• 한국지반공학회지:지반
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• 제26권9호
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• pp.33-47
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• 2010

Underground constructions continue to provide challenges to Geotechnical Engineers yet they pose the best opportunities for development and deployment of advance technologies for analysis, design and construction. The reason for this is that, by virtue of the nature of underground constructions, more data and information on ground characteristics and response become available as the construction progresses. However, due to several barriers, these data and information are rarely, if ever, utilized to modify and improve project design and construction during the construction stage. To enable the use of evolving realtime data and information, and adaptively modify and improve design and construction, the paper presents an analysis and design system, called AMADEUS, for underground projects. AMADEUS stands for Adaptive, real-time and geologic Mapping, Analysis and Design of Underground Space. AMADEUS relies on recent advances in IT (Information Technology), particularly in digital imaging, data management, visualization and computation to significantly improve analysis, design and construction of underground projects. Using IT and remote sensors, real-time data on geology and excavation response are gathered during the construction using non-intrusive techniques which do not require expensive and time-consuming monitoring. The real-time data are then used to update geological and geomechanical models of the excavation, and to determine the optimal, construction sequences and stages, and structural support. Virtual environment (VE) systems are employed to allow virtual walk-throughs inside an excavation, observe geologic conditions, perform virtual construction operations, and investigate stability of the excavation via computer simulation to steer the next stages of construction.

• 한국측량학회지
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• 제34권6호
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• pp.569-578
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• 2016

With the recent advancement of surveying and technology, the spatial data acquisition rates and precision have been improved continually. As the updates of spatial data are rapid, and the size of data increases in line with the advancing technology, the LOD (Level of Detail) algorithm has been adopted to process data expressions in real time in a streaming format with spatial data divided precisely into separate steps. The existing GRID analysis utilizes the single DEM, as it is, in examining and analyzing all data outside the analysis area as well, which results in extending the analysis time in proportion to the quantity of data. Hence, this study suggests a method to reduce analysis time and data throughput by acquiring and analyzing DEM data necessary for GRID analysis in real time based on the area of analysis and the level of precision, specifically for streaming DEM data, which is utilized mostly for 3D geographic information service.

• 한국태양에너지학회 논문집
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• 제30권3호
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• pp.25-32
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• 2010

A real-time monitoring system was developed for transfer, receive, backup and analysis of wind power data at three wind farm(Hang won, Hankyung and Sung san) in Jeju. For this monitoring system a communication system analysis, a collection of data and transmission module development, data base construction and data analysis and management module was developed, respectively. These modules deal with mechanical, electrical and environmental problem. Especially, time series graphic is supported by the data analysis and management module automatically. The time series graphic make easier to raw data analysis. Also, the real-time monitoring system is connected with wind power forecasting system through internet web for data transfer to wind power forecasting system's data base.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 춘계학술대회
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• pp.165-166
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• 2017

어느 곳에서나 실시간으로 데이터의 정보를 전달 받고 그를 의미 있는 데이터로 분석해 내는 것을 요구하고 있다. 이러한 분석을 위한 플랫폼에 대한 연구도 활발히 진행 중에 있다. 본 논문에서는 실시간으로 IoT 데이터를 수집하고 분석하는데 겪는 문제들을 해결해 내기 위해 중요한 요소가 무엇인지를 알아보려 한다. 기존의 데이터 수집 방법과 분석 방법보다 얼마나 더 나은지가 그 데이터의 가치를 판단하는 기준이 될 수 있다. 실시간으로 많은 곳에 있는 센서로부터 보다 빠르고 신속하게 데이터를 정확히 수집하고 저장하는 기술과 그 저장되어진 데이터로부터 값을 도출해 낼 수 있는 분석 방법이 중요하다. 따라서 IoT 환경에서의 분석 플랫폼의 중요한 요건은 대량의 데이터를 실시간 처리하고 그를 집중화 시켜 관리하는 것이라 할 수 있다.

