• Land and Housing Review
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• v.4 no.3
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• pp.271-277
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• 2013

The BNWF (Beam on Nonlinear Winkler Foundation) model is one of the simplest idealizations for a pile embedded in soil as it ignores the continuity of the soil. This method is difficult to model the behavior of pile group foundation subjected to lateral loading. The limitation can be overcome with the utilization of the finite element method (FEM) or finite different method (FDM) to represent a pile element embedded in a soil medium. Both the ground and piles are modeled with soild elements. The solid elements, which do not have rotational degree of freedom, is not appropriate for modeling piles. It can be overcome by substantially increasing the number of elements, which can be prohibitive for 3D modeling. This paper used the beam element and rigid link incorporated in the OpenSees to model the pile. The accuracy of the model is validated through comparison with lateral load test and BNWF analysis. It is shown that the method can capture the measured behavior accurately. It is therefore recommended to be used in group pile analyses.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.253-253
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• 2019

유사의 이동은 하천, 해안 지역과 같은 수계에서 하상의 변동, 침식과 퇴적을 일으켜 지형적인 변화를 초래한다. 유사의 이동은 유사의 특성과 유체의 유수동역학적 특성에 의해 결정되며 유체특성 간의 복잡한 상호 작용에 의해 변화한다. 유사가 가지는 점착성은 유사의 특성에 큰 영향을 끼친다. 입자의 크기가 매우 작은 점착성 유사는 그 표면이 가지는 전자기적 점착력에 의해 주위의 1차 입자나 다른 작은 알갱이들이 서로 뭉치는 응집과 충돌에 의해 크기가 작아지는 파괴의 과정을 겪는다. 이 과정을 응집현상이라고 하며 응집현상을 통해 점착성 유사의 크기와 밀도, 침강속도는 계속해서 변화한다. 따라서 점착성 유사의 응집거동 고려한 유사 이동 연구는 필수적이다. 과거 연구의 많은 사례에서 유사의 크기와 농도는 비례 관계를 가지는 것이 일반적이라 알려져 있다. 그러나 실제 현장에서 측정한 결과 유사의 크기와 농도가 반비례 관계를 가지는 특이점이 발견되었다. 실측 연구에서 발견된 응집거동에 따른 유사의 특성의 특이한 변화를 설명하기 위해 1차원 연직 수치 모형(1DV)을 이용하여 수치 실험을 수행하였다. 모의 수행 시, 흐름 조건을 크기와 방향이 일정한 순방향흐름(Current)에 특정 주기와 진폭을 가지는 진동 흐름(Oscillatory Flow)을 추가하여 진행하였다. 플럭의 성장과 그에 따른 입자의 크기는 많은 현상에 영향을 받는다. 그 중 응집현상의 응집 과정과 파괴 과정 중 어떤 현상이 더 우세한지 그 경쟁관계를 파악하여 플럭의 크기의 증감을 예측할 수 있게 농도(?)와 난류소산매개변수(?)를 이용하여 $c/G^{0.5}$로 매개화하였다. 실험 결과, 순방향 흐름을 제외하고 스토크스파 흐름 조건을 이용하여 진행된 모의에서는 플럭의 크기와 농도가 반비례하는 현상을 관찰할 수 없었으며 $c/G^{0.5}$ 의 변화 역시 흐름의 속도와 농도가 더 큰 지점에서 큰 값을 가지는 일반적인 결과를 나타내었다. 그러나 같은 조건에서 순방향흐름을 추가하여 모의한 결과에서는 플럭의 크기와 농도가 반비례하는 현상을 나타냈다. 연직 방향 $c/G^{0.5}$의 변화를 나타낸 그래프에서 응집과 파괴의 우세에 따라 $c/G^{0.5}$ 가 역전되는 현상을 확인하였다. 즉, 플럭의 크기는 난류의 구조와 그 영향에 의해 농도와 비례관계를 갖지 않을 수도 있다고 판단된다. 또한 본 연구에서 정상류 흐름 조건의 유무에 따라 플럭의 크기와 농도가 비례하거나 반비례하는 상반된 결과를 보였다. 정상류 흐름 조건이 난류의 강도에 큰 역할을 하며 이에 따라 비선형 관계에 영향을 끼친다는 것을 발견하였다. 그러나 흐름의 영향에 대한 더 자세한 분석은 본 연구에서 진행되지 않았으며 향후 연구 시에 분명히 고려되어야 할 사항이다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.405-410
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• 2021

In this study, a finite element analysis was performed applying a nonlinear material model and fatigue load conditions to evaluate the service life and spring stiffness of the resilient pad for rail fastening system. As a result of the fatigue analysis, the rate of change in spring stiffness compared to the initial condition was about 16%, indicating that fatigue hardening occurred. As for the stress generated in the longitudinal direction of the resilient pad, the difference between the stress generated at the center and the edge was about 10 times or more. In addition, it was analyzed that the equivalent stress of the outer boundary was more than twice as large as that of the central part. Therefore, it was analyzed that the damage and deformation of the resilient pad are the corners of the resilient pad under actual service conditions. The fatigue life diagram of the resilient pad (S-N curve) was derived using the equivalent stress of the resilient pad according to the fatigue cycles. Using the fatigue life diagram of the resilient pad derived in this study, it is considered that it can be used to predict the fatigue life under the relevant conditions by calculating the equivalent stress of the resilient pad under various load conditions.

