• 한국정보시스템학회지:정보시스템연구
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• 제17권4호
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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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• 제33권5호
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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.

• 한국ITS학회 논문지
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• 제20권1호
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• pp.193-202
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• 2021

고정밀 지도의 갱신은 정사영상 또는 점군 데이터 등을 원천 자료로 하여 기하 정보를 우선적으로 수정한 이후 지도를 구성하는 공간객체들 간의 연관관계를 재정립하는 방식으로 진행된다. 이러한 일련의 과정들은 기하 정보를 처리하는 데에 많은 시간을 소요하므로 차량의 실시간 경로 계획(Real-time route planning)에 빠르게 적용되기 어렵다. 따라서 이 연구에서는 그래프 구조를 활용하여 경로 계획을 위한 도로 연결구조를 우선적으로 업데이트 하는 방식 및 도로 네트워크의 특징을 고려한 그래프 구조의 저장 유형을 제안하였다. 또한 제안된 방법을 실제 도로 자료에 적용해 봄으로써 실시간 경로 정보 전송 시의 활용 가능성에 대해 검토하였다.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2008년도 춘계학술대회
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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.

• 한국지능시스템학회논문지
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• 제16권2호
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• pp.172-178
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• 2006

본 논문에서는 시계열 예측을 이용한 법원경매 정보제공 시스템을 개발하였다. 이 시스템은 권리분석을 위하여 낙찰가를 예측하고, 낙찰예측가에 따라 배당 정보를 제공하도록 설계되어 있으며, 이를 구현하기 위하여 물건 자료의 입력 인터페이스와 정보 제공을 위한 웹 인터페이스를 구축하였다. 자료 입력 인터페이스는 자료의 입력, 수정, 삭제의 기능을 가지며, 웹 인터페이스는 법원경매 물건을 중심으로 관련 정보를 제공한다. 실시간 정보 제공에 초점을 두고 자동 권리분석이 가능하도록 하기 위하여 낙찰가를 시계열 자료로 표현하여 낙찰예상가를 예측 방법을 제안하고, 기존의 방법과 비교 실험을 통하여 제안방법의 유효성을 검증한다.

• 한국산학기술학회논문지
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• 제21권9호
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• pp.662-671
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• 2020

GNSS 상시관측소 고시좌표는 측지기준점으로써의 중요성과 다양한 위성측위 응용 분야의 활용성을 고려할 때 최고의 정확도와 최신성을 갖도록 관리해야 한다. 특히, 지진 등에 따른 지각 변위는 그 크기에 해당하는 만큼 기존 성과에 편의를 유발함으로, 그 영향이 목표 정확도를 초과할 때에는 신속히 새로운 기준 좌표를 산정·제공하는 등 적절한 조치가 필요하다. 본 논문에서는 GNSS 상시관측소 데이터 자동처리 시스템과 연계 구현할 수 있는 칼만 필터에 기반 좌표 시계열의 편의검출 절차와 방법을 연구하였다. 이를 통해 필터 이노베이션과 재추정 시계열에 적용할 수 있는 통계 검정 기법을 구현한 후 과학기술용 GNSS 소프트웨어에 의해 추정한 국내 14개소 상시관측소 2010년~2011년 시계열에 적용해 그 성능과 특징을 파악하였다. 그 결과 통계검정의 오류와 신뢰성을 고려할 때 필터링 시계열에 대한 CUSUM(Cumulative Sum) 검사는 지진 등에 따른 잔류편의 그리고 이노베이션에 대한 광역검정은 특정 에포크에서 발생하는 돌출오차 검출에 효과적인 것으로 분석되었다.

• 동력기계공학회지
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• 제6권1호
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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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• 제11권4호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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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.