• Journal of Ocean Engineering and Technology
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• v.36 no.4
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• pp.243-250
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• 2022

Subsea power cables are subjected to various external loads induced by environmental and mechanical factors during manufacturing, shipping, and installation. Therefore, the prediction of the structural strength is essential. In this study, experimental and theoretical analyses were performed to investigate the axial stiffness of subsea power cables. A uniaxial tensile test of a 6.5 m three-core AC inter-array subsea power cable was carried out using a 10 MN hydraulic actuator. In addition, the resultant force was measured as a function of displacement. The theoretical model proposed by Witz and Tan (1992) was used to numerically predict the axial stiffness of the specimen. The Newton-Raphson method was employed to solve the governing equation in the theoretical analysis. A comparison of the experimental and theoretical results for axial stiffness revealed satisfactory agreement. In addition, the predicted axial stiffness was linear notwithstanding the nonlinear geometry of the subsea power cable or the nonlinearity of the governing equation. The feasibility of both experimental and theoretical framework for predicting the axial stiffness of subsea power cables was validated. Nevertheless, the need for further numerical study using the finite element method to validate the framework is acknowledged.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.184.1-184.1
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• 2010

In order to research the way to evaluate wind resource without actual Met Mast data, this paper has been carried out on the southeastern region of Jeju island, Korea. Although wind turbine has been an economical alternative energy resource, misjudging the prediction of lifetime or payback period occurs because of the inaccurate assessment of wind resource and the location of wind turbine. Using WindPRO(Ver. 2.7), a software for wind farm design developed by EMD from Denmark, wind resources for the southeastern region of Jeju island was analyzed, and the performance of WindPRO prediction was evaluated in detail. Met Mast data in Su-san 5.5Km far from Samdal wind farm, AWS in Sung-san 4.5km far from Samdal wind farm, and Korea Wind Map data had been collected for this work.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.864-865
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• 2011

Battery Disconnecting System (BDS) is the important equipment in electric vehicle system. Therefore, most of electric vehicle companies, i.e. Hyundai Motors, Renault Motors, General Motors, want to have the reliability of 15 years - 150, 000 miles. Recently, reliability prediction through Siemens Norm SN 29500 is considered without testing. In this paper, we will introduce the standard and various input parameters. Also the case study will be shown for BDS. Prediction model is constructed by listing all the components of BDS. It calculates the $\pi$ factors for each components using the conversion equation in the standard and converts the reference failure rates to the expected operating failure rates. According to the result, the parts which will be improved are EV-Relays.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.29-41
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• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Journal of Environmental Impact Assessment
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• v.16 no.1
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• pp.99-105
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• 2007

Since 1990s, the railway noise has been researched and developed in our nation. First of all, what's causing the noise and how to eliminate the cause of the noise must be found out. Secondly, cutting off the propagation path of the noise from the noise source to the receiving points. In this study the characteristics of prediction formula for the assessment of railway noise used in some nations including Korea were investigated. In order to develop the prediction formula of the railway noise, the noise radiated from railway vehicle, rails and sleepers, characteristics of noise barrier, velocity of train, ground effects, roughness should be analyzed and predicted. Especially, on the basis of acoustics, the characteristics of source are applied to acoustic power and directivity information.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.1-7
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• 2008

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss its methodological characteristics in comparison with other existing classification methods. Also, to assess the prediction power of the model, we conduct a series of experiments employing survey data of consumer choices of MP3 players. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• International Journal of Fluid Machinery and Systems
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• v.8 no.2
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• pp.63-72
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• 2015

Water hammer phenomena are important issues for the design and the operation of hydro power plants. Especially, if several reversible pump-turbines are coupled hydraulically there may be strong unit interactions. The precise prediction of all relevant transients is challenging. Regarding a recent pump-storage project, dynamic measurements motivate an improved turbine modeling approach making use of a Thoma number dependency. The proposed method is validated for several transient scenarios and turns out to improve correlation between measurement and simulation results significantly. Starting from simple scenarios, this allows better prediction of more complex transients. By applying a fully automated simulation procedure broad operating ranges of the highly nonlinear system can be covered providing a consistent insight into the plant dynamics. This finally allows the optimization of the closing strategy and hence the overall power plant performance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.2
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• pp.276-287
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• 1990

In this paper research result of transient thermal stress analysis of power plant turbine rotors for life prediction under severs operating conditions is presented. Galerkin's recurrence scheme is used for numerical solution of discretized FEM equation of transient heat conduction equation. Boundary conditions for the equation and operating conditions are intensively investigated for accurate life prediction of turbine rotors in operation. A computer program for on-site application is developed and tested. Distribution of thermal stress in turbine rotors during various operating condition is analyzed with the program and it is found that the peak thermal stress appears during cold stage conditions at the first stage of high pressure rotors.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.157-160
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• 1999

A prediction method of conditional event matching pre-diction (EMP) for a purpose of predicting nonlinear phenomena of insulator pollution was proposed in this paper. The EMP was used if the conditional probability for increase of insulator pollution exceeded a threshold value. A performance of the EMP was strongly related to selection of database of events and a closeness function. By use of the prediction of the insulator pollution based on the conditional EMP, reliable decision making for the washing timing of the polluted insulators was e-valuated based on actual data in Kasatsu substation, Japan.

• Health Policy and Management
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• v.12 no.3
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.