• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.1-15
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• 2017

Eutrophication of surface waters is of concern worldwide, because it can result in many undesirable water-quality and ecological problems, such as hypoxic 'dead' zones and harmful algal blooms, both associated with considerable economic costs. In this study, we used LSM (Land Surface Model) to simulate nitrogen in five major rivers in the Southern Korean Peninsula. The main objective of this research was to enhance nitrogen data for input of LM3V model in South Korea. Input data for nitrogen fluxes were categorized into three sections including agriculture fertilizer, livestock manure, atmosphere deposition, biological fixation, and sewage pollutants were used as the nitrogen input. For using LM3V model, the nitrogen input data were regenerated by considering states of agriculture and industry in South Korea at a $1/8^{\circ}$ resolution. Then, we simulated stream/river flows and N loads throughout the entire drainage networks in South Korea at a $1/8^{\circ}$ resolution. By using the same parameters for the entire country ($100,210km^2$), composed of 5 river basins with varying climate and land use, the model simulates spatial (11 sites) and temporal (1999~2010) patterns of flows and nitrate-N loads are resonable by comparing observed flow and nitrate-N loads. The r (Pearson's linear correlation) for water temperature, flow and nitrate-N at river were 080~0.93, 0.62~0.92 and 0.5~0.9 respectively. Based on enhanced N input data and model results, we find that LM3V model as land surface model can be applied in South Korea with interaction of atmosphere and land conditions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.366-372
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• 2009

Creep life prediction has been commonly used by a time-temperature parameter (TTP) which is correlated to an applied stress and temperature, such as Larson-Miller (LM), Orr-Sherby-Dorn (OSD), Manson-Haferd (MH) and Manson-Succop (MS) parameters. A stress-temperature linear model (STLM) based on Arrhenius, Dorn and Monkman-Grant equations was newly proposed through a mathematical procedure. For this model, the logarithm time to rupture was linearly dependent on both an applied stress and temperature. The model parameters were properly determined by using a technique of maximum likelihood estimation of a statistical method, and this model was applied to the creep data of Alloy 617. From the results, it is found that the STLM results showed better agreement than the Eno’s model and the LM parameter ones. Especially, the STLM revealed a good estimation in predicting the long-term creep life of Alloy 617.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.2
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• pp.55-67
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• 2022

Land use is a critical factor that affects the hydrological characteristics of watersheds, thereby determining the biological condition of streams. This study analyzes the effects of land uses in the watersheds on biological indicators of streams across the Han River basin using a linear model (LM) and generalized additive model (GAM). LULC and biological monitoring data of streams were obtained from the Korean Ministry of Environment. The proportions of urban, agricultural, and forest areas in the watersheds were regressed to the three biological indicators, including diatom, benthic macroinvertebrate, and fish of streams. The estimated LM and GAM models for the biological indicators were then compared, using regression determination R² and AIC values. The results revealed that GAM models performed better than the LM models in explaining the variances of biological indicators of streams, indicating the non-linear relationships between biological indicators and land uses in watersheds. Also, the results suggested that the indicator of macroinvertebrates was the most sensitive indicator to land uses in watersheds. Although non-linear relationships between watershed land uses and biological indicators of streams could vary among biological indicators, it was consistent that streams' biological integrity significantly deteriorated by a relatively low percentage of urban areas. Meanwhile, biological indicators of streams were negatively affected by the relatively high percentage of agricultural areas. The results of this study can be integrated into effective quantitative criteria for the watershed management and land use plans to enhance the biological integrity of streams. In specific, land uses management plans in watersheds may need more close attention to urban land use changes than agricultural land uses to sustain the biological integrity of streams.

• Analytical Science and Technology
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• v.32 no.4
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• pp.131-138
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• 2019

This paper reports results on the physical properties of a powder type of $Al_2O_3:C$ commonly used as a luminescence dosimeter using the LM-OSL technique. On the analysis with the general order kinetics model, the LM-OSL signal measured appeared to be composed of three components (fast, medium, slow) showing the largest area in the medium component. The photoionization cross sections of three components were distributed between $10^{-19}{\sim}10^{-21}cm^2$. The values of the thermal assistance energy were evaluated the largest in slow component and the smallest in fast component, which indicates the electrons trapped in defects attributed to slow component should be the most sensitive to thermal vibration among three components. In illumination to blue light, the fast component showed a rapid linear decay and completely disappeared after light exposure time of about 5 s. The medium component decayed with two exponential elements but the slow component did not observed any noticeable change until light exposure time of 40 s. In a dose response study, all components exhibited a linear behaviour up to approximately 10 Gy.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.411-433
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• 2004

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.207-216
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• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.

• Communications of Mathematical Education
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• v.31 no.4
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• pp.367-387
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• 2017

The educational environment in the digital age of the 21st century definitely affects teaching and learning methods to be changed. In addition, the perceptions and methods of mathematics education in the digital age have also been changing. This study proposes a university mathematics education model suitable for the digital age, which makes full use of the internet/digital environment and leads the students to participate in the learning processes. We apply the proposed model to Linear Algebra course, and present a concrete method of teaching and learning model including evaluation. This will be the first study on how to organize and operate digital courses in Korea in accordance with the mathematics education in the digital era which is rapidly spreading around the world.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.121.2-121
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• 2001

Many of today´s robot are required to perform tasks which demand a high level of accuracy in end-effector positioning. Those rigid robots are very inefficient and slow because its have large and heavy links, In an attempt to solve these problems, a robots using flexible beam were created. But the single-link flexible beam is infinite-dimensional system. Many researchers have proposed controlling such a beam an approximated model consisting of a finite a number of models. In this paper, we start by deriving the analytic model for the dynamics of general single-link beam, and a controller is designed for flexible beam with integral type servo system bases of the linear matrix inequality (LM) technique. To the end, simulation results show that a designed controller guarantees affective vibration control the single-link flexible beam.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.