• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2202-2211
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• 2010

The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.2
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• pp.245-256
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• 2016

Prediction of TBM performance is very important for machine selection, and for reliable estimation of construction cost and period. The purpose of this research is to analyze the evaluation process of various prediction models for TBM performance and applied methodology. Based on the solid literature review since 2000, a classification system of TBM performance prediction model is proposed in this study. Classification system suggested in this study can be divided into two stages: selection of input parameter and application of prediction techniques. We also analyzed input and output parameters for prediction model and frequency of use. Lastly, the future research and development trend of TBM performance prediction is suggested.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.167-179
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• 2020

Recently, the increasing importance of artificial intelligence (AI) technology has led to its increased use in various fields in the shipbuilding and marine industries. For example, typical scenarios for AI include production management, analyses of ships on a voyage, and motion prediction. Therefore, this study was conducted to predict a response amplitude operator (RAO) through AI technology. It used a neural network based on one of the types of AI methods. The data used in the neural network consisted of the properties of the vessel and RAO values, based on simulating the in-house code. The learning model consisted of an input layer, hidden layer, and output layer. The input layer comprised eight neurons, the hidden layer comprised the variables, and the output layer comprised 20 neurons. The RAO predicted with the neural network and an RAO created with the in-house code were compared. The accuracy was assessed and reviewed based on the root mean square error (RMSE), standard deviation (SD), random number change, correlation coefficient, and scatter plot. Finally, the optimal model was selected, and the conclusion was drawn. The ultimate goals of this study were to reduce the difficulty in the modeling work required to obtain the RAO, to reduce the difficulty in using commercial tools, and to enable an assessment of the stability of medium/small vessels in waves.

• Atmosphere
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• v.24 no.4
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Journal of the Korean Geotechnical Society
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• v.30 no.11
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• pp.17-23
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• 2014

In this study, the prognostic model for the prediction of the road surface temperature is developed using the surface energy balance theory. This model not only has a detailed micro meteorological physical attribute but also is able to accurately represent each surface energy budget. To verify the performance, the developed model output was compared with the German Weather Service (DWD)'s Energy Balance Model (EBM) output, which is based on the energy budget balance theory, and the observations. The simulated results by using both models are very similar to each other and are compatible with the observed data.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.899-903
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• 1994

This paper describes two methods to assess the output speech, quality of vocoders for telephone conferencing in digital mobile communication networks. The proposed methods are the sentence discrimiantion method and the modified degraded mean opinion score (MDMOS) test. We apply these two methods to Qualcomm code excited linear prediction (QCELP), vector sum excited linear prediction (VSELP) and regular pulse excited-long term predictin (RPE-LTD) voceders to evaluate which vocoding algorithm can process mixed voice signal from two speakers better for telephone conferencing. From the experiments we obtain that the VSELP vocoding algorithm reveals superior output speech quality to the other two.

• Journal of IKEEE
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• v.24 no.4
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• pp.1176-1179
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• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.543-543
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• 2010

The objective of this research is estimating the location of interested sound source by using the similarity between a beamformer output in time domain and the candidate signal. The waveform of beamformer output at the location of sound source is similar with the waveform emitted by that source. To estimate the location of sound source by using this feature, we define quantified similarity between candidate signal and beamformer output. The candidate signal describes the signal which is generated by interested source. In this paper, similarity is defined by four methods. The two methods use time vector comparison, and the other two methods use time-frequency map or linear prediction coefficients. To figure out the results and performance of localization by using similarities, we demonstrate two conditions. The one is when two pure tone sources exist and the other condition is when several bird sounds exist. As a consequence, inner product with two time-vectors and structural similarity with spectrograms can estimate the locations of interest sound source.

• Journal of Power Electronics
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• v.13 no.2
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• pp.197-205
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• 2013

In energy injection resonant AC-AC converters, due to the low frequency effect of the AC input envelope and the low energy injection losses requirement, the constant and steady control of the high frequency AC output envelope is still a problem that has not been solved very well. With the aid of system modeling, this paper analyzes the mechanism of the envelope pit on the resonant AC current. The computing methods for the critical damping point, the falling time and the bottom value of the envelope pit are presented as well. Furthermore, this paper concludes the stability precondition of the system AC output. Accordingly, an optimal control method for the AC output envelope is put forward based on the envelope prediction model. This control method can predict system responses dynamically under different series of control decisions. In addition, this control method can select best series of control decisions to make the AC output envelope stable and constant. Simulation and experimental results for a contactless power transfer system verify the control method.