• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.367-370
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• 2024

본 논문에서는 데이터-드리븐 기법을 이용해 가상환경에서 사용자의 동작에 따라 아바타의 저글링 움직임을 자연스럽게 처리할 수 있는 방법을 제안한다. 사용자의 저글링 동작 정보를 이용하여 아바타의 움직임을 제어할 뿐만 아니라 가상 공의 궤적을 실시간으로 표현할 수 있다. 이 과정에서 사용자의 손위치 정보를 모두 활용하는 것이 아닌, 한 쪽 손의 데이터를 기반으로 다른 쪽 손의 궤적을 합성한다. 또한 계산량이 큰 물리 기반 최적화 과정이 아닌, 상대적으로 경량화된 기법인 포물선 운동을 활용해 가상 공의 궤적으로 실시간으로 표현할 수 있는 결과를 보여준다.

• Journal of Aerospace System Engineering
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• v.15 no.4
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• pp.7-10
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• 2021

Including 425 Program, which is Korean military surveillance and reconnaissance satellite, there were mostly civil-driven space programs in Korea. However, there are increasing numbers of military demand-driven space program in nowadays. Furthermore, it is positive effects on launch vehicle development in Korea that the termination of Korea-U.S. missile guideline. In this paper, it emphasizes the needs of system engineering(SE) management method which meets both defense system's characteristics and space's characteristics. These characteristics are such as non-fixable after the launch, the security issue in defense system. And it also introduces SE tool, methodology and its philosophy. There are several functions that data management, issue management, risk management, and technical requirement management. Also describing its implications and direction of improvement.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.1-6
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• 2022

Real-time systems using web cralwing data must provide users with data from the same database as remote data. To do this, the web crawler repeatedly sends HTTP(HtypeText Transfer Protocol) requests to the remote server to see if the remote data has changed. This process causes network load on the crawling server and remote server, causing problems such as excessive traffic generation. To solve this problem, in this paper, based on user events, we propose a real-time web crawling technique that can reduce the overload of the network while securing the reliability of maintaining the sameness between the data of the crawling server and data from multiple remote locations. The proposed method performs a crawling process based on an event that requests unit data and list data. The results show that the proposed method can reduce the overhead of network traffic in existing web crawlers and secure data reliability. In the future, research on the convergence of event-based crawling and time-based crawling is required.

• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.513-517
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• 2013

Recently, maskless lithography (ML) systems have become popular in digital manufacturing technologies. To achieve high-throughput manufacturing processes, digital micromirror devices (DMD) in ML systems must be driven to their operational limits, often in harsh conditions. We propose an instrument and algorithm to detect DMD malfunctions to ensure perfect mask image transfer to the photoresist in ML systems. DMD malfunctions are caused by either bad DMD pixels or data transfer errors. We detect bad DMD pixels with $20{\times}20$ pixel by white and black image tests. To analyze data transfer errors at high frame rates, we monitor changes in the frame rate of a target DMD pixel driven by the input data with a set frame rate of up to 28000 frames per second (fps). For our data transfer error detection method, we verified that there are no data transfer errors in the test by confirming the agreement between the input frame rate and the output frame rate within the measurement accuracy of 1 fps.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.341-351
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• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.111-122
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• 2023

Since the organization of civil servants has been divided and stratified according to the characteristics of the bureaucracy, it is inevitable that the organization and personnel will increase when new tasks arise. Even in the process of informatization, only the processing method was brought online while leaving the existing business processing procedures as they were, so there was no reduction in manpower through informatization. In order to maintain or upgrade the current administrative services while reducing the number of civil servants, it is inevitable to use AI technology. By using data and AI to integrate the 'powers and responsibilities assigned to the officials in charge', manpower can be reduced, and the reduced costs can be reinvested in the collection, analysis, and utilization of on-site data to further promote intelligent informatization. In this study, as a way for the government's success in intelligent informatization innovation, we proposed a 'Civil Servants-AI Collaboration Platform'. This Platform based on the civil servant proposal system as a reward system and the characteristics of intelligent informatization that are different from the informatization. By establishing a 'Civil Servants-AI Collaboration Platform', the performance evaluation system of the short-term evaluation method by superiors can be improved to a data-driven always-on evaluation method, thereby alleviating the rigid hierarchy of government organizations. In addition, through the operation of Collaboration Platform, it will become common to define and solve problems using data and AI, and the intelligence informatization of government organizations will be activated.