• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.52-55
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• 2000

Investigated via a series of finite element process simulation is the effect of diverse process variables on some selected non-dimensional parameters characterizing the strip in hot strip rolling. Then on the basis of these parameters an on-line model is derived for the precise prediction of roll and roll power. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model.

• Journal of Power Electronics
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• v.11 no.1
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• pp.59-63
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• 2011

This paper presents the modeling of the sampling effect in the p-type average current mode control. The prediction of the high frequency components near half of the switching frequency in the current loop gain is given for the p-type average current mode control. By the proposed model, the prediction accuracy is improved when compared to that of conventional models. The proposed method is applied to a buck converter, and then the measurement results are analyzed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.534-537
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• 2011

This Paper present prediction Vehicle Interior Noise using ATF(Acoustic Transfer Function) and engine radiated sound power. This is useful tool to qualifying the effectiveness of Air-borne noise Path. Furthermore This method provide acoustic package performance of the vehicle and able to prepare frequency band to same segment or benchmarking vehicle.

• Solar Energy
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• v.20 no.2
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• pp.67-73
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• 2000

This study represents the methodology of performance prediction for small hydro power(SHP) sites. Nine tunnel type SHP sites with diversion dam were selected and the performance characteristics were analyzed by using a developed model. Also, primary design specifications such as design flowrate, plant capacity, and operational rate were suggested and feasibility for tunnel-type SHP sites were estimated. It was found that the design flowrate was most important parameter to exploit SHP plant and the methodology developed in this study was useful tool to analyze the performance of SHP sites.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• Journal of Power Electronics
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• v.6 no.2
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• pp.104-113
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• 2006

Satellites have provided the impetus for the orderly development of reliability engineering research and analysis because they tend to have complex systems and hence acute problems. They were instrumental in developing mathematical models for reliability, as well as design techniques to permit quantitative specification, prediction and measurement of reliability. Reliability engineering is based on implementing measures which insure an item will perform its mission successfully. The discipline of reliability engineering consists of two fundamental aspects; $(1^{st})$ paying attention to details, and $(2^{nd})$ handling uncertainties. This paper uses some of the basic concepts, formulas and examples of reliability theory in application. This paper emphasizes the practical reliability analysis of a Low Earth Orbit (LEO) Micro-satellite power subsystem. Approaches for specifying and allocating the reliability of each element of the power system so as to meet the overall power system reliability requirements, as well as to give detailed modeling and predicting of equipment/system reliability are introduced. The results are handled and analyzed to form the final reliability results for the satellite power system. The results show that the Electric Power Subsystem (EPS) reliability meets the requirements with quad microcontrollers (MC), two boards working as main and cold redundant while each board contains two MCs in a hot redundant.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1069-1076
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• 2002

The high temperature creep behavior of heat machine systems such as aircraft engines, boilers and turbines in power plants and nuclear reactor components have been considered as an important and needful fact. There are considerable research results available for the design of high temperature tube materials in power plants. However, few studies on the Initial Strain Method (ISM) capable of securing repair, maintenance, cost loss and life loss have been made. In this method, 3 long time prediction Of high temperature creep characteristics can be dramatically induced through a short time experiment. The purpose of present study is to investigate the high temperature creep lift of Udimet 720, SCM 440-STD61 and 1Cr-0.5Mo steel using the ISM. The creep test was performed at 40$0^{\circ}C$ to $700^{\circ}C$ under a pure loading. In the prediction of creep life for each materials, the equation of ISM was superior of Larson-Miller Parameter(LMP). Especially, the long time prediction of creep life was identified to improve the reliability.

• Computers and Concrete
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• v.5 no.3
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.