• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3612-3624
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• 2021

At present, in the case of pump fast optimization, there is a problem of rapid, accurate and effective prediction of cavitation performance. In "A Cavitation Performance Prediction Method for Pumps PART1-Proposal and Feasibility" [1], a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments of a mixed flow pump. However, whether this method is applicable to vane pumps with different specific speeds and whether the prediction results of this method are accurate is still worthy of further study. Combined with the experimental results, the research evaluates the sensitivity and accuracy at different flow rates. For a certain operating condition, the method has better sensitivity to different flow rates. This is suitable for multi-parameter multi-objective optimization of pump impeller. For the test mixed flow pump, the method is more accurate when the area ratios are 13.718% and 13.826%. The cavitation vortex flow is obtained through high-speed camera, and the correlation between cavitation flow structure and cavitation performance is established to provide more scientific support for cavitation performance prediction. The method is not only suitable for cavitation performance prediction of the mixed flow pump, but also can be expanded to cavitation performance prediction of blade type hydraulic machinery, which will solve the problem of rapid prediction of hydraulic machinery cavitation performance.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.3
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• pp.28-34
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• 2015

An analysis method of trajectory prediction has been suggested and the developed trajectory prediction module, which is an important functional component of the Arrival Manager (AMAN) of Jeju airport, has been tested by applying the suggested method. The objective of this method is to improve prediction performance of the trajectory prediction module. The trajectory prediction module predicts the trajectories based on the real-time track data and flight plans. Therefore, the suggested analysis method includes the simulation framework which is based on real-time playback, recording, and graphic display systems for testing. Besides, the definition of time error, which is a important index for the time based scheduling system, such as AMAN, is included in the suggested analysis method. An example of arrival time prediction accuracy improvement through the suggested analysis method has also been presented.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.93-102
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• 2009

A prediction of streamflow based on multi-objective function is presented to check the performance of Probability Distributed Model(PDM) in Miho stream basin, Chungcheongbuk-do, Korea. PDM is a lumped conceptual rainfall runoff model which has been widely used for flood prevention activities in UK Environmental Agency. The Monte Carlo Analysis Toolkit(MCAT) is a numerical analysis tools based on population sampling, which allows evaluation of performance, identifiability, regional sensitivity and etc. PDM is calibrated for five model parameters by using MCAT. The results show that the performance of model parameters(cmax and k(q)) indicates high identifiability and the others obtain equifinality. In addition, the multi-objective function is applied to PDM for seeking suitable model parameters. The solution of the multi-objective function consists of the Pareto solution accounting to various trade-offs between the different objective functions considering properties of hydrograph. The result indicated the performance of model and simulated hydrograph are acceptable in terms on Nash Sutcliffe Effciency*(=0.035), FSB(=0.161), and FDBH(=0.809) to calibration periods, validation periods as well.

• Journal of Korea Foundry Society
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• v.33 no.2
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• pp.63-68
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• 2013

Generally, metal is the most important material used in many engineering applications. Therefore, it is important to understand and predict the damage of metal as result of the impact. The objective of this research is to evaluate the damage criterion on the impact performance of A356 Al-alloy castings. Both experimental method and computational analysis were used to achieve the research objective. In this paper, we performed impact test according to various impact velocities to the A356 cast aluminium specimen for damage prediction. Impact computational simulation was done by applying properties obtained from the tensile test, and damages was predicted according to the damage criteria based plastic work. The good agreement of the results between the experiment and computer simulation shows that the reliability of the proposed FE simulation method to predict fracture of A356 casting components by impact.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.291-297
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• 2006

The BVI(blade vortex interaction) noise Prediction has been one of the most challenging acoustic analyses in helicopter aeromechanical Phenomenon. It is well known high resolution airloads data with accurate tip vortex positions are necessary for the accurate prediction of this phenomenon. The truly unsteady time-marching free-wake method, which is able to capture the tip vortices instability in hover and axial flights, is expanded with the rotor flapping motion and trim routine to predict unsteady airloads in forward and descent flights. And Farassat formulation 1-A based on the FW-H equation is applied for the noise prediction considering the blade flapping motion. Main objective of this study is to validate the newly developed prediction code. To achieve the objective, the descent flight condition of AH-1 OLS(operational loads survey) configuration is analyzed using present code. The predicted sectional thrust distribution and sectional airloads time histories show the present scheme is able to capture well the unsteady airloads caused by a parallel BVI. Finally, the predicted noise data, observed in two different positions where are 3.44 times of rotor radius far from the hub center, are quite reasonable agreements with the experimental data compared to the other analysis results.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• Earthquakes and Structures
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• v.16 no.6
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• pp.679-690
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• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.545-550
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• 2007

The objective of this paper is to suggest a characteristics of Noise and the Noise Prediction Model and the appropriate Noise Impact Mitigation Method for a elevated railway bridges construction. The characteristics on noises are investigated and evaluated according to a type of railway bridges such as steel, concrete and steel/concrete compound bridges, a types of train, a distance and height from railways. The noise prediction study has been made by the evaluation of differences between model values and in-situ measurement, around the railways. For the noise prediction, the Mithra program and the electronic properties of noises have been adopted.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1668-1677
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• 1993

The objective of this paper is to develop a computational model for predicting the fatigue propagation of collinear multiple surface cracks under constant amplitude and variable amplitude loadings. After examining fatigue crack growth behavior for CT specimens and single surface crack specimens, empirical equations of(11) and(12) are proposed for the prediction of fatigue life in a multiple surface crack geometry. The accuracy of the proposed model is verified using a life prediction computer program. Several case studies were performed to check the accuracy of the proposed model and to verify the usefulness of the developed program. Good agreement is observed between the numerical results based on the proposed model and the published experimental data.