari-8/Advanced Himawari Imager (AHI) CSR data assimilation. Furthermore, this study also can contribute to preparing for the utilization of products from the Geo-Kompsat-2A (GK-2A), which will be launched in 2018 by the National Meteorological Satellite Center (NMSC) of KMA.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.2
• /
• pp.93-101
• /
• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of Korea Water Resources Association
• /
• v.54 no.6
• /
• pp.365-379
• /
• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.