• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1266-1266
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1067-1067
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• 2005

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2007.05a
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• pp.199-202
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• 2007

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.252-258
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• 2007

There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• Journal of Ocean Engineering and Technology
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• v.28 no.1
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• pp.6-11
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• 2014

This paper presents the results of an experimental study of wave run-ups on a semi-submersible offshore structure. A series of model tests with a 1:80 scale ratio were carried out in the two-dimensional wave basin of MOERI/KIOST. The experimental model had two columns and one pontoon. The model was fixed and wave elevations were measured at five points per column. Two different draft (operational & survival) conditions and three wave heights were considered under regular wave conditions. First, the nonlinear characteristics of wave run-ups are discussed by using the time series data. Then, the wave heights are compared with numerical results based on the potential flow model. The comparison shows fairly good correlation between the experiments and computations. Finally, wave run-ups under the operational and survival conditions are suggested.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.53-67
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• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• Smart Structures and Systems
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• v.11 no.1
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• pp.1-17
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• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.475-485
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• 2014

A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

• Health Policy and Management
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• v.19 no.4
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• pp.33-52
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• 2009

Health promotion policies have needed to assess in detailed and evidence-based work to set a policy goal and clear future directions of health promotion in Korea. To identify the major factors related with health promotion, we assessed the associations between public health outcome (potential years of life loss, PYLL) and national health determinants. For this purpose, we used a pooled cross sectional time-series regression analysis with corrected fixed effect models involving sixteen member countries of the Organisation for Economic Cooperation and Development during the period 1970 to 2001. The PYLL was positively associated with tobacco and alcohol consumption (model 1 and 2) and calories intake (model 2 and 3) while the PYLL was negatively associated with GDP, fruit and vegetable intake (model 2), number of doctors (model 3), coverage rates of health care security, and elderly population rates (model 4). In conclusion, health behaviors related with tobacco, alcohol, and nutrition were significant health determinants for health outcome. Overall analysis results of this study will provide a guidance toward improved macro- and micro-policy development for future health promotion policy in Korea.