• Journal of Distribution Science
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• v.12 no.11
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• pp.77-83
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• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.93-105
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• 2014

This study evaluated the relative efficiency of mobile emission reduction countermeasures through a Data Envelopment Analysis (DEA) approach and determined the priority of countermeasures based on the efficiency. Ten countermeasures currently applied for reducing greenhouse gases and air pollution materials were selected to make a scenario for evaluation. The reduction volumes of four air pollution materials(CO, HC, NOX, PM) and three greenhouse gases($CO_2$, $CH_4$, $N_2O$) for the year 2027, which is the last target year, were calculated by utilizing both a travel demand forecasting model and variable composite emission factors with respect to future travel patterns. To estimate the relative effectiveness of reduction countermeasures, this study performed a super-efficiency analysis among the Data Envelopment Analysis models. It was found that expanding the participation in self car-free day program was the most superior reduction measurement with 1.879 efficiency points, followed by expansion of exclusive bus lanes and promotion of CNG hybrid bus diffusion. The results of this study do not represent the absolute data for prioritizing reduction countermeasures for mobile greenhouse gases and air pollution materials. However, in terms of presenting the direction for establishing reduction countermeasures, this study may contribute to policy selection for mobile emission reduction measures and the establishment of systematic mid- and long-term reduction measures.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.94-94
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• 2023

유역에서의 홍수를 예측하기 위한 다양한 강우-유출 모형들이 개발되어 사용되고 있다. 개념적 강우-유출 모형들은 신뢰성과 적용성이 높아 실무에서 널리 활용되어왔으나, 강우-유출 과정을 단순화하여 고려하므로 유출예측의 정확도에 한계가 있다. 또한 모형의 매개변수에 여러 불확실성이 존재하므로 충분한 양의 관측자료를 사용한 보정 작업이 필요하다. 물리적 강우-유출 모형들은 유출예측 결과가 비교적 물리적으로 정확하다는 장점이 있지만, 높은 계산 비용 및 수치적 불안정성으로 인하여 실무에의 적용이 힘들다. 본 연구에서는 홍수 예측의 정확도와 효율성을 모두 확보할 수 있는 하이브리드 기법을 개발하였다. 본 연구에서 개발한 기법은 물리적 모형인 동역학파 모형과 개념적 모형인 순간단위도 모형, 그리고 딥러닝 모형을 결합하여 사용하는 기법이다. 유역의 조도계수 및 지형을 활용한 동역학파 시뮬레이션을 수행하였으며, 동역학파 시뮬레이션 결과 및 멱함수로 나타내어지는 비선형적 강우-유출 관계를 이용하여 유역의 순간단위도를 유도였다. 또한, 딥러닝 모형인 LSTM 모형을 활용하여 강우손실 매개변수를 추정하였으며, 이를 이용하여 강우손실을 계산한 후 유효강우주상도를 산정하였다. 그리고 유역 출구에서의 홍수수문곡선은 유효강우주상도와 순간단위도를 활용한 회선적분을 통해 예측되었다. 본 연구에서 개발한 기법을 시험유역 및 자연유역에서의 홍수 예측에 적용해보았으며, 예측 결과는 NSE=0.55-0.90, R²=0.67-0.95의 높은 정확도를 보였다. 본 연구에서 유도하는 순간단위도는 한 유역에서 유일하지 않으며, 유효 강우강도의 함수이므로 홍수 예측에 비선형적 강우-유출 관계를 고려할 수 있으며, 수많은 유효 강우강도에 대한 순간단위도들은 멱함수를 이용하여 순간적으로 유도될 수 있다. 또한, 유역의 강우 특성이나 지표면의 토양수분, 식생과 같은 특성을 딥러닝 모형을 통해 고려함으로써 강우 손실 산정의 불확실성을 줄일 수 있다. 또한, 순간단위도 유도를 위한 기초작업인 동역학파 시뮬레이션은 유역의 지형과 조도계수만을 필요로 하므로 미계측 유역에의 적용이 유리하다.

• Atmosphere
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• v.34 no.2
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• pp.153-176
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• 2024

At 0843 UTC 30 May 2021, a commercial aircraft encountered severe turbulence at z = 11.5 km associated with the rapid development of Mesoscale Convective System (MCS) in the Gyeonggi Bay of Korea. To investigate the generation mechanisms of Near-Cloud Turbulence (NCT) near the MCS, Weather Research and Forecasting model was used to reproduce key features at multiple-scales with four nested domains (the finest ∆x = 0.2 km) and 112 hybrid vertical layers. Simulated subgrid-scale turbulent kinetic energy (SGS TKE) was located in three different regions of the MCS. First, the simulated NCT with non-zero SGS TKE at z = 11.5 km at 0835 UTC was collocated with the reported NCT. Cloud-induced flow deformation and entrainment process on the downstream of the overshooting top triggered convective instability and subsequent SGS TKE. Second, at z = 16.5 km at 0820 UTC, the localized SGS TKE was found 4 km above the overshooting cloud top. It was attributed to breaking down of vertically propagating convectively-induced gravity wave at background critical level. Lastly, SGS TKE was simulated at z = 11.5 km at 0930 UTC during the dissipating stage of MCS. Upper-level anticyclonic outflow of MCS intensified the environmental westerlies, developing strong vertical wind shear on the northeastern quadrant of the dissipating MCS. Three different generation mechanisms suggest the avoidance guidance for the possible NCT events near the entire period of the MCS in the heavy air traffic area around Incheon International Airport in Korea.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.597-605
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• 2014

The continuous observations for atmospheric aerosols were conducted during 3 years (2009 to 2011) by using Gwangju Institute of Science and Technology (GIST) multi-wavelength Raman lidar at Gwangju, Korea ($35.10^{\circ}N$, $126.53^{\circ}E$). The aerosol depolarization ratios calculated from lidar data were used to identify the Asian dust layer. The optical properties of Asian dust layer were different according to its vertical distribution. In order to investigate the difference between the optical properties of each individual dust layers, the transport pathway and the transport altitude of Asian dust were analyzed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We consider that the variation of optical properties were influenced not only their transport pathway but also their transport height when it passed over anthropogenic pollution source regions in China. The lower particle depolarization ratio values of $0.12{\pm}0.01$, higher lidar ratio of $67{\pm}9sr$ and $68{\pm}9sr$ at 355 nm and 532 nm, respectively, and higher ${\AA}ngstr\ddot{o}m$ exponent of $1.05{\pm}0.57$ which are considered as the optical properties of pollution were found. In contrast with this, the higher particle depolarization ratio values of $0.21{\pm}0.09$, lower lidar ratio of $48{\pm}5sr$ and $46{\pm}4sr$ at 355 nm and 532 nm, respectively, and lower ${\AA}ngstr\ddot{o}m$ exponent of $0.57{\pm}0.24$ which are considered as the optical properties of dust were found. We found that the degree of mixing of anthropogenic pollutant aerosols in mixed Asian dust govern the variation of optical properties of Asian dust and it depends on their altitude when it passed over the polluted regions over China.