• Journal of Distribution Science
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• v.12 no.6
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• pp.21-29
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• 2014

Purpose - This research undertakes to understand the competitiveness of the steel distribution industry of both Korea and China to strengthen Korea-Sino economic cooperation, examines impediments to trade between the two countries to analyze causes which affect trade, and examines improvements in these areas to identify means of trade expansion. Through this survey of a defined period, we can identify the structural factors of trade dependence in the relationship between Korea and China. Research design, data, and methodology - The data were collected from the Korea Traders Association, the Korea Customs Office, and UN Comtrade, from which whole table indexes are calculated. The research methodology uses trade-related indexes to focus on analyzing comparative advantages based on time-series analysis statistics data (2000-2012) by using the analysis index of trade intensity index (TII), the revealed comparative advantage index (RCA), and the trade specialization index (TSI). Results - The export ratio for Korea to China was slightly higher in 2000 at 2.867, and the export ratio for Korea to China was sustained in 2005. However, it diminished gradually, reaching 1.263 in 2012. During the period 2000-2012, the indexes were maintained without any significant change. However, they still remain close to -1. In particular, in 2012 it is the closest it has ever been to -1. Therefore, China has a comparative advantage in export specialization. On the other hand, Korea has a comparative advantage in import specialization. For the research period, all indexes were much lower than 1, which means that Korea has consistently had a comparative disadvantage against China for the past 10 years when compared to other industries, even though it experienced improvement in 2000. Conclusions - The summary of conclusions based on empirical analysis research are as follows: First, per the trade intensity index of industries between the two countries, we conclude that the export ratio index in 2000 is 2.867, which means the export ratio of Korea to China is slightly higher. Furthermore, the ratios of 2.259 and 1.263 held in 2005 and 2012, respectively, meaning that the export ratio of Korea to China was maintained in 2005, but was diminishing gradually as the index in 2012 was 1.263. Second, per the trade specialization index of the steel distribution industry between Korea and China, the value was -0.379 in 2000, -0.368 in 2005 and -0.568 in 2012. Looking at the whole period of 2000-2012, the indexes remained without any significant change. However, they are still moving closer to -1. In particular, in 2012 it is the closest it has ever been to -1. Third, regarding the revealed comparative advantage index of the steel distribution industry between Korea and China, the RCA indexes in 2005 and 2012 are 0.246 and 0.306, respectively, which are still far from 1, even though the index has improved compared to the 2000's value of 0.0001. Therefore, the Korean steel distribution industry is at a significant comparative disadvantage to that of the Chinese steel distribution industry.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.664-674
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• 2001

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.

• International Commerce and Information Review
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• v.10 no.4
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• pp.495-512
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• 2008

Because Korea and Japan has joined WTO and OECD, it is impossible to carry out a direct export-promoted policy such as export subsidies. Therefore, the only policy which is internationally valid for promoting an export is the export insurance. Hence export insurance system became more useful tool since it's one of the few allowed subsidies under WTO. This paper examines to find the impacts of export insurance on the export supply in Korea and Japan. The period of data is from 1980 to 2006. Unlike previous studies on the effectiveness of export subsidy in export supply, the current study examines the stationarity nature of the concerned variables. The unit root tests show that all variables are not I(0) Time Series. Instead, they are I(1) Time Series. To this, cointegration verification was conducted based on the use of Johansen verification method to define the existence (or non-existence) of long-term balance relationship among variables. The concerned variables are revealed to be cointegrated. In order to analyze, this study introduce a VEC model. In this paper we construct two VEC models. The one is about Korea, the other is about Japan. The empirical evidences show that export insurance system has not contributed to promoting export supply in Japan. But the results of empirical analysis showed significant and positive effects of Korea export insurance upon the export supply.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.155-169
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• 2008

A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.1
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• Journal of Korean Society for Quality Management
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• v.27 no.3
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• pp.51-66
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• 1999

This paper empirically investigates the impact of winning a quality award by investigating the rate of return of a firm's stock in the stock market, and by analyzing the contribution and effectiveness to a firm's competitiveness. It also compares the effect of firms winning MB(Malcolm Baldrige) award with that of firms winning Korean quality awards on the stock market. A comparative method is used to analyze the change of award-winning firms'rate of return and then they are classified by time-series, cross-sectional, firm's size, award agency, and the year of receiving the award. The number of firms employed in this study is 74, however, multiple award-winning firms are included in the analysis, which increased the sample size to 118. Results indicate that Korean quality awards improve an award-winning firms'market value but not as much as the MB award did.