• Advances in nano research
• /
• v.3 no.3
• /
• pp.143-168
• /
• 2015

Initiation of crack and its growth simulation requires accurate model of traction - separation law. Accurate modeling of traction-separation law remains always a great challenge. Atomistic simulations based prediction has great potential in arriving at accurate traction-separation law. The present paper is aimed at establishing a method to address the above problem. A method for traction-separation law prediction via utilizing atomistic simulations data has been proposed. In this direction, firstly, a simpler approach of common neighbor analysis (CNA) for the prediction of crack growth has been proposed and results have been compared with previously used approach of threshold potential energy. Next, a scheme for prediction of crack speed has been demonstrated based on the stable crack growth criteria. Also, an algorithm has been proposed that utilizes a variable relaxation time period for the computation of crack growth, accurate stress behavior, and traction-separation atomistic law. An understanding has been established for the generation of smoother traction-separation law (including the effect of free surface) from a huge amount of raw atomistic data. A new curve fit has also been proposed for predicting traction-separation data generated from the molecular dynamics simulations. The proposed traction-separation law has also been compared with the polynomial and exponential model used earlier for the prediction of traction-separation law for the bulk materials.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.183-183
• /
• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Atmosphere
• /
• v.30 no.2
• /
• pp.115-124
• /
• 2020

The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.2
• /
• pp.35-44
• /
• 2022

In this paper, we propose an intra-picture prediction method by a quadratic surface modeling method for depth video coding. The pixels of depth video are transformed to 3D coordinates using distance information. A quadratic surface with the smallest error is found by least square method for reference pixels. The reference pixel can be either the upper pixels or the left pixels. In the intra prediction using the quadratic surface, two predcition values are computed for one pixel. Two errors are computed as the square sums of differences between each prediction values and the pixel values of the reference pixels. The pixel sof the block are predicted by the reference pixels and prediction method that they have the lowest error. Comparing with the-state-of-art video coding method, simulation results show that the distortion and the bit rate are improved by up to 5.16% and 5.12%, respectively.

• YAKHAK HOEJI
• /
• v.59 no.1
• /
• pp.29-39
• /
• 2015

In recent years, physiologically based pharmacokinetic (PBPK) modeling has been widely used in pharmaceutical industries as well as regulatory health authorities for drug discovery and development. Several application areas of PBPK have been introduced so far including drug-drug interaction prediction, transporter-mediated interaction prediction, and pediatric PK prediction. The purpose of this review is to introduce PBPK and illustrates one of its application areas, particularly pediatric PK prediction by utilizing existing adult PK data and in vitro data. The evaluation of the initial PBPK for adult was done by comparing with experimental PK profiles and the scaling from adult to pediatric was conducted using age-related changes in size such as tissue compartments, and protein binding etc. Sotalol and lorazepam were selected in this review as model drugs for this purpose and were re-evaluated using the PBPK models by GastroPlus$^{(R)}$. The challenges and strategies of PBPK models using adult PK data as well as appropriate in vitro assay data for extrapolating pediatric PK at various ages were also discussed in this paper.

• Tunnel and Underground Space
• /
• v.17 no.6
• /
• pp.484-492
• /
• 2007

The blasting vibration prediction in the country is mainly carried out by using the scaled distance method. But, this method needs a real-scale test blasting. The blasting vibration prediction has been performed using the data measured at borehole blasting for the purpose of a geological investigation before beginning a construction of a tunnel. In this prediction method, it is difficult to reflect the propagation characteristics of ground vibration generated from a real-scale blasting. propagation. This paper presents a new method for estimating blasting vibration by using superposed data of single hole blasting waveform with a delay time.

• Convergence Security Journal
• /
• v.14 no.4
• /
• pp.19-26
• /
• 2014

Mobile malicious code is typically spread by the worm, and although modeling techniques to analyze the dispersion characteristics of the worms have been proposed, only macroscopic analysis was possible while there are limitations in predicting on certain viruses and malicious code. In this paper, prediction methods have been proposed which was based on Markov chain and is able to predict the occurrence of future malicious code by utilizing the past malicious code data. The average value of the malicious code to be applied to the prediction model of Markov chain model was applied by classifying into three categories of the total average, the last year average, and the recent average (6 months), and it was verified that malicious code prediction possibility could be increased by comparing the predicted values obtained through applying, and applying the recent average (6 months).

• Journal of the Korea Society for Simulation
• /
• v.14 no.3
• /
• pp.91-100
• /
• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.191-194
• /
• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• International Journal of Highway Engineering
• /
• v.10 no.3
• /
• pp.199-208
• /
• 2008

Prediction the performance of pavement provides proper information to an agency on decision-making process; especially evaluating the pavement performance and prioritizing the work plan. To date, there are a number of approaches to predict the future deterioration of pavements. However, there are some limitation to proper prediction of the pavement service life. In this paper, pavement performance model and pavement condition prediction model are developed in order to improve pavement condition prediction method. The prediction model of pavement condition through the regression analysis of real pavement condition is based on the probability distribution of pavement condition, which set to 5%, 15%, 25% and 50%, by condition of the pavement and traffic volume. The pavement prediction model presented from the behavior of individual pavement condition which are set to 5%, 15%, 25% and 50% of probability distribution. The performance of the prediction model is evaluated from analyzing the average, standard deviation of HPCI, and the percentage of HPCI which is lower than 3.0 of comparable section. In this paper, we will suggest the more rational method to determine the future pavement conditions, including the probabilistic duration and deterministic modeling methods regarding the impact of traffic volume, age, and the type of the pavement.