• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.45-59
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• 2003

The method in this study, which is more efficiency than the existing method, propose the optimal rehabilitation model based on the deterioration prediction of the laying pipe by using the deterioration survey method of the water distribution system. The deterioration prediction model divides the deterioration degree of each pipe into 5 degree by using the probabilistic neural network. Also, the optimal residual durability is estimated by the calculated deterioration degree in each pipe and pipe diameter. The optimal rehabilitation model by integer programming base on the shortest path can calculate a time and cost of maintenance, rehabilitation, and replacement. Also, the model is divided into budget constraint and no budget constraint. Consequently, the model proposed by the study can be utilized as the quantitative method for the management of the water distribution system.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.1
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• pp.79-83
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• 2010

The conventional welding technique such as shield metal arc welding has been mostly applied to the piping system of the nuclear power plants. It is well known that this welding technique causes the overheating and welding defects due to the large groove angle of weld. On the other hand, the narrow gap welding(NGW) technique has many merits, for instance, the reduction of welding time, the shrinkage of weld and the small deformation of the weld due to the small groove angle and welding bead width comparing with the conventional welds. These characteristics of NGW affect the deformation behavior and the distribution of welding residual stress of NGW, thus it is believed that the residual stress results obtained from conventional welding procedure may not be applied to structural integrity evaluation of NGW. In this paper, the welding residual stress of NGW was predicted using the nonlinear finite element analysis to simulate the thermal and mechanical effects of the NGW. The present results can be used as the important information to perform the flaw evaluation and to improve the weld procedure of NGW.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.426-434
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• 2010

This study is experimentally performed to evaluate the strength reduction behavior and its statistical properties of plain woven glass/epoxy composites. The results indicate that the major impact damage of plain woven glass/epoxy composites is the fiber breakage and matrix crack, whereas the dominant impact damage of unidirectional carbon/epoxy laminates is the delamination, which depends on the stacking sequence. The residual strength prediction models, previously proposed on unidirectional laminates, are applied to evaluate the residual strength of plain woven glass/epoxy composites with impact damage. Among these models, the results by Caprino and Avva's model have a good agreement with the experimental results. To investigate the variability of residual strength of the impacted composite materials, a statistical model was proposed and its results were in conformance with the experimental results regardless of their thickness.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.204-207
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• 2002

The current paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm In the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3D-SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of The Korean Astronomical Society
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• v.50 no.2
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• pp.21-27
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• 2017

The autoregressive method provides a univariate procedure to predict the future sunspot number (SSN) based on past record. The strength of this method lies in the possibility that from past data it yields the SSN in the future as a function of time. On the other hand, its major limitation comes from the intrinsic complexity of solar magnetic activity that may deviate from the linear stationary process assumption that is the basis of the autoregressive model. By analyzing the residual errors produced by the method, we have obtained the following conclusions: (1) the optimal duration of the past time for the forecast is found to be 8.5 years; (2) the standard error increases with prediction horizon and the errors are mostly systematic ones resulting from the incompleteness of the autoregressive model; (3) there is a tendency that the predicted value is underestimated in the activity rising phase, while it is overestimated in the declining phase; (5) the model prediction of a new Solar Cycle is fairly good when it is similar to the previous one, but is bad when the new cycle is much different from the previous one; (6) a reasonably good prediction of a new cycle can be made using the AR model 1.5 years after the start of the cycle. In addition, we predict the next cycle (Solar Cycle 25) will reach the peak in 2024 at the activity level similar to the current cycle.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.3
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• pp.211-216
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• 2013

Most of hull resistance prediction methods which are used to calculate the towing force of disabled ships are very simple and old-fashioned. In particular, in cases of barge ships, a method similar to the US Navy Towing Manual is being used. This paper reviewed the US Navy Towing Manual and the notification method of Korea Ministry of Oceans and Fisheries and proved that these prediction methods are irrational and inaccurate. Furthermore, a new Modified-Yamagata-Barge method is introduced as a more rational and accurate resistance prediction method which can be applied in case of barge ships.

• Advances in materials Research
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• v.8 no.4
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• pp.295-311
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• 2019

In this study, a three-dimensional Finite Element Model has been developed to estimate the size of the weakened zone in a bi-material a ceramic bonded to metal. The calculations results were compared to those obtained using Scanning Electron Microscope (SEM). In the case of elastic-plastic behaviour of the structure, it has been shown that the simulation results are coherent with the experimental findings. This indicates that Finite Element modeling allows an accurate prediction and estimation of the weakening effect of residual stresses on the bonding interface of Alumina. The obtained results show us that the three-dimensional numerical simulation used by the Finite Element Method, allows a good prediction of the weakened zone extent of a ceramic, which is bonded with a metal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.736-739
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• 2009

In this paper, an efficient lossless compression algorithm using motion adaptation is proposed. It is divided into two parts: a motion adaptation based nonlinear predictor part and a residual data coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors using motion adaption. The predictor decides the proper selection of the intra and inter prediction values according to the past prediction error. The reduced error is coded by existing context adaptive coding method. Experimental results show that the proposed algorithm has the higher compression ratio than context modeling methods, such as FELICS, CALIC, and JPEG-LS.