• Journal of KIBIM
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• v.9 no.3
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• pp.1-7
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• 2019

Digital twin technology has been actively developed to monitor and assess the current state of actual structures. The digital twin changes the traditional observation method performed in the field to the real-time observation and detection system using virtual online model. Thus, this study designed a digital twin model for a beam and examined the feasibility of the digital twin for bridges. To reflect the current state of the bridge, model updating was performed according to the field test data to construct an analysis model. Based on the constructed bridge analysis model, the relationship between strain and displacement was used to represent a virtual model that behaves in the same way as the actual structure. The strain and displacement relationship was expressed as a matrix derived using an approximate analytical theory. Then, displacements can be obtained using the measured data obtained from strain sensors installed on the bridge. The coordinates of the obtained displacements are used to construct a virtual digital model for the bridge. For verification, a beam was fabricated and tested to evaluate the digital twin model constructed in this study. The displacements obtained from the strain and displacement relationship agrees well with the actual displacements of the beam. In addition, the displacements obtained from the virtual model was visualized at the locations of the strain sensor.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.57.2-57.2
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• 2018

In astronomical observation, sequential device control and real-time data processing are important to maximize observing efficiency. We have developed series of automatic observing software (KAOS, KHU Automatic Observing Software), e.g. KAOS30 for the 30 inch telescope in the McDonald Observatory and KAOS76 for the 76 cm telescope in the KHAO. The series consist of four packages: the DAP (Data Acquisition Package) for CCD Camera control, the TCP (Telescope Control Package) for telescope control, the AFP (Auto Focus Package) for focusing, and the SMP (Script Mode Package) for automation of sequences. In this poster, we introduce KAOS10 which is being developed for controlling a small telescope such as aperture size of 10 cm. The hardware components are the QHY8pro CCD, the QHY5-II CMOS, the iOptron CEM 25 mount, and the Stellarvue SV102ED telescope. The devices are controlled on ASCOM Platform. In addition to the previous packages (DAP, SMP, TCP), KAOS10 has QLP (Quick Look Package) and astrometry function in the TCP. QHY8pro CCD has RGB Bayer matrix and the QLP transforms RGB images into BVR images in real-time. The TCP includes astrometry function which adjusts the telescope position by comparing the image with a star catalog. In the future, We expect KAOS10 be used on the research of transient objects such as a variable star.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.49-56
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• 2000

Instrumental observation of earth quakes in KIGAM was first attempted in the earty 1980`s by using 6 portable seismographs in the vicinity of Yang-San Faults. Now twenty-four permanent stations, which are equipped with short-period or broad-band seismometer, are included in seismic research network in KIGAM, including KSRS array station in Wonju which is consisted of 26 bore-hole stations. The seismic network of KIGAM is also linked to that of KEPRI(Korea Electric Power Research Institute)which is consisted of eight stations installed within and around the nuclear power plants. Owing to real-time data acquisition by telemetry, it became feasible to automatically locate hypocenters of the local events within fifteen minutes by computer data processing system, named KEMS(Korea Earthquake Monitoring System). Results of the hypocenter determination, together with observational data, are compiled and stored in the data base system. And they are published via web site whose URL is http://quake.kigam.re.kr KIGAM is also running t재 permanent geomagnetic stations installed in Daejun and Kyungju. The observed geomagnetic data are transmitted to Earthquake Research Centre in KIGAM by seismic network and compiled for the purpose of earthquake prediction research and other basic geophysical research.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.1-8
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• 2017

In recent years, the quixotic nature and concentration of rainfall due to global climate change has intensified. To monitor localized heavy rainfalls, a reliable disaster monitoring and warning system with advanced remote observation technology and high-precision display is important. In this paper, we propose a GIS-based intuitive and realistic 3D radar data display technique for accurate and detailed weather analysis. The proposed technique performs 3D object modeling of various radar variables along with ray profiles and then displays stereoscopic radar data on detailed geographical locations. Simulation outcomes show that 3D object modeling of weather radar data can be processed in real time and that changes at each moment of rainfall events can be observed three-dimensionally on GIS.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.146-155
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• 2002

We present a new technique for achieving source separation when given only a single charmel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single charmel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.146-146
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• 2002

We present a new technique for achieving source separation when given only a single channel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.269-274
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• 2024

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator(CAPPI),representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.

• Journal of Korea Water Resources Association
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• v.38 no.6 s.155
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• pp.485-494
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• 2005

It is very important to analyze the correlation between discharge and water quality. The observation of discharge and water quality are effective at same point as well as same time for real time management. But no less significant is the fact that there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. In this case, it need to observe accurate discharge data, and to develop forecasting program or system using real time data. In this paper, discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and compared with discharge using WMS(Watershed Modeling System) model. WMS shows better results when peak discharge is small and hydrograph is smooth. Forecasted discharge of neural network model have achieved the highest overall accuracy of specific discharge and WMS. Neural network model forecast change of discharge well on unrecored station.

• Journal of Environmental Science International
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• v.26 no.4
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• pp.521-527
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• 2017

Solar radiation forecasts are important for predicting the amount of ice on road and the potential solar energy. In an attempt to improve solar radiation predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, support vector machines and logistic regression. To validate machine learning models, the results from the simulation was compared with the solar radiation data observed over Jeju observation site. According to the model assesment, it can be seen that the solar radiation prediction using random forest is the most effective method. The error rate proposed by random forest data mining is 17%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.395-397
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• 2010

AWS that exist in Pusan is watching local meteorological phenomena established in place that the weather observatory does not exist by real time, and is used usefully to early input data of numerical weather forecasting model. I wished to display downtown of Pusan and air temperature change of peripheral area using this AWS data. Analyzed volatility using AWS observation data for 5 years to recognize air temperature change of Pusan area through data about temperature among them. Drew air temperature distribution chart by season of recapitulative Pusan area applying IDW linear interpolation with this.