• Journal of Korea Technology Innovation Society
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• v.13 no.3
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• pp.513-537
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• 2010

According to the "FDI-as-market-discipline" hypothesis, inward FDI acts as a mechanism of change in market structure affecting innovative activities of domestic firms. We used panel KIS data for testing this hypothesis. Binary probit estimation shows that, in contrast to the German case of Bertschek (1995), FDI is insignificant in Korean case for explaining product innovation. 1his result maybe comes from the fact that the industries in Korea are more monopolistic or oligopolistic than those of Germany. Using panel data, we tried random parameter estimation using matrix weighted average of GLS and OLS. The result shows different estimates from cross-section outcome and panel estimation with parameter homogeneity, so we can infer large parameter heterogeneity across firms. But, interpretation for FDI variable is similar across panel and cross-section estimation.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1128-1136
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• 2012

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.29-32
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• 2015

It has been a big trend of the convergence technologies about communication systems and vehicular industry to improve safety and convenience. To achieve a number of infotainment vehicular applications, vehicles should transmit information with high reliability. A robust and accurate channel estimation scheme is of great importance to achieve the goal. In this paper, we present a novel enhanced decision-directed channel estimation scheme called FADP (Frequency Averaging Data Pilot) for dynamic time-varying vehicular channels in IEEE 802.11p. We use linear averaging filtering in frequency domain, and utilize the correlation characteristic of the channels between the adjacent two data symbols, update the CR in time domain to get more accuracy. Finally, analysis and simulation results reveal that compared with exist schemes, the proposed scheme has a good performance in mean square error (MSE) and bit error rate (BER).

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.161-168
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• 2015

The epidemic is seemed to be extremely difficult for accurate predictions. The new models have been suggested that show quite different results. The basic reproductive number of epidemic for consequent time intervals are estimated based on stochastic processes. In this paper, we proposed a transmission distribution estimation for Ebola virus disease epidemic model. This estimation can be easier to obtain in real time which is useful for informing an appropriate public health response to the outbreak. Finally, we implement our proposed method with data from Guinea Ebola disease outbreak.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.505-513
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• 2017

Recently, numerous small area estimation studies have been conducted to obtain more detailed and accurate estimation results. Most of these studies have employed spatial regression models, which require a clear definition of spatial neighborhoods. In this study, we introduce the Delaunay triangulation as a method to define spatial neighborhood, and compare this method with the k-nearest neighbor method. A simulation was conducted to determine which of the two methods is more efficient in defining spatial neighborhood, and we demonstrate the performance of the proposed method using a land price data.

• Korean Journal of Agricultural Science
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• v.47 no.1
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• pp.19-28
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• 2020

This study proposed a simple estimation method for irrigation return flow from paddy fields using the water balance model. The merit of this method is applicability to other paddy fields irrigated from agricultural reservoirs due to the simplicity compared with the previous monitoring based estimation method. It was assumed that the unused amount of irrigation water was the return flow which included the quick and delayed return flows. The amount of irrigation supply from a reservoir was estimated from the reservoir water balance with the storage rate and runoff model. It was also assumed that the infiltration was the main source of the delayed return flow and that the other delayed return flow was neglected. In this study, the amount of reservoir inflow and water demand from paddy field are calculated on a daily basis, and irrigation supply was calculated on 10-day basis, taking into account the uncertainty of the model and the reliability of the data. The regression rate was calculated on a yearly basis, and yearly data was computed by accumulating daily and 10-day data, considering that the recirculating water circulation cycle was relatively long. The proposed method was applied to the paddy blocks of the Jamhong and Seosan agricultural reservoirs and the results were acceptable.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.961-966
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• 2000

It is difficult to change or remedy concrete structure after hardened. It is usual to evaluate the quality of hardened concrete using several test method. This study was performed to make fundamental data that could be used to evaluate the quality of hardened concrete. This study is to estimate mix proportion of hardened concrete. Each elements of concrete needed different estimation methods. First, the cement that handled by the most important compounds measured by XRF(X-ray fluorecence) machine with scanning Ca-K${\alpha}$. Second, the coarse aggregate that divided by maximum size measured by the area comparison method that starts from the assumption of uniform distribution. Third, the fine aggregate measured by the weight comparison method that needs several prerequsite constants which concerned cement hydration reaction. Fourth, the water content would be estimated by expert system that has data base of design data, the contents of above estimation results, the characteristics of concrete strength. As the result of the above research, some conclusions are as follows. The cement estimation method resulted by reliability of mean 96.7%, standard deviation 3.92. The area comparison method resulted by reliability of mean 95.3%, standard deviation 2.08. The weight comparison method resulted by reliability of mean 93.3%, standard deviation 3.35.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.1
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• pp.82-99
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• 1997

The purpose of this study is to establish work classification system of landscape construction in order to offer the basis of new estimation system of public landscape construction. New estimation system is based on historical construction data. For application of this system, the standard work classification system is necessary. Because extensive cost data should be accumulated under an unified construction work classification system. In the study of new estimation system carried by KICT(Korea Institute of Construction Technology), landscaping works belong to earth work of civil engineering. It looks very unreasonable work classification, because landscape archtecture has its own specialties and professional domain. In this study, information classification systems in the construction industry and various landscaping works of housing developments are analysed. As a result. a standard work classification system of housing landscape construction is proposed in section VI-3. This standard work classification structure consists of three levels divisions (i.e large work division, middle work division, small work division) . Now in this study, housing landscape construction works are divided into four large works and twenty six middle works. According to work attributes, middle and small work division is possible to subdivide into details.