• Journal of Climate Change Research
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• v.4 no.2
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• pp.95-104
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• 2013

Average cumulative precipitation in summer have increased by 350 mm compared with 1980s. As precipitation is expected to increase, the risk of landslides by heavy rainfall also is expected to rise. Therefore, establishment of adaptation plan for landslides is urgently needed. In 2011, Korea Ministry of Environment(KME) conducted vulnerability assessment to support establishment of adaptation plan for local governments. However, the result of vulnerability assessment had three limitations. First, KME didn't use standard scenario of Korea Meteorological Administration(KMA). Second, They conducted same standardization method for all variables. Third, They derived relative vulnerability which is not quantitative. The purpose of this study is to improve the limitations of existing vulnerability assessment and identify quantitative criteria to ensure scientific reliability. To achieve this purpose, we carried out three ways of advancement. First, application of new climate scenario, which is RCP 8.5 from KMA. Second, improvement of variables of vulnerability assessment. Third, derivation of quantitative criteria of vulnerability. The findings can support establishment of adaptation plan for local governments more effectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.488-496
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• 2023

Ship and bridge structures are a type of long box-shaped structure, and resistance to vertical bending moment is a key factor in their structural design. In particular, because box girders are repeatedly exposed to irregular wave loads for a long time, the continuous collapse behavior of structural members must be accurately predicted. In this study, plastic collapse behavior, including buckling according to load changes of the box girder receiving pure bending moments, was analyzed using a numerical analysis method. The analysis targets were selected as three box girders used in the Gordo experiment. The cause of the difference was considered by comparing the results of the structural strength experiment with those of non-linear finite element analysis. This study proposed a combination of the entire and local sagging shape to reflect the effect of the initial sagging caused by welding heat that is inevitably used to manufacture carbon steel materials. The procedures reviewed in the study and the contents of the initial sagging configuration can be used as a good guide for analyzing the final strength of similar structures in the future.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.458-467
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• 2010

In this paper, a new audio reproduction system was developed in which the cross-talk signals would be reasonably cancelled at an arbitrary listener position. To adaptively remove the cross-talk signals according to the listener's position, a method of tracking the listener position was employed. This was achieved using the two microphones, where the listener direction was estimated using the time-delay between the two signals from the two microphones, respectively. Moreover, room reverberation effects were taken into consideration where linear prediction analysis was involved. To remove the cross-talk signals at the left-and right-ears, the paths between the sources and the ears were represented using the KEMAR head-related transfer functions (HRTFs) which were measured from the artificial dummy head. To evaluate the usefulness of the proposed listener tracking system, the performance of cross-talk cancellation was evaluated at the estimated listener positions. The performance was evaluated in terms of the channel separation ration (CSR), a -10 dB of CSR was experimentally achieved although the listener positions were more or less deviated. A real-time system was implemented using a floating-point digital signal processor (DSP). It was confirmed that the average errors of the listener direction was 5 degree and the subjects indicated that 80 % of the stimuli was perceived as the correct directions.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Ecology and Environment
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• v.35 no.1 s.97
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• pp.28-35
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• 2002

The effect of physical parameters on water quality was analyzed using monitoring data of 193 agricultural reservoirs. The retention time of reservoirs ($t_d$) ranged between 10 and 140 days, and the ratio of drainage area (DA) to reservoir surface area (SA) was between 10 and 120. Both ratios of DA/SA and total area (TA)/ reservoir storage (ST) in Korean agricultural reservoirs were relatively greater than those in natural lakes in other countries. As retention time was plotted against DA/SA ratio, it was shorter in Korean reservoirs than natural lakes. The semi-logarithmic relationship between TA/SA and t>$t_d$ was $t_d\;=\;42.21(TA/ST)^{-1}$ (n = 50, $R^2\;=\;0.89$). While areal loading of total phosphorus (TP) was below $4\;gTP{\cdot}m^{-2}{\cdot}yr^{-1}$ in general, it exceeded $10\;gTP{\cdot}m^{-2}{\cdot}yr^{-1}$ in reservoirs where DA/SA ratio was greater than 100, which implies that areal loading of TP increases as DA/SA ratio increases. Chl-a concentration was positively related with the mean depth of reservoir, implying the higher Chl-a concentration with deeper the mean depth. Therefore, the deeper reservoir might be advantageous in water quality management perspective if other morphological conditions are similar. The empirical regression equation using physical parameters was also suggested in the estimation of TP concentration in the reservoirs. Combined information presented in this paper might be applicable to the water quality management in agricultural reservoirs.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Korean Journal of Microbiology
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• v.52 no.4
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• pp.406-414
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• 2016

Among 6 leu codons, CUG is the most frequently used codon in E. coli. It is recognized by leu-tRNA(CAG) encoded by four genes scattered on two chromosomal loci (leuT and leuPQV ). In the process of constructing a strain with no functional leu-tRNA (CAG) gene on chromosome, we made two mutant strains separately, one on leuPQV locus (${\Delta}leuPQV$), and the other on leuT locus [$leuT^*$(GAG)], where the anticodon of leuT was changed from CAG to GAG, thereby altering its recognition codon from CUG to CUC. We attempted to combine these two mutations by transduction using $leuT^*$(GAG) strain as a donor and ${\Delta}leuPQV$ strain as a recipient. Large and small colonies appeared from this transduction. From PCR and DNA sequencing, large colony was confirmed to be the reciprocal recombinant as expected, but the small colonies contained both mutant $leuT^*$(GAG) and wild type leuT (CAG) genes in the cell. This heterozygous diploid strain did not show any unusual morphology under microscopic observation, but, interestingly, it showed a linear growth curve in rich medium with much slower growth rate than wild type cell. It always formed homogenous small colonies in the selection medium, but, when there was no selection, it readily segregated into $leuT^*$(GAG) and leuT (CAG). From these observations, we suggested that the strain with both $leuT^*$(GAG) and leuT (CAG) genes was not a partial diploid (merodiploid), but a full diploid cell having two different chromosomes. We proposed a model explaining how such a heterozygous diploid cell was formed and how and why its growth showed a linear growth curve.