• Korean Journal of Computational Design and Engineering
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• v.20 no.4
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• pp.401-411
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• 2015

Cybrid virtual plant concept is defined as a cyber plant mimicking a physical plant by using plant engineering data and sensor data coming from sensors attached to facilities of the physical plant. Cybrid virtual plant is a new concept for plant industry so that plant managers and operators' requirements need to be captured for systematic application of the concept to the plant industry. The paper proposed an architecture of the cybrid virtual plant, and provided requirement analysis results for a specific plant company. A database, named smart-cube repository, for the proposed cybrid virtual plant is also proposed and its conceptual data structure is described.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.71-78
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• 2017

Many studies have been implemented to manage solar plant being supplied widely in recent years. This study analyzed heat emission of solar cell using unmanned aerial vehicle(UAV)-based thermal infrared sensor, and major conclusions are as belows. Firstly, orthomosaic image and digital surface model(DSM) data were acquired using UAV-based RGB sensor, and solar light module layer necessary to analyze the heat emission of solar cell was constructed by these data. Also as a result of horizontal error into validation points using virtual reference service(VRS) survey for evaluating the location accuracy of solar light module layer, higher location accuracy could be acquired like standard error of $dx={\pm}2.4cm$ and $dy={\pm}3.2cm$. And this study installed rubber patch to test the heat emission of solar cell and could analyzed efficiently the location of rubber patch being emitted heat using UAV-based thermal infrared sensor. Also standard error showd as ${\pm}3.5%$ in analysis between calculated cell ratio by rubber patch and analyzed cell ratio by UAV-based thermal infrared sensor. Therefore, it could be efficiently analyzed to heat emission of solar cell using UAV-based thermal infrared sensor. Also efficient maintenance of solar plant could be possible through extracting the code of solar light module being emitted of heat automatically.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.414-421
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• 2024

In this paper, we design and verify a system that can detect piping leakage noise in an environment with significant reverberation and reflection using a multi-channel acoustic sensor module as a technology to prevent major plant accidents caused by leakage. Four-channel microphones arranged in a tetrahedron are designed as a single sensor module to measure three-dimensional sound intensity vectors. In an environment with large effects of reverberation and reflection, the measurement error of each sensor module increases on average, so after placing multiple sensor modules in the field, measurement results showing locations with large errors due to effects such as reflection are excluded. Using the intersection between three-dimensional vectors obtained from several pairs of sensor modules, the coordinates where the sound source is located are estimated, and outliers (e.g., positions estimated to be outside the site, positions estimated to be far from the average position) are detected and excluded among the points. For achieving aforementioned goal, an excluding algorithm by deciding the outliers among the estimated positions was proposed. By visualizing the estimated location coordinates of the leakage sound on the site drawing within 1 second, we construct and verify a system that can detect the location of the leakage sound in real time and enable immediate response. This study is expected to contribute to improving accident response capabilities and ensuring safety in large plants.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.154-160
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• 2003

Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain time delay. Uncertain time delay can cause degradation in performance and stability of distributed control system based on VDN. This paper first investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain varying time delay. The validity of the proposed control scheme is demonstrated with real-time velocity control of DC servo motor located in remote site.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.34-46
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• 2016

Networked Control System (NCS) has evolved in the past decade through the advances in communication technology. The problems involved in NCS are broadly classified into two categories namely network issues due to network and control performance due to system network. The network problems are related to bandwidth allocation, scheduling and network security, and the control problems deal with stability analysis and delay compensation. Various delays with variable length occur due to sharing a common network medium. Though most delays are very less and mostly neglected, the network induced delay is significant. It occurs when sensors, actuators, and controllers exchange data packet across the communication network. Networked induced delay arises from sensor to controller and controller to actuator. This paper presents an adaptive delay compensation process for efficient control. Though Smith predictor has been commonly used as dead time compensators, it is not adaptive to match with the stochastic behavior of network characteristics. Time delay adaptive compensation gives an effective control to solve dead time, and creates a virtual environment using the plant model and computed delay which is used to compensate the effect of delay. This approach is simulated using TrueTime simulator that is a Matlab Simulink based simulator facilitates co-simulation of controller task execution in real-time kernels, network transmissions and continuous plant dynamics for NCS. The simulation result is analyzed, and it is confirmed that this control provides good performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1671-1680
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• 2014

There are always much concern about the leakage of radiation materials in the event of dismantle or unexpected accident of nuclear power plant. In order to remove the leakage of radiation materials, appropriate dispersion detection techniques for radiation materials are necessary. However, because direct handling of radiation materials is highly restricted and risky, developing radiation-related techniques needs computer simulation in advance to evaluate the feasibility. In this paper, we propose a radiation imaging technique which can acquire 3D dispersion information of radiation materials and tested by simulation. Using two virtual 1D radiation sensors, we obtain stereo radiation images and acquire the 3D depth to virtual radiation materials using stereo disparity. For point and plane type virtual radiation materials, the possibility of the acquisition of stereo radiation image and 3D information are simulated.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.