• The Korean Journal of Health Service Management
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• v.9 no.3
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• pp.43-55
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• 2015

Objectives : This study analyzed the managerial efficiency of hospitals and identified the productivity trends for three years. Methods : Data were collected from 44 tertiary hospitals and 32 university hospitals from 2009 to 2011. Efficiency scores and productivity trends were evaluated with the DEA (Data Envelopment Analysis) method. The input variables were the numbers of beds, doctors, nurses, and health personnel, and the medical costs. The output variables were the numbers of outpatients, and inpatients, and the medical revenues. Along with the traditional input-oriented DEA analysis, the Malmquist Productivity Index(MPI) was calculated. Results : First, the mean values of the study variables showed gradual increases in all the variables for all the study years. Second, technical efficiency scores varied depending on the study year. Third, MPI decreased from 2009 to 2010 (MPI=0.986), and then increased from 2010 to 2011(MPI=1.011). The contributions of the Efficiency Change Index and Technical Change Index on the MPI varied depending on the study year. Conclusions : This study provides information to hospital managers about changes in hospital performances. External environments had more influence on hospital performances, and hospital managers will need to manage these influences from factors surrounding the hospitals.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Environmental and Resource Economics Review
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• v.15 no.4
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• pp.643-672
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• 2006

This study has assessed the industrial human toxicity index by means of toxic substances emissions in South Korean industry. The data used in analysis are the 146 kinds of the toxic chemicals emissions and final demands, total outputs in the input-output table. As a results, human carcinogenic index was $11.86198{\times}10^3$ for overall industries, and $0.26360{\times}10^3$ for average. The industries of higher human toxicity index can be ranked as follows: Mother vehicles and parts (7.85033) > Pig iron and crude steel(4.57409) > Primary iron and steel products(4.36668) > Other transportation equipments and parts(3.43293) > Inorganic basic chemical products(2.64379), etc. Such result can be considered as the priority order of regulation based on industrial characteristics, when the demand and industrial policies should be carried out for the deduction fof toxic substances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7A
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• pp.515-522
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• 2009

In this paper, a novel transmission scheme with spatial modulation is proposed for coded orthogonal frequency division multiplexing (OFDM). The multiple-input multiple-output (MIMO) technique, so-called spatial modulation (SM), divides input data into antenna index and data signals, transmitting data signals through the specific antenna chosen by the antenna index. In order to retrieve data stream at the receiver, SM needs to detect the antenna index which means that data signals are transmitted via a certain antenna. For this reason, it should be guaranteed that channel matrix is orthogonal. For the real environment, a MIMO channel has difficulty in maintaining orthogonality due to spatial correlation. Moreover, the receiver of the conventional SM is operated by hard decision, so that this scheme has a limit to be adopted for practical systems. Therefore, soft-output demappers for the conventional and proposed schemes are derived to detect antenna index and data stream by soft decision, and a novel transmission scheme combined with spatial modulation is proposed to improve the bit error rate (BER) performance of the conventional scheme.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.316-323
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• 2010

In this paper, we propose fingertip extraction and hand motion recognition method for augmented reality applications. First, an input image is transformed into HSV color space from RGB color space. A hand area is segmented using double thresholding of H, S value, region growing, and connected component analysis. Next, the end points of the index finger and thumb are extracted using morphology operation and subtraction for a virtual keyboard and mouse interface. Finally, the angle between the end points of the index finger and thumb with respect to the center of mass point of the palm is calculated to detect the touch between the index finger and thumb for implementing the click of a mouse button. Experimental results on various input images showed that our method segments the hand, fingertips, and recognizes the movements of the hand fast and accurately. Proposed methods can be used the input interface for augmented reality applications.

• International Journal of Quality Innovation
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• v.10 no.1
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• pp.1-23
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• 2009

Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan-do-check-act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma ($6{\sigma}$) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.143-159
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• 1998

This paper proposes a new type of fuzzy approximate reasoning method that combines a self organizing feature map and a fuzzy logic. Previous methods considered only input part to determine the number of fuzzy rules, while this paper considers both input and output parts simultaneously. Our approach proved to improve the inference performance. We also developed a new index for avoiding overlearning which guarantees more accurate results. Experimental results showed that our approach surpasses the performance of Takagi & Hayashi (1991) approach.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.21-44
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• 2010

The paper estimates changes in total factor productivity and technical efficiency change index and technical change index using Malmquist productivity index(MPI) in fisheries wholesale products over the time period of 2006 through 2008. The model considers a number of employees and operating costs as input factors, and sales and EBIT(earnings before tax and interest) as output factors. The results indicate that, between 2006 and 2007, there is in general technical progress in which TCI(Technical Change Index) indicates 2.7994 in the sale scale of 50 million won through 100 million won, while there are no efficiency in TECI(Technical Efficiency Change Index), PECI(Pure Efficiency Change Index) and SECI(Scale Efficiency Change Index) which are estimated to be around 1. Between 2007 and 2008 technical efficiency and technical progress are generally declined, compared to those of 2006 and 2007. Wilcoxon's rank-sum test shows that there are statistically significant difference of TCI and MPI between two periods at the level of 5%, while there are statistically significant difference of TECI, PECI and SECI between two periods at the level of 5%.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.475-488
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• 2017

To simulate accurate drought, a drought index is needed to reflect the hydrometeorological phenomenon. Several studies have been conducted in Korea using the Modified Surface Water Supply Index (MSWSI) to simulate hydrological drought. This study analyzed the limitations of MSWSI and quantified the uncertainties of MSWSI. The influence of hydrometeorological components selected as the MSWSI components was analyzed. Although the previous MSWSI dealt with only one observation for each input component such as streamflow, ground water level, precipitation, and dam inflow, this study included dam storage level and dam release as suitable characteristics of the sub-basins, and used the areal-average precipitation obtained from several observations. From the MSWSI simulations of 2001 and 2006 drought events, MSWSI of this study successfully simulated drought because MSWSI of this study followed the trend of observing the hydrometeorological data and then the accuracy of the drought simulation results was affected by the selection of the input component on the MSWSI. The influence of the selection of the probability distributions to input components on the MSWSI was analyzed, including various criteria: the Gumbel and Generalized Extreme Value (GEV) distributions for precipitation data; normal and Gumbel distributions for streamflow data; 2-parameter log-normal and Gumbel distributions for dam inflow, storage level, and release discharge data; and 3-parameter log-normal distribution for groundwater. Then, the maximum 36 MSWSIs were calculated for each sub-basin, and the ranges of MSWSI differed significantly according to the selection of probability distributions. Therefore, it was confirmed that the MSWSI results may differ depending on the probability distribution. The uncertainty occurred due to the selection of MSWSI input components and the probability distributions were quantified using the maximum entropy. The uncertainty thus increased as the number of input components increased and the uncertainty of MSWSI also increased with the application of probability distributions of input components during the flood season.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.163-169
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• 2014

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.