• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.50-59
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• 2007

The advent of towed geo-electrical array surveying on water and land has resulted in datasets of magnitude approaching that of airborne electromagnetic surveying and most suited to 1D inversion. Robustness and complete automation is essential if processing and reliable interpretation of such data is to be viable. Sharp boundaries such as river beds and the top of saline aquifers must be resolved so use of smoothness constraints must be minimised. Suitable inversion algorithms must intelligently handle low signal-to-noise ratio data if conductive basement, that attenuates signal, is not to be misrepresented. A noise-level aware inversion algorithm that operates with one elastic thickness layer per electrode configuration has been coded. The noise-level aware inversion identifies if conductive basement has attenuated signal levels so that they are below noise level, and models conductive basement where appropriate. Layers in the initial models are distributed to span the effective depths of each of the geo-electric array quadrupoles. The algorithm works optimally on data collected using geo-electric arrays with an approximately exponential distribution of quadrupole effective depths. Inversion of data from arrays with linear electrodes, used to reduce contact resistance, and capacitive-line antennae is plausible. This paper demonstrates the effectiveness of the algorithm using theoretical examples and an example from a salt interception scheme on the Murray River, Australia.

• Korean Journal of Social Welfare
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• v.67 no.4
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• pp.279-299
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• 2015

This study examines the impact of Long-Term Care Insurance(LTCI) on family caregivers(especially focused on female household members) labor supply in South Korea. When public care and informal care are substitutes, LTCI will change allocation of time of family caregivers to spend more time to paid work. The impact of LTCI on labor supply depends on each country's institutional level of public care services. If public care can not substitute for informal care, labor supply of family caregivers will not rise significantly. The conclusions of vigorous empirical study from western countries' are incompatible and problem of endogeneity in terms of methodology has been raised consistently. The dataset of this study are used the third and ninth waves of Korea Welfare Panel. As a result, the introduction of LTCI had no effect on labor supply of household members. Robust findings suggest the positive effects of caregiving on labor market outcomes in simple comparison t-test, but not in fixed-effect regression. Compared with western countries, South Korea's public care services can be interpreted as a supplement to only part that remained at the level does not substitute informal care. These findings may suggest that if LTCI become much more prevalent in the future, senior citizens and family members will be able to choose the LTCI arrangement that best suits their needs.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.378-388
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• 2016

International societies are currently working together to achieve the Climate-Smart Agriculture (CSA) initiative which aims the triple wins: (1) sustainably increasing agricultural productivity and incomes; (2) adapting and building resilience to climate change; and (3) mitigating greenhouse gases emissions. In terms of its scope and context, CSA follows the '3Nong (三農)' vision cast about 200 years ago by Dasan Jeong Yak-Yong who emphasized the triad of governance, management and monitoring towards comfortable, profitable and noble agriculture. Yet, the CSA provides the practical aims that facilitate the development of holistic indicators for quantitative evaluation and monitoring, on which decision-making support system is based. In this study, we introduce an agent-based model, i.e. Mathematical Programming Multi-Agent Systems (MP-MAS), as a tool for supporting the decision-making toward CSA. We have established the initial version of MP-MAS adapted for domestic use and present the preliminary results from an application to the rice farming case in Haenam, Korea. MP-MAS can support both farmers and policy-makers to consider diverse management options from multiple perspectives. When the modules for system resilience and carbon footprint are added, MP-MAS will serve as a robust tool that fulfills not only CSA but also Dasan's '3Nong' vision of sustainable agricultural-societal systems.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• International Area Studies Review
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• v.21 no.4
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• pp.85-100
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• 2017

The study tries to shed empirical light on the relation between the concentration of population in the primary city and per capita economic growth of the country, using the data for 113 nations over the period, 2000-2010. The concentration of population is measured in two ways, the ratio of the primary city's population to the total and to the second city. Using the ratio of the primary city's population to that of the entire nation, the empirical results neither show the robust negative relations nor the reverse U relation between primary city's concentration and economic growth. The ratio of the primary city to the second city however turns out to have a negative relation to per capita GDP growth. This result implies economic growth of a nation can be enhanced by decreasing the gap between the primary and the second ranked cities and does not support the reverse U hypothesis by Handerson(1974, 2003).

• Environmental and Resource Economics Review
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• v.13 no.1
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• pp.33-47
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• 2004

Even though the price of extracted but unprocessed coal has been available in Korea, the use of it as scarcity index would be inappropriate because of price subsidy. Following Halvorsen and Smith(1984), Kim and Lee(2002) derived estimates of the shadow price of unextracted coal by estimating the restricted cost function and differentiating with respect to the quantity of coal extracted. In Korea, however, due to the limited data the capital prices have been computed inconsistently case by case without relying on the robust formula like the Christensen-Jorgenson methodology used in US, which could result in biased estimators of the restricted cost function. In the paper the shadow prices of the resources in situ are obtained by measuring an input distance function defined by Shephard (1970), which requires only the data on the quantities of inputs and output. Empirical results for the Korean coal mining industry show that these shadow prices as a coal scarcity have increased fast by approximately three times in comparisons with those obtained by Kim and Lee.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.801-806
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• 2004

Although the technology for emotion recognition is important one which demanded in various fields, it still remains as the unsolved problem. Especially, it needs to develop the algorithm based on human facial image. In this paper, we propose the facial image analysis algorithm for emotion recognition. The proposed algorithm is composed as the facial image extraction algorithm and the facial component extraction algorithm. In order to have robust performance under various illumination conditions, the fuzzy color filter is proposed in facial image extraction algorithm. In facial component extraction algorithm, the virtual face model is used to give information for high accuracy analysis. Finally, the simulations are given in order to check and evaluate the performance.

• The Journal of Information Technology
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• v.2 no.2
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• pp.251-267
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• 1999

This study examines that pricing multiple on and incremental explanatory power of equity book value(earnings) increase(decrease) as financial health decrease. Test using a sample of 75 bankrupt firms and test using a cross-sectional, pooled sample both yield inference consistent with predictions. It is thus hypothesized that the more bankrupt time are, the higher(lower) pricing multiple book value of equity(earnings) obtained. Findings are robust to inclusion of controls for debt/assets ratio, ROA, and ROIC. Overall, the results is the hypothesis.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1245-1255
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• 2017

The online learning is a process of obtaining the solution for a given objective function where the data is accumulated in real time or in batch units. The stochastic gradient descent method is one of the most widely used for the online learning. This method is not only easy to implement, but also has good properties of the solution under the assumption that the generating model of data is homogeneous. However, the stochastic gradient method could severely mislead the online-learning when the homogeneity is actually violated. We assume that there are two heterogeneous generating models in the observation, and propose the a new stochastic gradient method that mitigate the problem of the heterogeneous models. We introduce a robust mini-batch optimization method using statistical tests and investigate the convergence radius of the solution in the proposed method. Moreover, the theoretical results are confirmed by the numerical simulations.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.2 s.40
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• pp.3-14
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• 2007

This study proposes an automatic method to generate 3D building models using a draft map, which is an intermediate product generated during the map generation process based on aerial photos. The proposed method is to generate a terrain model, roof models, and wall models sequentially from the limited 3D information extracted from an existing draft map. Based on the planar fitting error of the roof corner points, the roof model is generated as a single planar facet or a multiple planar structure. The first type is derived using a robust estimation method while the second type is constructed through segmentation and merging based on a triangular irregular network. Each edge of this roof model is then projected to the terrain model to create a wall facet. The experimental results from its application to real data indicates that the building models of various shapes in wide areas are successfully generated. The proposed method is evaluated to be an cost and time effective method since it utilizes the existing data.