• Journal of Intelligence and Information Systems
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• v.2 no.2
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• pp.43-54
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• 1996

Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.

• International Area Studies Review
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• v.21 no.1
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• pp.181-200
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• 2017

This study investigates the impact of exchange rate and exchange rate volatility on the stock prices of eight industries from 2006 to 2015. The first and second exchange rate exposure of these eight industries is estimated with respect to four different exchange rates, namely the US dollar, Japanese yen, European currency unit, and British pound. In exchange rate exposure, stock prices in foods-beverages, paper-wood, electricity-gas, and banks industries are negatively related to exchange rate, whereas stock prices in electrical-electronic equp. and transport-equp. industries are positively related to exchange rate as expected. However stock price in machinery industry is negatively related to exchange rate, which is opposite to the expectation. Negative relationship is found between stock price in chemicals industry and exchange rate. In exchange rate volatility exposure, stock price in paper-wood industry is found to be negatively related to exchange rate volatility. Stock price in banks industry is also negatively related to exchange rate volatility. This result is opposite as expected, because banks are supposed to get more revenue by issuing derivatives related to foreign exchange when exchange rate volatility increases.

• IE interfaces
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• v.11 no.3
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• pp.193-207
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• 1998

The agriculture and fishery share in the Korean GDP is continuously decreasing after 1960s. Furthermore the proportion of these industries in the GDP has diminished as low as 10 percent in recent years. However, the stockbreeding sector in these industries are considerably expanded. More than 50 percent of the whole farmhouses are involved in the livestock farming, and the stock farming portion is steadily increased in its size and scope. Thus, the mechanization and the automization of stockbreeding equipments are greatly required to reduce down production cost, as well as to win the competitiveness in the global market. From this aspect, developed in this paper is a stockbreeding management system (SMS) for dairy cattle, which can be used in small and medium sized dairy farms. First, the basic schema of the stockbreeding management system are addressed in view of stockbreeding management for individual dairy cattle. Electronic identification (EI) systems and sensory devices have changed stockbreeding management strategy from group stock control into individual stock control manner. The SMS receives stock body measurement data through the sensory devices such as weight, temperature, and milk conductivity meters. A common database then integrates those measuring data together so that the SMS can determine the appropriate solution on each stock's breeding such as feeding and milking. Thus, each stock can be supervised by a sophisticated SMS that provides the best solution to the stockbreeding throughout the stock's whole life-cycle. Secondly. six major submodules of the SMS, based on the EI and sensory devices, are proposed. They are individual stock management, disease management, health management, feeding management, milking management, and a propagation management submodule. Finally, a prototype system for the SMS is demonstrated. The system is developed using Delphi 2 client-server system run under the Windows 95 environment.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.10a
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• pp.25-28
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• 2000

In this paper, the technology for accelerating degradation & life estimation on the traction motor was introduced with the stator form-winding sample coils of the 200 Class insulation system The accelerative degradation was performed in 10 cycles, which were composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, condition diagnosis test such as insulation resistance & polarization index, capacitance & dielectric loss and partial discharge properties were investigated in the temperature range of $20{\sim}160^{\circ}C$. Relationship between degradation conditions and diagnosis results were analyzed to find an dominative degradation factor at the end-life point

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.11a
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• pp.644-647
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• 1999

The main objective of this paper is to design and test a new type of polymer ZnO surge arrester for DC power system of railroad vehicles. The rated voltage is 1500v direct current. The main research works are focused on structure design by finite element method, rat ing vol tape, temporary over vol tape and studies of character int ice of polymeric surge arrester .

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1810-1817
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• 2015

The railway vehicle has an auxiliary power unit for supplying power to the associated electronic control devices and passenger service unit. Typically, input voltage from the catenary for rolling stock is highly fluctuating according to the substation capacity, vehicle propulsion and regeneration. Especially, the frost and freezing on contact wire in winter can cause a blackout inside vehicle, and also brings about electronic components damaging and the system down. To prevent this problem, a large filter and capacitor is used. But this is not a perfect solution, because it is increasing weight of the unit. In this paper, a new algorithm is proposed to suppress the disturbance without adding devices. Simulation and experimental results show that the proposed algorithm has performance to suppress the disturbance at the sudden input voltage variations.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.52-55
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• 1997

In this study, PLZT stock solutions were prepared by sol-gel processing to fabricate PLZT thin films. The stock solutions were spin-coated on ITO-glass and the film were annealed by rapid thermal annealing(RTA). The variation of tile crystallographic structure of the thin films and the phase transition with respect to it were observed using Raman spectra. Raman result showed that the band of spectra are broad as the amount of Zr substitution increased and specially, abrupt change occurs in the raman spectra upon crossing the tetragonal-rhombohedral phase boundry at 2/55/45 PLZT thin film. So, the fact that the crystallographic structure was transitted from tetragonal to rhombohedral structure was certified.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• Journal of Internet Computing and Services
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• v.2 no.3
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• pp.93-98
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• 2001

It is difficult to develop Electronic Commerce System due to expansion, maintenance and repair of the system. In this paper, the author proposes 3-Tier structure Stock Market Management System using JAVA and CORBA. The event service of CORBA supports the interactive environment. For improvement of expansion, performance, security, maintenance, repair. and efficiency, the 3-Tier structure Stock Market Management System is implemented using CORBA and JDBC middle ware in this environment.