• Proceedings of the SAREK Conference
• /
• 2006.06a
• /
• pp.99-104
• /
• 2006

Accurate flow rate distribution has been become a very important part for controling of air change rate since the introduction of house ventilation system. An inappropriate selection of fan due to Incorrect prediction of friction loss makes waste energy. The purpose of this study is to recognize applicability of T-Method at house ventilation system by comparing experiment with T-method, The result of this study is as follows Flow rate is small amount in a house, so duct size must be accurate. And duct design with Equal Friction Loss Method presented large error range. Equal friction loss method is not fit to applicate small amount air flow rate. T-Method predicts accurate flow rate comparatively in a house ventilation system. Error range was 3.5%.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.29-29
• /
• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• Proceedings of the KWS Conference
• /
• 2002.05a
• /
• pp.146-148
• /
• 2002

The welding distortion can result in problems such as dimensional inaccuracies during assembly and raise concerns on safety during service. Therefore, an accurate prediction and a reduction of the deformation are critical to improving the quality of the weldment. In this study, four cases for reducing welding distortion is proposed and it is evaluated through experiments.

• Journal of the Korean Graphic Arts Communication Society
• /
• v.11 no.1
• /
• pp.57-69
• /
• 1993

Currently, Computer-To-Plate printing system comes into wide use, an accurate color simulation system is demanded. This paper is described some basic operational expressions known in the simulation of colors by use of halftone dots, and is proposed improved color prediction of multicolor halftone, The experimental results show that proposed color predict Eq. is useful and valid in predicting the color reproduction of multicolor halftone.

• Journal of computational fluids engineering
• /
• v.19 no.3
• /
• pp.1-7
• /
• 2014

One of the most important factors in sailing yacht design is an accurate velocity prediction. Velocity prediction programs (VPPs) are widely used to predict velocity of sailing yachts. VPPs, which are primarily based on experimental data and experience of long years, suffer limitations applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using the computational fluid dynamics (CFD) is proposed. Using the developed method, velocity and motion of a 30 feet sloop yacht, which was developed by Korea Research Institute of Ship and Ocean (KRISO) and termed KORDY30, were predicted in upwind sailing condition.

• Environmental Engineering Research
• /
• v.15 no.3
• /
• pp.135-140
• /
• 2010

With the various urban characteristics of each city, the existing water demand prediction, which uses average liter per capita day, cannot be used to achieve an accurate prediction as it fails to consider several variables. Thus, this study considered social and industrial factors of 164 local cities, in addition to population and other directly influential factors, and used main substance and cluster analyses to develop a more efficient water demand prediction model that considers unique localities of each city. After clustering, a multiple regression model was developed that proved that the $R^2$ value of the inclusive multiple regression model was 0.59; whereas, those of Clusters A and B were 0.62 and 0.74, respectively. Thus, the multiple regression model was considered more reasonable and valid than the inclusive multiple regression model. In summary, the water demand prediction model using principal component and cluster analyses as the standards to classify localities has a better modification coefficient than that of the inclusive multiple regression model, which does not consider localities.

• Computers and Concrete
• /
• v.25 no.6
• /
• pp.565-574
• /
• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• Korean Journal of Agricultural Science
• /
• v.43 no.4
• /
• pp.507-521
• /
• 2016

Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

• Computers and Concrete
• /
• v.17 no.2
• /
• pp.173-188
• /
• 2016

Generally, thermal stress induced by hydration heat causes cracking in mass concrete structures, requiring a thorough control during the construction. The prediction of the thermal stress is currently undertaken by means of numerical analysis despite its lack of reliability due to the properties of concrete varying over time. In this paper, a method for the prediction of thermal stress in concrete structures by adjusting thermal stress measured by a thermal stress device according to the degree of restraint is proposed to improve the prediction accuracy. The ratio of stress in concrete structures to stress under complete restraint is used as the degree of restraint. To consider the history of the degree of restraint, incremental stress is predicted by comparing the degree of restraint and the incremental stress obtained by the thermal stress device. Furthermore, the thermal stresses of wall and foundation predicted by the proposed method are compared to those obtained by numerical analysis. The thermal stresses obtained by the proposed method are similar to those obtained by the analysis for structures with internally as well as externally strong restraint. It is therefore concluded that the prediction of thermal stress for concrete structures with various boundary conditions using the proposed method is suggested to be accurate.

• 한국전산유체공학회:학술대회논문집
• /
• 2004.10a
• /
• pp.47-51
• /
• 2004

Accurate Prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Lomax(B-L), Spalart-Allmaras(S-A), $\kappa-\epsilon$, $\kappa-\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control (ins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.