• 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.

• Journal of Power System Engineering
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• v.3 no.1
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• pp.38-44
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• 1999

Subway is one of the most important transportation and its facilities are increased by the drift of population to cities in these days. But heat generation results from lighting, human and traffic increase in subway, half-closed space, gives uncomfortable sense to the subway passengers. Therefore, natural ventilation by piston effect is done to relieve uncomfortable sense. But train wind by piston effect gives uncomfortable sense to the subway passengers, too. So the numerical calculation of inflow and outflow amounts is important to predict thermal environment and reduce train wind. In case of actual survey of train wind in target station, the amount of train wind are about $3100m^3/train$ at the minimum, about $6000m^3/train$ at the maximum, about $4200m^3/train$ on average. When comparison between simulation for train wind prediction and actual survey for accuracy was done train wind prediction program showed similar results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• The Journal of Information Systems
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• v.33 no.1
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• pp.31-44
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• 2024

Purpose The purpose of this study is to use climate data to find the algorithm with the highest Hallabong production prediction ability and to predict future Hallabong production in areas where Hallabong cultivation is expected to be possible. Design/methodology/approach The research is conducted in two stages. In the first step, find the algorithm with the highest predictive power among XGBoost, Random Forest, SVM, and LSTM methodologies. In the second stage, the algorithm found in the first stage is applied to predict future Hallabong production in three regions where Hallabong production is expected to be possible. Findings As with many prediction studies, we found that XGBoost showed the highest prediction power. Even in areas where Hallabong production is expected to be possible, Hallabong production was predicted to be highest in Hongcheon, Gangwon-do, which has the highest latitude.