• 대한수학회지
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• 제38권3호
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• pp.657-667
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• 2001

We propose a statistical interpolation approximate solution for a nonlinear stochastic integral equation of a stock price process. The proposed method has the order O(h$^2$) of local error under the weaker conditions of $\mu$ and $\sigma$ than those of Milstein' scheme.

• 한국해양공학회지
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• 제5권2호
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• pp.191-191
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• 1991

• ETRI Journal
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• 제5권2호
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• pp.17-28
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• 1983

This paper deals with the problem of optimally controlling traffic flows in urban transportation traffic networks. For this, a nonlinear discrete-time model of urban traffic network is first suggested in order to handle the phenomenon of traffic flows such as oversaturatedness and/or undersaturatedness. Then an optimal control problem is formulated and a hierarchical optimization technique is applied, which is based upon a prediction-type two-level method of Hirvonen and Hakkala.

• 대한조선학회논문집
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• 제44권1호
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• pp.1-7
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• 2007

Recently, researches to find rational mathematical model for prediction of capsizing have been progressed by ITTC. Lee(1997) developed a mathematical model which describes 6 DOF transient motions, such as capsizing, of a ship in regular waves. In this study a mathematical model for prediction of capsizing in following and quartering seas is developed based on Lee's model. And factors affecting prediction of capsizing are analyzed through comparing simulation results with experimental results. Present simulation results are compared with ITTC bench mark test results. In rolling tests with beam seas and tree runs with stern quartering seas, capsizing events are predicted well. But calculated roll angle is larger than experimental one. It is found that nonlinear manoeuvring coefficients don't affect the prediction of capsizing events.

• KEPCO Journal on Electric Power and Energy
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• 제5권3호
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• pp.201-207
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• 2019

An anchorage system is essential for most reinforced concrete structures to connect building components. Therefore, the prediction of strength of the anchor is very important issue for safety of the structures themselves as well as structural components. The prediction models in existing design codes are, however, not applicable for large anchors because they are based on the small size anchors with diameters under 50 mm. In this paper, new prediction models for strength of a single anchor, especially the tensile strength of a single anchor, is developed from the experimental results with consideration of size effect. Size effect in the existing models such as ACI or CCD method is based on the linear fracture mechanics which is very conservative way to consider the size effect. Therefore, new models are developed based on the nonlinear fracture mechanics rather than the linear fracture mechanics for more reasonable prediction. New models are proposed by the regression analysis of the experimental results and it can predict the tensile strength of both small and large anchors.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.532-535
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• 2004

This paper presents a nonlinear finite element analysis procedure for the prediction of shear strength of reinforced concrete deep beams. A computer program, named RCAHESTC(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile. compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The proposed numerical method for the prediction of shear strength of reinforced concrete deep beams is verified by comparison with the reliable experimental results.

• ETRI Journal
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• 제33권1호
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• pp.27-31
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• 2011

This paper addresses estimating the frequency of a cisoid in the presence of white Gaussian noise, which has numerous applications in communications, radar, sonar, and instrumentation and measurement. Due to the nonlinear nature of the frequency estimation problem, there is threshold effect, that is, large error estimates or outliers will occur at sufficiently low signal-to-noise ratio (SNR) conditions. Utilizing the ideas of averaging to increase SNR and weighted linear prediction, an optimal frequency estimator with smaller threshold SNR is developed. Computer simulations are included to compare its mean square error performance with that of the maximum likelihood (ML) estimator, improved weighted phase averager, generalized weighted linear predictor, and single weighted sample correlator as well as Cramer-Rao lower bound. In particular, with smaller computational requirement, the proposed estimator can achieve the same threshold and estimation performance of the ML method.

• 한국군사과학기술학회지
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• 제16권5호
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.