• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.292-303
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• 2003

An efficient mobility management for mobile stations plays an important role in mobile communication network. Two basic operations of mobility management are location registration and paging. A zone-based registration (ZBR) is implemented in most of mobile communication systems and we consider the mobility management scheme that combines a zone-based registration and a selective paging (SP). We propose new analytical model that can reflect on the characteristics of the ZBR based on 2-dimensional random walk mobility model and more efficient paging schemes considering the proposed model. We evaluate the performance of the mobility management scheme with our mobility model to determine the optimal size of location area that will result in the minimum signaling traffic on radio channels. Numerical results are provided to demonstrate that our mobility model is useful to evaluate the ZBR more exactly.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.347-360
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• 1998

Numerical simulations for fluid flow and solute transport in a fracture rock masses are performed by using a transient flow model, which is based on the three-dimensional stochastic and discrete fracture network model (DFN model) and is coupled hydraulic model with mechanical model. In the numerical simulations of the solute transport, we used to the particle following algorithm which is similar to an advective biased random walk. The purpose of this study is to predict the response of the tracer test between two deep bore holes (GPK1 and GPK2) implanted at Soultz sous Foret in France, in the context of the geothermal researches.l The data sets used are obtained from in situcirculating experiments during 1995. As the result of the transport simulation, the mean transit time for the non reactive particles is about 5 days between two bore holes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.

• 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.

• 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.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.366-370
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• 2001

한정된 무선 채널로 보다 많은 가입자에게 이동통신 서비스를 제공하기 위해서는 무선 채널의 효율을 높여야 하며 이를 위해 효율적인 위치등록 방법이 필수적이다. 본 연구에서는 이동기준 위치등록(movement-based registration, MBR)과 거리기준 위치등록(distance-based registration, DBR)의 성능을 평가하고 두 위치등록 방법의 성능을 비교한다. 본 연구에서는 육각형 셀 환경하에서 2차원 랜덤워크 모형 (2-dimensional random walk model)에 기반을 둔 이동성 모형을 이용하여 MBR과 DBR의 위치등록 부하를 구한다. 특히 기존의 수식과는 다른 형태의 DBR의 위치등록 부하에 대한 식을 제시하고 이를 이용하여 DBR이 MBR에 비하여 항상 우수한 성능을 나타냄을 보인다. 또한 다양한 경우에 대한 수리적 결과를 통하여 무선 채널에서의 신호 트래픽을 최소로 해 주는 최적 제어변수의 값을 제시한다. 본 연구의 결과는 시스템의 운용환경에 따라 적절한 위치등록 방법을 평가하는 데에 효과적으로 이용될 수 있다.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.323-331
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• 2021

With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO₂, NH₃, H₂S, CH₄) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.