• Journal of Industrial Technology
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• v.27 no.A
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• pp.141-147
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• 2007

In this paper, we define the multi-states exponential cell resident time mobility model for describing the mobility characteristics of mobile user and analyzed the total cost of the hybrid method using multi-states cell resident time. Generally, the mobile user has three states for its movement, such as staying, walking and driving. This multi-states cell resident time based hybrid method reflects the movement characteristics of mobile user and adapts the location update period according to the states of mobility speed. As a results of the performance analysis, we can get the optimum parameters of the hybrid method for multi-states based mobile users.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.251-261
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• 1996

Databases are critical to business information systems and RDBMS is most widely used for the database system. Normalization was designed to control various anomalies(insert, update, and delete anomalies). However normalized database design does not account for the tradeoffs necessary for the performance reason. In this research, we model a database design problem without storage constraint. Given a normalized database design, in this model, we do the denormalization of duplicating columns in order in reduce frequent join processes. In this paper, we consider insert, update, delete, and storage cost, and the anomalies are treated by additional disk I/O cost necessary for each insert, update transaction. We propose a branch and bound method, and show considerable cost reduction.

• The Journal of Information Technology and Database
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• v.3 no.1
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• pp.3-24
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• 1996

Databases are critical to business information systems, and RDBMS is most widely used for the database system. Normalization has been designed to control various anomalies(insert, update, and delete anomalies). However, normalized database design does not account for the tradeoffs necessary for the performance. In this research, we develop a model for database design by denormalization of duplicating attributes in order to reduce frequent join processes. In this mood, we consider insert, update, delete, and query costs. The anomaly and data inconsistency are removed by additional disk I/O which is necessary for each update and insert transaction. We propose a branch and bound method for this model, and show considerable cost reduction.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1542-1551
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• 2006

In the Grid Database, some replicas will have more requests from the clients than others. A fast consistency algorithm has been presented to satisfy the high demand nodes in a shorter period of time. But it has poor performance in multiple regions of high demand for forming the island of locally consistent replicas. Then, a leader election method is proposed, whereas it needs much additional cost for periodic leader election, information storage, and message passing, Also, false leader can be created. In this paper, we propose a tree-based algorithm for replica update propagation. Leader replicas with high demand are considered as the roots of trees which are interconnected. All the other replicas are sorted and considered as nodes of the trees. Once an update occurs at any replica, it need be transmitted to the leader replicas first. Every node that receives the update propagates it to its children in the tree. The update propagation is optimized by cost reduction for fixed propagation schedule. And it is also flexible for the dynamic model in which the demand conditions change with time.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.383-393
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• 2008

Online games are the popular topic due to the increased total online game market volume nowadays. And many studies on online games are released. But most studies used the questionnaire method that reveals only section of the situation like a snapshot. For this reason, previous studies have a little limitation that does not show dynamical changing issues like a product life cycle and changes in customer's mind Because of this, we studied on online games with the system dynamic model which can show dynamic simulations to analysis time series data. We chose MMORPG (Massively Multi-play Online) RPG (Role Playing Game) in sort of online games because it has many absorbing factors and enthusiastic users. We designed the simulation model which analyzes the influences of update and CRM strategy on users. We put the game developer who is ready for updated version game and released that periodically and focused on dormant users who used to be enthusiastic about MMORPG. The simulation results showed that the update has positive influences on new users gathering and hold established users. And CRM strategies help to prevent dormant users from transferring to rivals by offering them re-absorbing factors. Through this study, we confirmed the importance of update on online games and the necessity of introducing CRM strategy in the online game market.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.29-35
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• 2009

The aim of the present study was to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples by using the visible and near infrared spectrum. Before the equation merge, correlation coefficient of calibration equation for nitrogen content on each rice parts was 0.945 (Leaf), 0.928 (Stem), and 0.864 (Whole plant), respectively. In the calibration models created by each part in the rice plant under the various regression method, the calibration model for the leaf was recorded with relatively high accuracy. Among of those, the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method. The calibration equation was sensitive based on variety and location variations. However, we have merged and enlarged various of the samples that made not only to measure the nitrogen content more accurately, but also later sampling populations became more diversified. After merging, $R^2$ value becomes more accurate and significantly to 0.950 (L.), 0.974 (S.), 0.940 (W.). Also, after removal of outlier, R2 values increased into 0.998, 0.995, and 0.997. In view of the results so far achieved, Standard error of prediction (SEP) and SEP (C) were reduced in the stem and whole plant. Biases were reduced in the leaf, stem as well as whole plant. Slopes were high in the stem. Standard deviation reduced in the stem but $R^2$ was high in the stem and whole plant. Result was indicated that calibration equation make update, and updating robust calibration equation from merge function and multi-variate calibration.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.182-187
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• 2007

A fuzzy modeling method is proposed to build the dynamic model of a walking-beam reheating furnace from the recorded data. In the proposed method, the number of membership function on each variable is increased individually and the modeling accuracy is evaluated iteratively. When the modeling accuracy is satisfied, the membership functions on each variable are fixed and the structure of fuzzy model is determined. Because the training data is limited, in this process, as the number of membership function increase, it is highly possible that some rules are missing, i.e., no data in the training set corresponds to the consequent part of a missing rule. To complete the rulebase, the output of the model constructed at the previous step is used to generate the consequent part of the missing rules. Finally, in the real time application, a rolling update scheme to rulebase is introduced to compensate the change of system dynamics and fine tune the rulebase. The proposed method is verified by the application to the modeling of a reheating furnace.

• Smart Structures and Systems
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• v.11 no.1
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.