• 전기학회논문지
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• 제62권10호
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• pp.1361-1367
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• 2013

This paper describes and suggests the data visualization for real time power system operation based on energy management system. Because real time power system operation performs analysis of the vast amount of on-line data, the operators need intuitive data visualization to find out useful information in the big data. Especially, in emergency situation, the data visualization is able to assist the operators in handling the crisis quickly and efficiently. Therefore, this paper aims to improve displays of output of real time power system operation by visualizing on-line big data. Through this study, we can develop improved visualization technique for real time power system operation, which has highly readable displays of output and intuitive information.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.134-142
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• 2021

This paper proposes a probabilistic method in analyzing timing measurements to determine the periodicity of real-time tasks. The proposed method fills a gap in existing techniques, which either concentrate on the estimation of worst-case execution times, or do not consider the stochastic behavior of the real-time scheduler. Our method is based on the Z-test statistical analysis which calculates the probability of the measured period to fall within a user-defined standard deviation limit. The distribution of the measured period should satisfy two conditions: its center (statistical mean) should be equal to the scheduled period of the real-time task, and that it should be symmetrical with most of the samples focused on the center. To ensure that these requirements are met, a data adjustment process, which omits any outliers in the expense of accuracy, is presented. Then, the Z-score of the distribution according to the user-defined deviation limit provides a probability which determines the periodicity of the real-time task. Experiments are conducted to analyze the timing measurements of real-time tasks based on real-time Linux extensions of Xenomai and RT-Preempt. The results indicate that the proposed method is able to provide easier interpretation of the periodicity of real-time tasks which are valuable especially in comparing the performance of various real-time systems.

• 제어로봇시스템학회논문지
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• 제14권7호
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• pp.720-725
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• 2008

This paper proposes a system architecture for USN with a service robot to provide more active assisted living services for elderly persons by monitoring their mental and physical well-being with USN environments at home, hospital, or silver town. Sensors embedded in USN are used to detect preventive measures for chronic disease. Logged data are transferred to main controller of a service robot via wireless channel in which the analysis of data is performed. For the purpose of handling emergency situations, it needs real-time processing on gathering variety sensor data, routing algorithms for sensor nodes to a moving sink node and processing of logged data. This paper realized multi-hop sensor network to detect user movements with biometric data transmission and performed algorithms on Xenomai, a real-time embedded Linux. To leverage active sensing, a mobile robot is used of which task was implemented with a priority to process urgent data came from the sink-node. This software architecture is anticipated to integrate sensing, communication and computing with real-time manner. In order to verify the usefulness of a proposed system, the performance of data transferring and processing on a real-time OS with non real-time OS is also evaluated.

• 한국산업정보학회논문지
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• 제28권5호
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• pp.89-108
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• 2023

본 연구는 공식발표 통계지표의 적시성 확보를 위해 기존 Nowcasting 방법론을 살펴보고 실시간 경기 현황 분석이 가능한 Real-time nowcasting 모형을 운용하기 위한 대안 데이터와 그 수집 체계를 점검한다. 공공영역과 민간영역에서 경기지표를 예측할 수 있는 고빈도 실시간 데이터를 탐색하고, 나아가 데이터의 수집, 가공, 모형화를 위한 클라우드 기반의 구축과정을 제안한다. 더불어 Real-time nowcasting 모형 추정 및 데이터 관리에 있어 고려해야 할 요소를 확인함으로써 적시성 및 안정성을 갖춘 공식 통계지표의 예측 프로세스를 제시한다.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.46-53
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• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• 한국컴퓨터정보학회논문지
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• 제20권12호
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• pp.37-44
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• 2015

In this paper, we propose the assistance method to enable safe driving through analysis of dangerous driving behavior using real-time alarm by vehicle speed, azimuth data and smartphone. For this method, smartphone is receiving driving data from digital tachograph using communication. Safe driving habit is a very important issue to commercial vehicle because that driver's long time driving than other vehicle type driver. Existing methods are very inefficient to improve immediately dangerous driving habits during driving because proceed driving behavior analysis after the vehicle operation. We propose the new safe driving assistance method that can prevent traffic accidents by real-time and improve the driver's wrong driving habits through real-time dangerous driving behavior analysis and notification the result to the driver. We have confirmed that the method in this paper will help to improve driving habits and can be applied through the proposed method implementation and simulation experiment.