• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.181-189
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• 1993

Over the past two decades, forecasting has gained widespread acceptance as an integral part of business planning and decision making. Accurate forecasting is a prerequisite to successful planning. Accordingly, recent advances in forecasting techniques are of exceptional value to corporate planners. But most of forecasting mothods are reveal its limit and problem for precision and reliability duing to each relationship for raw data and possibility of explanation for each variable. Therefore, to construct the Integrated Forecasting Model(IFM) for Information Factor Analysis, it shoud be considered that whether law data has time lag and variables are explained. For this. following several method can be used : Least Square Method, Markov Process, Fibonacci series, Auto-Correlation, Cross-Correlation, Serial Correlation and Random Walk Theory. Thus, the unified property of these several functions scales the safety and growth of the system which may be varied time-to-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.63-65
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• 2022

DDI(Drug-Drug Interaction)는 병원에서 발생하는 약물이상반응의 30%를 유발하는 부작용이지만, 현실적으로 모든 약물쌍의 DDI 를 기존 in vivo, in vitro 방식으로 예측하는 것은 불가능하다. 그렇기에, 다양한 in silico 방식의 DDI 예측 모델이 연구되고 있다. 본 연구에서는, 단백질 네트워크 상에서 RWR(Random Walk with Restart) 알고리즘을 통해 약물과 직접적으로 상호작용하는 단백질과 간접적으로 상호작용하는 단백질의 정보를 사용하여 DDI 를 예측하는 모델을 개발하였다. 이 모델을 통하여 기존에 발견하지 못한 DDI 를 새롭게 발견하고, 신약 개발 시에도, 신약과 함께 복용 시 문제를 일으킬 수 있는 약물을 예측하여 약물 이상반응을 방지하고자 한다.

• Korean Journal of Cognitive Science
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• v.33 no.3
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• pp.155-168
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• 2022

In this paper, we introduce the SNUDM-G, a diffusion model analysis tool with improved computational speed. Although the diffusion model has been applied to explain various cognitive tasks, its use was limited due to computational difficulties. In particular, SNUDM(Koh et al., 2020), one of the diffusion model analysis tools, has a disadvantage in terms of processing speed because it sequentially generates 20,000 data when approximating the diffusion process. To overcome this limitation, we propose to use graphic processing units(GPU) in the process of approximating the diffusion process with a random walk process. Since 20,000 data can be generated in parallel using the graphic processing units, the estimation speed can be increased compared to generating data through sequential processing. As a result of analyzing the data of Experiment 1 by Ratcliff et al. (2004) and recovering the parameters with SNUDM-G using GPU and SNUDM using CPU, SNUDM-G estimated slightly higher values for certain parameters than SNUDM. However, in term of computational speed, SNUDM-G estimated the parameters much faster than SNUDM. This result shows that a more efficient diffusion model analysis for various cognitive tasks is possible using this tool and further suggests that the processing speed of various cognitive models can be improved by using graphic processing units in the future.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.563-569
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• 2002

To overcome the weaknesses of conventional finite difference model in pollutant dispersion modelling, the particle tracking method is used. In this study, a three dimensional particle tracking model which can be used in Princeton Ocean Model was developed and verified through the various numerical tests. Usability of the model was also confirmed through the ocean outfall modelling in Tampa Bay, Florida. As it is expected, random walk model showed the less dispersion in a range compared to the conventional finite difference model and its reason is estimated due to an error from numerical diffusion which the conventional model holds. This newly developed model is expected to be used in various ocean dispersion modelling.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.23-32
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• 2018

The purpose of this study is to establish a walking energy weighted ERAM model that can predict the pedestrian volume by the connection structure of the vertical and horizontal spaces within a three-dimensional building. The process of building a walking-energy weighted ERAM model is as follows. First, the spatial graph was used to reproduce three-dimensional buildings with vertical and horizontal spatial connection structures. Second, the walking energy was measured on the spatial graph. Third, ERAM model was used to apply weights with spatial connection properties in random walking environment, and the walking energy weights were applied to the ERAM model to calculate the walk energy weighted ERAM values and visualize the distribution of pedestrian flow. To verify the validation of the established model, existing and proposed spatial analysis models were compared to real space. The results of this study are as follows : The model proposed in this study showed as much elaborated estimation of pedestrian traffic flow in real space as in traditional spatial analysis models, and also it showed much higher level of forecasting pedestrian traffic flow in real space than existing models.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.222-233
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• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.151-159
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• 2009

Network models for pedestrian flows have been studied in various ways. However, because of the simplicity and application, a number of researchers prefer the CA Model to analyze pedestrian's complicated behavior. These kinds of models based on Agent are being used as a microscopic analyzing method since it can easily adapt individuals' various characters and movement types. However, because pedestrians' movement can be (easily) effected by where they are and where they head, some models using the same rules have limit when considering pedestrians' every different movement. In this research, homogeneous section is defined as a similar movement type of individuals. With MDPM, we suggest simulation method explaining one-way walk and side-walk which could not be done in past.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.104-116
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• 2022

The purpose of this study is to systematically determine an optimal percentile cut-off in histogram analysis for calculating the mean parameters obtained from a non-Gaussian continuous-time random-walk (CTRW) diffusion model for differentiating individual glioma grades. This retrospective study included 90 patients with histopathologically proven gliomas (42 grade II, 19 grade III, and 29 grade IV). We performed diffusion-weighted imaging using 17 b-values (0-4000 s/mm²) at 3T, and analyzed the images with the CTRW model to produce an anomalous diffusion coefficient (D_m) along with temporal (𝛼) and spatial (𝛽) diffusion heterogeneity parameters. Given the tumor ROIs, we created a histogram of each parameter; computed the P-values (using a Student's t-test) for the statistical differences in the mean D_m, 𝛼, or 𝛽 for differentiating grade II vs. grade III gliomas and grade III vs. grade IV gliomas at different percentiles (1% to 100%); and selected the highest percentile with P < 0.05 as the optimal percentile. We used the mean parameter values calculated from the optimal percentile cut-offs to do a receiver operating characteristic (ROC) analysis based on individual parameters or their combinations. We compared the results with those obtained by averaging data over the entire region of interest (i.e., 100th percentile). We found the optimal percentiles for D_m, 𝛼, and 𝛽 to be 68%, 75%, and 100% for differentiating grade II vs. III and 58%, 19%, and 100% for differentiating grade III vs. IV gliomas, respectively. The optimal percentile cut-offs outperformed the entire-ROI-based analysis in sensitivity (0.761 vs. 0.690), specificity (0.578 vs. 0.526), accuracy (0.704 vs. 0.639), and AUC (0.671 vs. 0.599) for grade II vs. III differentiations and in sensitivity (0.789 vs. 0.578) and AUC (0.637 vs. 0.620) for grade III vs. IV differentiations, respectively. Percentile-based histogram analysis, coupled with the multi-parametric approach enabled by the CTRW diffusion model using high b-values, can improve glioma grading.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.