• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.3
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• pp.78-83
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• 2014

To minimize the financial loss due to power facility malfunction, on-line diagnostic techniques are required to grasp any abnormal state of facilities in the live line as well as devices to diagnose abnormal states of power facility in an easy and prompt manner. This study aims to develop a portable UV detecting system by means of UV sensors for easier and efficient inspection of the degradation state of power facility in a long distance. Accordingly, it includes a simulation of corona discharges that may occur due to degradation of power facility and detection of ultraviolet pulse generation depending on the corona discharge intensity and measuring distance in application of UV sensors. Additionally, the optimal algorithm is determined for its application to the system's degradation diagnosis program based on the measured experiment data.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.3
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• pp.260-266
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• 2006

In this paper, We coupled fluid balance and director balance equation from Ericksen-Leslie's continuum theory and observed the motion of Liquid Crystal molecular. We simulated flow velocity and director distribution in which flow effect is considered in switching on and switching off state. We interpreted the dynamic response characteristic caused by the flow. As the result of the simulation, We could see the flow effect. In the case of Twisted Nematic(TN) cell, this flow caused abnormal twist temporarily in switching off state. We could prove that this abnormal twist is a direct cause of optical bounce phenomenon known well until now with the result of simulation. In addition, We analyzed the mechanism of the fast response due to flow in the case of Optically Compensated Bend(OCB) cell.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.16-21
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• 2005

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.664-668
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• 2012

This paper presents the operation strategy of KEPCO(Korea Electric Power COporation) ${\pm}80kV$ 60MW Bipole HVDC system that will be applied between Guemak C/S(converter station) and Hanlim C/S in Jeju island. Unlike intertie HVDC system, this system is located in AC power grid inside. Therefore, the enhancement of system security related with line flow and bus voltages can be major operation strategy. In this paper, in particular, the optimal operation algorithm in the abnormal(not steady state) power system is presented and simulated.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.76-78
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• 2005

We coupled fluid balance equation and director balance equation from Ericksen-Leslie's continuum theory and observed the motion of Twisted Nematic (TN) Liquid Crystals. We simulated flow velocity distribution and director distribution. We interpreted the dynamic response characteristic caused by the flow. As the result of the simulation, We could see the flow effect. And this flow caused abnormal twist to 4msec in switching off state. We could prove that this abnormal twist is a direct cause of optical bounce phenomenon known well until now with the result of simulation.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.169-176
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• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.109-117
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• 2022

This paper proposes a method for detecting flight anomalies of drones through the difference between the command of flight controller (FC) and the navigation solution. If the drones make a flight normally, control errors generated by the difference between the desired control command of FC and the navigation solution should converge to zero. However, there is a risk of sudden change or divergence of control errors when the FC control feedback loop preset for the normal flight encounters interferences such as strong winds or navigation sensor abnormalities. In this paper, we propose the method with a deep neural network model that predicts the control error in the normal flight so that the abnormal flight state can be detected. The performance of proposed method was evaluated using the real-world flight data. The results showed that the method effectively detects anomalies in various situation.

• The Plant Pathology Journal
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• v.37 no.5
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• pp.437-445
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• 2021

Tilletia laevis Kuhn (syn. Tilletia foetida (Wallr.) Liro.) causes wheat common bunt, which is one of the most devastating plant diseases in the world. Common bunt can result in a reduction of 80% or even a total loss of wheat production. In this study, the characteristics of T. laevis infection in compatible wheat plants were defined based on the combination of scanning electron microscopy, transmission electron microscopy and laser scanning confocal microscopy. We found T. laevis could lead to the abnormal growth of wheat tissues and cells, such as leakage of chloroplasts, deformities, disordered arrangements of mesophyll cells and also thickening of the cell wall of mesophyll cells in leaf tissue. What's more, T. laevis teliospores were found in the roots, stems, flag leaves, and glumes of infected wheat plants instead of just in the ovaries, as previously reported. The abnormal characteristics caused by T. laevis may be used for early detection of this pathogen instead of molecular markers in addition to providing theoretical insights into T. laevis and wheat interactions for breeding of common bunt resistance.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.