• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1272-1281
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• 2006

RFID 기술을 이용함으로써 얻을 수 있는 많은 장점으로 인하여, 공급망에서 발생하는 실시간 제품 정보를 수집하고 관리하기 위하여 RFID 기술이 도입되고 있다. 기존의 RFID 기반의 공급망 관리 시스템은 제품의 위치에 따른 가시성은 확보할 수 있지만, 제품의 모든 상태에 따른 가시성은 확보하지 못한다는 단점이 있다. 이러한 단점을 해결하기 위해, 본 논문에서는 공급망 상의 제품 이동을 계획하고, 제품이 계획에 따라 이동할 때 발생하는 정보를 실시간으로 모니터링하고 통제할 수 있는 제품 상태 기반의 물류 통제 시스템을 설계하고 개발하였다. 이를 위해 본 연구에서는 첫째, 공급망에서 발생하는 제품 상태의 정의와 상태 변화의 흐름을 state chart로 표현하고, 둘째, 공급망에서의 폐쇄형관리 패러다임을 통한 제품 통제(감시 및 예외처리)를 정의하였으며, 셋째, Temporal data modeling을 통해 RFID 데이터 기반의 Database를 설계하고, 마지막으로, Publish/Subscribe 모델을 통해 효율적인 제품 상태 기반의 물류 통제 시스템 아키텍처를 설계하였다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.05a
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• pp.58-60
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• 2004

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.10
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• pp.840-845
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• 2012

An ARX(Auto-Regressive eXogenous) modeling technique for vortex dynamics in the BFS(Backward Facing Step) flow field is proposed in this paper. In order for the modeling of the dynamics, the spatial and temporal modes are extracted through POD(Proper Orthogonal Decomposition) analysis. Determining the orders of the inputs and outputs for an ARX structure is carried out by the spectrum analysis and temporal mode analysis, respectively. The order of input delay terms is also determined by the flow velocity. Finally the coefficients of the ARX model are designed by using an artificial neural network.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.16-25
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• 2011

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Embedded System context-awareness service that interacts with the physical space can have property such as time. The exceptional behaviors of the system that interact with physical space can result in critical damage and cause danger to the operation of an embedded system. we propose an approach which should include spatio-temporal property and exceptional management in the context model, and verify its effectiveness using an expanded Petri-Net. The context-awareness service modeling of an embedded system is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net for the exceptional behaviors of the system. The proposed methodology demonstrated using an example that is emergency medical service. The use of expanded Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness for the exceptional handling.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.137-149
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• 2015

Flood planning needs to recognize trends for extreme precipitation events. Especially, the r-year return level is a common measure for extreme events. In this paper, we present a nonstationary temporal model for precipitation return levels using a hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitation measured in Korea with a generalized extreme value (GEV). The temporal dependence among the return levels is incorporated to the model for GEV model parameters and a linear model with autoregressive error terms. We apply the proposed model to precipitation data collected from various stations in Korea from 1973 to 2011.

• Journal of Korean Society on Water Environment
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• v.28 no.6
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• pp.866-881
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• 2012

Han River is a complex water system consisting of many lakes. The water quality of Lake Paldang is significantly affected by incoming flows, which are the South and North branches of the Han River, and the Kyungan Stream. In order to manage the water quality of the Lake Paldang, we should consider the entire water body where the incoming flows are included. The objectives of this study are to develop an integrated river and lake modeling system for Han River system using a multidimensional dynamic model and evaluate the model's performance against field measurement data. The integrated model was calibrated and verified using field measurement data obtained in 2007 and 2008. The model showed satisfactory performance in predicting temporal variations of water level, flow rate and temperature. The Root Mean Square Error (RMSE) for water temperature simulation were $0.88{\sim}2.13^{\circ}C$ (calibration period) and $1.05{\sim}2.00^{\circ}C$ (verification period) respectively. And Nash-Sutcliffe Efficiency (NSE) for water temperature simulation were 1089~0.98 (calibration period) and 0.90~0.98 (verification period). Utilizing the validated model, we analyzed the spatial and temporal distributions of temperature within Han River system. The variations of temperature along the river reaches and vertical thermal profiles for each lakes were effectively simulated with developed model. The suggested modeling system can be effectively used for integrated water quality management of water system consisting of many rivers and lakes.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.257-266
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• 2022

Inundation damage is increasing every year due to localized heavy rain and an increase of rainfall exceeding the design frequency. Accordingly, the importance of hydraulic structures for flood control and defense is also increasing. The hydraulic structures are designed according to its purpose and performance, and the amount of flood is an important calculation factor. However, in Korea, design rainfall is used as input data for hydrological analysis for the design of hydraulic structures due to the lack of sufficient data and the lack of reliability of observation data. Accurate probability rainfall and its temporal distribution are important factors to estimate the design rainfall. In practice, the regression equation of temporal distribution for the design rainfall is calculated using the cumulative rainfall percentage of Huff's quartile method. In addition, the 6^th order polynomial regression equation which shows high overall accuracy, is uniformly used. In this study, the optimized regression equation of temporal distribution is derived using the variable selection method according to the principle of parsimony in statistical modeling. The derived regression equation of temporal distribution is verified through the significance test. As a result of this study, it is most appropriate to derive the regression equation of temporal distribution using the stepwise selection method, which has the advantages of both forward selection and backward elimination.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.10
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• pp.833-839
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• 2012

In this paper, we analyze the reconstruction error in the modeling of flow field on BFS(Backward Facing Step). In order for the mathematical modelling of a density on the field, the spatial and temporal modes are extracted by POD(Proper Orthogonal Decomposition) method. After formulating the modeling error, we summarize the relationship between the energy strength and the reconstruction errors. Moreover the allowable modeling error limits in the flow control point of view are confined by analysing in the frequency domain as well as time domain of the reconstructed data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.