• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.3
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• pp.299-306
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• 2001

It is one of the most effective methods for performance improvement of electric railway vehicle to make better the wheel-rail adhesion character. In order to research adhesion character, adhesion system is developed. The experiment system makes it possible to change various adhesion parameters. This paper studied to restrain vibration of slip speed using torque control gy means of slip speed.

• Journal of the Korean Society for Railway
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• v.8 no.5
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• pp.477-482
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• 2005

Through a year's commercial operation, Korea High Speed Railway has solved defectives from several breakages at the beginning and is going into the stage of stable operation. Among issues, wheel wear becomes a matter of primary concerns in view of vehicle's stability and maintenance. It was understood as above that wear status has been improved in the test by which railway system including vehicles and tracks was stabilized during a year's commercial operation, comparing to that with excessive wear in the trial operation prior to opening to the public. To make out wheel's wear status and characteristics of equivalent conicity at present when the service has been introduced a year ago and the average cumulative mileage of vehicles reach almost 500,000km, wheel's wear types were analyzed with the current vehicles in service.

• Journal of the Korean Society for Railway
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• v.8 no.6 s.31
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• pp.539-543
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• 2005

This paper proposed the EGBOM, Extended Generic BOM, which is more flexible BOM system than GBOM. The GBOM system has the limitation to construct Result BOM and to select options by choosing only one Variant under given Cluster. In order to overcome this limitation, we propose the EGBOM which can make more various Result EGBOM, which can use to rolling stock through constructing urban transit BOM using the proposed EGBOM. The last, this paper describes the various Result BOM examples constructed from Source BOM of UT_EGBOM, such as rolling-stock maintenance BOM, individual car BOM, etc.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1646-1648
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• 2005

This paper proposes the Advanced Generic BOM(AGBOM) which is more flexible BOM system than existing GBOM. The existing GBOM system has the limitation of Result BOM construction and selecting options by choosing only one Variant under given Cluster. In order to overcome this limitation, AGBOM can make more various Result BOM by selecting several Variants under given Cluster. Also, this paper describes UT-AGBOM which can use to rolling stock through constructing urban transit BOM using the proposed AGBOM. The last, this paper describes the various Result BOM examples constructed from Source BOM of UT-AGBOM, such as rolling-stock maintenance BOM, individual car BOM, etc.

• Culinary science and hospitality research
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• v.20 no.2
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• pp.89-99
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• 2014

This study aims to make stock using cod bones with the hot water extraction method. Moisture content, chromaticity, pH, salinity, sugar content, mineral contents, quantitative analysis, and overall acceptance were studied to determine the standard formula. The results are as follows. The moisture content decreased (p < 0.001) and color value increased as heating time increased. The pH was highest in CS5 which was heated for 30 minutes and lowest in CS1. The salinity and sugar content significantly increased with more heating time (p < 0.001). In terms of mineral contents, sodium was highest in 138.87~154.17 mg, magnesium and iron showed proportion difference with increased heating time. The mineral analysis test result revealed that sodium, magnesium and iron showed proportional difference with increased heating time, while potassium and calcium did not change. The result of quantitative analysis test showed transparency, fishy smell, delicate flavor, savory flavor, salt taste and umami taste became stronger as high-pressure heating time increased. From these result, CS4 was evaluated to be the best in appearance, flavor, taste, aftertest and overall acceptance. Also, 60 minute high-pressure heating time is the most desirable to produce stock using cod bones as a main ingredient.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.24-26
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• 1995

The Development of A.C. Induction Motor for Railway Rolling Stock. The traction motor is designed as 4-pole induction motor with self ventilation. The winding insulation is throughout of materials of class C. The rotor is designed as a squrrel rotor with copper bar and casting. The rotor speed is detected by means of a pulse generator. The newer tection motor have no casting(frame). Punched-in holes make up the air duct and transfer the heat losses in complete. Maximim motor rpm is higher due to rotor construction. New is the entry of water-cooled traction motors in urban, However the water cooling design in - unfortunately - not applicable in traction motor.

• Analyses & Alternatives
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• v.3 no.1
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• pp.135-157
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• 2019

The linkages between asset prices and macroeconomic outcomes are long-standing issue to both economists and monetary authorities. This paper explores the impact of asset prices on output and price in China. It focuses on the impacts of asset prices on the low quantiles of GDP gap and high quantiles of price gaprespectively. The main findings are the following: the influence of stock price gap, stock returns, and money growth on the different quantile of GDP gap and price gap are noticeable different, and there are significant impacts on the left tail of GDP gap distribution and on the right tail of price gap distribution. This implies that the results coming from simple regression will underestimate the economic risk imposed by asset price volatility. Moreover, these results also provide the caveat that one should cautiously distinguish the meaning of asset price gap and asset price growth rate and use them, through their contents are similar in some sense. One implication for monetarypolicy is that authority should interpret the relationship between asset prices and macro-economy in wider perspectives, and make the policy decision taking the impacts of asset prices on the tails of economy.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1281-1286
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• 2005

This study shows organizational designs to increase the coordination between infrastructure and rolling stock operation organizations after rail reform based on wheel/rail interface and train/track interaction. Information sharing, face-to-face meeting, liaison role, task force, manager responsible for coordination, and team organization could help to coordinate infrastructure construction plan and train operation plan. It is necessary for the organizations to begin to study the interaction between track and train in Korean environments to make the coordination more efficient.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.