• Proceedings of the Korean Geotechical Society Conference
• /
• 2005.03a
• /
• pp.185-190
• /
• 2005

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. In order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The six landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The six factors consist of two topographic factors and four geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 86.5% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Proceedings of the KSME Conference
• /
• 2001.11b
• /
• pp.288-293
• /
• 2001

A critical heat flux (CHF) prediction method using an artificial neural network (ANN) was evaluated for application to the high-heat-flux (HHF) subcooled flow boiling. The developed ANN predictions were compared with the experimental database consisting of a total of 3069 CHF data points. Also, the prediction performance by the ANN was compared with those by mechanistic models and a look up table technique. The parameter ranges of the experimental data are: $0.33{\leq}D{\leq}37.5mm$, $0.002{\leq}L{\leq}4m$, $0.37{\leq}G{\leq}134Mg/m^2s$, $0.1{\leq}P{\leq}20MPa$, $50\leq{\Delta}h_{sub,in}\leq1660kJ/kg$, and $1.1{\leq}q_{CHF}\leq276MW/m^2$. $276MW/m^2$. It was found that 91.5% of the total data points were predicted within $a{\pm}20%$ error band, which showed the best prediction performance among the existing CHF prediction methods considered.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.6
• /
• pp.1239-1248
• /
• 2021

In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model's performance through K-fold cross validation.

• 한국전산유체공학회:학술대회논문집
• /
• 2003.10a
• /
• pp.257-257
• /
• 2003

• 한국전산유체공학회:학술대회논문집
• /
• 2006.05a
• /
• pp.161-162
• /
• 2006

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.36 no.3
• /
• pp.363-369
• /
• 2012

The moderator thermal flow in the CANDU calandria is generally complex and highly turbulent because of the interaction of the buoyancy force with the inlet jet inertia. In this study, the prediction performance of turbulence models for the accurate analysis of the moderator thermal flow are assessed by comparing the results calculated with various types of turbulence models in the commercial flow solver FLUENT with experimental data for the test vessel at Sheridan Park Engineering Laboratory (SPEL).Through this comparative study of turbulence models, it is concluded that turbulence models that include the source term to consider the effects of buoyancy on the turbulent flow should be used for the reliable prediction of the moderator thermal flow inside the CANDU calandria.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.260-260
• /
• 2017

This study aims to compare the performance of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability model) and TiVaSS (Time-variant Slope Stability model) in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. The present study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( index), which was developed for addressing point-like landslide locations. In addition, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models have characteristics that are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30% to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.23-34
• /
• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1997.05a
• /
• pp.356-361
• /
• 1997

To investigate parametric effect on CHF and to get CHF data, experimental study has been performed with vertical round tubes under the condition of low pressure and low flow (LPLF). Test sections are made of Inconel-625 tube and have the geometry of 8 and 10 mm in diameter, and 0.5 and 1.0 m in heated length. All experiments have been conducted at the pressure of under 9 bar, the mass flux of under 250 kg/$m^2$ and the inlet subcooling of 350 and 450 kJ/kg, for stable upward flow with water as a coolant. Flow regime analysis has been performed for obtained CHF data with Mishima's flow regime map, which reveals that most of the CHF occur in the annular-mist flow regime. General parametric trends of the collected CHF data are consistent with those of previous studies. However, for the pressure effect on CHF, two different are observed; For relatively high mass flux, CHF increases with pressure and far lower mass flux, CHF decrease with pressure. Using modern data regression tool, ACE algorithm, two new CHF correlations for LPLF condition are developed based on local condition and inlet condition, respectively. The developed CHF correlations show better prediction accuracy compared with existing CHF prediction methods.

• Transactions of Materials Processing
• /
• v.27 no.4
• /
• pp.227-235
• /
• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.