• Journal of Information Technology Services
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• v.10 no.1
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• pp.135-149
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• 2011

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.1199-1204
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• 2006

The pressure drop and heat transfer coefficient were measured at room temperature in CFB heat exchanger with multiple vertical tubes. Also the circulation rate of solid particles was measured. The theoretical model for predicting heat transfer coefficient using the solid flowrate was developed in this study. The model predictions were compared with the measured heat transfer coefficient to show relatively good agreement.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.49-54
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• 2016

The wind power plants were installed in many places because of the low climate changing effects since 2000. Generally, the wind power plants located in the seaside and the mountainous area and the heights of the windmills are about 40 m~140 m above the ground level. So the noises emitted from the wind power plants propagate far away compared with other environment noise sources like trains and cars noise. Because of these reasons, the noise emitted from the wind power plant is easy to cause the additional social problems like as noise complaints. Under the situation, the ministry of environment has established the guideline to evaluate the environmental effects for the wind power plant. According to the guideline, the noise of the wind power plant has to meet 55 dB(A) at daytime and 45 dB(A) at night in the residential area, which is regulated in the noise and vibration management law. But, it is difficult to estimate the noise emitted from the wind power plant because of the absence of the prediction model of the wind power plant noise. Therefore, the noise prediction model for wind power plants using the regression analysis method is developed in this study. For the development of the model, the sound pressure levels of the wind power plants in Jeju island are measured and the correlations between the sound pressure levels are analyzed. Finally, the prediction equation of the wind power plant noise using by regression analysis method derived. The prediction equation for the wind power plant noise proposed in this study can be useful to evaluate the environmental effects in any wind power plant development district.

• Plant Journal
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• v.15 no.2
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• pp.56-61
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• 2019

In this study, the failure analysis of the internal tube occurred in the high pressure feedwater heater for power generation boiler of 500 MW supercritical pressure coal fired power plant was investigated. I suggested a prediction model that can diagnose internal tube failure by changing the position of level control valve on the shell side and the suction flow rate of the boiler feedwater pump. The suggested prediction model is demonstrated through additional cases of feedwater system unbalance. The simultaneous comparison of the shell side level control valve position and the suction flow rate of the boiler feedwater pump compared to the normal operating state value, even in the case of the high pressure feedwater heater for the power boiler, It can be a powerful prediction diagnosis.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.86-96
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• 1998

A numerical study has been carried out of three-dimensional turbulent flows around a MIRA reference vehicle model both with and without wheels in computation. Two convective difference schemes with two k-$\varepsilon$ turbulence models are evaluated for the performance such as drag coefficient, velocity and pressure fields. Pressure coefficients along the surfaces of the model are compared with experimental data. The drag coefficient, the velocity and pressure fields are found to change considerably with the adopted finite difference schemes. Drag forces computed in the various regions of the model indicate that design change decisions should not rely just on the total drag and that local flow structures are important. The results also indicate that the RNG model with the QUICK scheme predicts fairly well the tendency of velocity and pressure fields and gives more reliable drag coefficient rather than the other cases.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.171-186
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• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• The KSFM Journal of Fluid Machinery
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• v.17 no.3
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• pp.46-51
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• 2014

In this study, a numerical analysis methodology is studied to predict thermal and flow characteristics of C3X vane with internal cooling. Effects of turbulence models, transition models and viscous work term on temperature and pressure distributions on the vane surface are investigated. These optional terms have few effects on the pressure distributions over the vane surface. However, they have great influence on prediction of the temperature distributions on the vane surface. The combination of k-${\omega}$ based SST turbulence model, ${\gamma}$ transition model and viscous work term are better than RSM turbulence model on prediction of the surface temperature. The average temperature difference between CFD results and experimental results is calculated 2 % at the pressure side and 1 % at the suction side. Furthermore computing time of this combination is half of the RSM turbulence model. When k-${\omega}$ based SST turbulence model and ${\gamma}$ transition model with viscous work term are applied, more accurate predictions of thermal and internal flow characteristics of high pressure turbine are expected.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.344-348
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• 2007

The acoustic liner has been used for the suppression of noise. The impedance characteristics of the acoustic liner are increased by the incident pressure. For the estimation of the acoustic liner on the incident acoustic pressure effect, the modified impedance model is suggested on the basis of the GE impedance prediction model. The modified impedance model is originated from the 3 parameter impedance model, and extended to the incident pressure parameter. The modified model is applied on the simple duct analysis with variant source pressure. Through the computation, it is observed that the fore directivity patterns of the duct are varied by the incident SPL level.