• Atmosphere
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• v.29 no.1
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• pp.21-39
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• 2019

The conventional observations of the Korea Meteorological Administration (KMA) and National Centers for Environmental Prediction (NCEP) are compared in the numerical weather forecast system at the Korea Institute of Atmospheric Prediction Systems (KIAPS). The weather forecasting system used in this study is consists of Korea Integrated Model (KIM) as a global numerical weather prediction model, three-dimensional variational method as a data assimilation system, and KIAPS Package for Observation Processing (KPOP) as an observation pre-processing system. As a result, the forecast performance of NCEP observation was better while the number of observation is similar to the KMA observation. In addition, the sensitivity of forecast performance was investigated for each SONDE, SURFACE and AIRCRAFT observations. The differences in AIRCRAFT observation were not sensitive to forecast, but the use of NCEP SONDE and SURFACE observations have shown better forecast performance. It is found that the NCEP observations have more wind observations of the SONDE in the upper atmosphere and more surface pressure observations of the SURFACE in the ocean. The results suggest that evenly distributed observations can lead to improved forecast performance.

• Tribology and Lubricants
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• v.36 no.5
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• pp.262-266
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• 2020

Herein, we present a numerical investigation of wear analysis of sliding systems with a constant speed subjected to Archard's wear law. For this investigation, we compared two methods: eigen-wear analysis and adaptive meshing technique. The eigen-wear analysis is advantageous to predict the evolution of contact pressure due to wear using the initial contact pressure and contact stiffness. The adaptive meshing technique in finite element analysis is employed to obtain transient wear behavior, which needs significant computational resources. From the eigen-wear analysis, we can determine the appropriate element size required for finite element analysis and the time increment required for wear evolution by a dimensionless variable above a certain value. Since the prediction of wear depends on the maximum contact pressure, the finite element model should have a reasonable representation of the maximum contact pressure. The maximum contact pressure and wear amount according to this dimensionless variable shows that the number of fine meshes in the contact area contributes more to the accuracy of the wear analysis, and the time increment is less sensitive when the number of contact nodes is significantly larger. The results derived from a two-dimensional wear model can be applied to a three-dimensional wear model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.217-230
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• 2022

The adequate control of TBM face pressure is of vital importance to maintain face stability by preventing face collapse and surface settlement. An EPB shield TBM excavates the ground by applying face pressure with the excavated soil in the pressure chamber. One of the challenges during the EPB shield TBM operation is the control of face pressure due to difficulty in managing the excavated soil. In this study, the face pressure of an EPB shield TBM was predicted using the geological and operational data acquired from a domestic TBM tunnel site. Four machine learning algorithms: KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), and XGB (eXtreme Gradient Boosting) were applied to predict the face pressure. The model comparison results showed that the RF model yielded the lowest RMSE (Root Mean Square Error) value of 7.35 kPa. Therefore, the RF model was selected as the optimal machine learning algorithm. In addition, the feature importance of the RF model was analyzed to evaluate appropriately the influence of each feature on the face pressure. The water pressure indicated the highest influence, and the importance of the geological conditions was higher in general than that of the operation features in the considered site.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1209-1223
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• 2022

Blood pressure is one of the key physiological parameters for determining human health, and can prove whether human cardiovascular function is healthy or not. In general, what we call blood pressure refers to arterial blood pressure. Blood pressure fluctuates greatly and, due to the influence of various factors, even varies with each heartbeat. Therefore, achievement of continuous blood pressure measurement is particularly important for more accurate diagnosis. It is difficult to achieve long-term continuous blood pressure monitoring with traditional measurement methods due to the continuous wear of measuring instruments. On the other hand, radar technology is not easily affected by environmental factors and is capable of strong penetration. In this study, by using machine learning, tried to develop a linear blood pressure prediction model using data from a public database. The radar sensor evaluates the measured object, obtains the pulse waveform data, calculates the pulse transmission time, and obtains the blood pressure data through linear model regression analysis. Confirm its availability to facilitate follow-up research, such as integrating other sensors, collecting temperature, heartbeat, respiratory pulse and other data, and seeking medical treatment in time in case of abnormalities.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.2
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• pp.91-96
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• 2014

An acoustical model for detecting the leak location in a buried gas pipeline has been developed. This model is divided into an experimental model for sound diagnosis, and a theoretical model for sound prediction, which is based on the transfer matrix method, representing the sound pressure and the volume velocity as state variables. The power spectrum is measured by attaching only one microphone to the closed end pipe. It has been shown that the response magnitude of acoustic pressure signals calculated by the acoustical model depends upon the thickness and diameter of a pinhole. The validity for the acoustical model has been verified through a comparison between the measured and calculated results.

• Journal of computational fluids engineering
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• v.15 no.3
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• pp.24-31
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• 2010

boiling flow in vertical tube. The multiphase flow model used in this CFD analysis is the two-fluid model in which liquid(water) and gas(vapour) are considered as continuous and dispersed fluids, respectively. A wall boiling model is also used to simulate the subcooled boiling heat transfer at the heated wall boundary. The diameter and heated length of tube are 0.0154 m and 2 m, respectively. The system pressure in tube is 4.5 MPa and the inlet subcooling is 60 K. The near-wall grid size in the non-dimensional wall unit for lqiuid phase ($y^+_{w,l}$) was examined from 101 to 313 at the outlet boundary. The CFD calculations predicted the void distributions as well as the liquid and wall temperatures in tube. The predicted axial variations of the void fraction and the wall temperature are compared with the measured ones. The CFD prediction of the wall temperature is shown to slightly depend on the near-wall grid size but the axial void prediction has somewhat large dependency. The CFD prediction was found to show a better agreement with the measured one for the large near-wall grid, e.g., $y^+_{w,l}$ > 300 at the tube exit.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.140-147
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• 2003

There are many problems in the prediction of soil dynamic behaviors because undrained excess pore water pressure builds up and then the strain softening behavior is occurred simultaneously. A few analytical methods based on the dynamic constitutive model have been proposed but the model hardly predict the excess pore water pressure directly. In this study, the verification on the disturbed state concept (DSC) model, proposed by Dr, Desai was performed. Some laboratory tests such as conventional triaxial tests and cyclic triaxial tests were carried out to determine DSC Parameters and then disturbance values are determined by the proposed equation. Through this verification, it is proved that the disturbed state concept can express reliably the soil dynamic characteristics such as excess pore water pressure and strain softening behavior. It is also found that the critical disturbance which is determined at the minimum curvature of disturbance function can be a the specific index.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4134-4145
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• 2023

This paper proposes strain-based failure model of A533B1 pressure vessel steel to simulate failure, followed by application to OECD lower head failure (OLHF) test simulation for experimental validation. The proposed strain-based failure model uses simple constant and linear functions based on physical failure modes with the critical strain value determined either using the lower bound of true fracture strain or using the average value of total elongation depending on the temperature. Application to OECD Lower Head Failure (OLHF) tests shows that progressive deformation, failure time and failure location can be well predicted.