• The Journal of Information Systems
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• v.17 no.4
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• pp.281-299
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• 2008

An online game is popular topic due to the increased total online game market volume nowadays. Even though many studies on an online game are released, most studies have used survey method that reveal only section of the situation like a snapshot. For this reason, previous studies have a little limitation that does not show dynamically changing issues like a product life cycle and change in customer's satisfaction. Because of this, we researched on an online game with the system dynamic model which can show dynamic simulation to analysis time series data. We chose MMORPG (Massively Multi-play Online Role Playing Game) in sort of an online game because it has many absorbing factors and enthusiastic users. We assumed that the game developer is ready for updated version game and release that periodically and focused on dormant users who used to be enthusiastic about MMORPG and designed simulation model which analyze how to influence of update and CRM strategy on users. The simulation results showed that the update has positive influences to gather new users and hold established users and CRM strategies help to prevent dormant users from transferring to rivals to offer them re-absorbing factors. Through this study, we confirmed importance of update on a online game and suggested the necessity to introduce CRM strategy in an online game market.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.193-202
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• 2021

The update of a high-precision map was carried out by modifying the geometric information using ortho-images or point-cloud data as the source data and then reconstructing the relationship between the spatial objects. These series of processes take considerable time to process the geometric information, making it difficult to apply real-time route planning to a vehicle quickly. Therefore, this study proposed a method to update the road network for route planning using a graph data structure and storage type of graph data structure considering the characteristics of the road network. The proposed method was also reviewed to assess the feasibility of real-time route information transmission by applying it to actual road data.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.172-178
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• 2006

This paper presents a development of court auction information system using time series forecasting. The system forecast a highest bid price for claim analysis, and it is designed to offer an quota information by the bid price. For this realization, we implemented input interface of object data and web interface of information support. Input interface can be input, update and delete function and web interface is support some information of court auction object. We propose a forecasting method of a highest bid price for auto-claim analysis with real time information support and the results are verified the feasibility of the proposed method by experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.662-671
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• 2020

It is crucial to manage and maintain the geodetic reference coordinates of GNSS continuously operating reference stations (CORSs) in consideration of their fundamental roles in geodetic control and positioning navigation infrastructure. Earthquake-induced crustal displacement directly impacts the reference coordinates, so such events should be promptly detected, and appropriate action should be made to maintain the target accuracy, including update of the geodetic coordinates. To this end, this paper deals with online schemes for the detection of persistent shifts in the coordinate time-series produced by an automatic GNSS processing system. Algorithms were implemented to test filtered results, such as hypothesis tests of the innovation sequence of a Kalman filter and a cumulative sum (CUSUM) test. The results were assessed by the time-series of coordinates of 14 CORS for two years, including the 2011 Tohoku earthquake. The results show that the global hypothesis test is practical for detecting abrupt jumps, whereas CUSUM is effective for identifying persistent shifts.

• Journal of Power System Engineering
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• v.6 no.1
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• pp.96-101
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• 2002

In this paper, we address the problem of controlling an end-effector to track and grab a moving target using the visual servoing technique. A visual servo mechanism based on the image-based servoing principle, is proposed by using visual feedback to control an end-effector without calibrated robot and camera models. Firstly, we consider the control problem as a nonlinear least squares optimization and update the joint angles through the Taylor Series Expansion. And to track a moving target in real time, the Jacobian estimation scheme(Dynamic Broyden's Method) is used to estimate the combined robot and image Jacobian. Using this algorithm, we can drive the objective function value to a neighborhood of zero. To show the effectiveness of the proposed algorithm, simulation results for a six degree of freedom robot are presented.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.406-409
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• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.331-337
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• 2004

Target motion analysis with a sonar system in general uses a regular sampling time and thus obtains regular target information regardless of the target maneuver status. This often results in overconsumption of the limited sonar resources. We propose two methods of the IMM(interacting Multiple Model) PDAF algorithm for sonar resource management to improve target motion analysis performance and to save sonar resources in this paper. In the first method, two different process noise covariance which are used as mode sets are combined based on probability. In the second method, resource time which are processed from two mode sets is calculated based on probability and then considered as update time at next step. Performance of the proposed algorithms are compared with the other algorithms by a series of Monte Carlo simulation.