• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.139.1-139.1
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• 2011

Electric vehicle use independent electricity of high voltage. if isolation of electricity is destructed, devices and people are considerably damaged. Therefore, detection of ground fault is necessary for electric vehicle. As the existing detection method of ground fault can not detect ground fault when isolation of both positive side and negative side of electricity is destructed, and change of voltage of electricity. This paper proposed detection method for ground fault of both two sides of electricity and change of voltage. The proposed method is verified by analysis of equivalent circuit.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.975-978
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• 1997

This Paper presents a high impedance fault (HIF) detection algorithm of distribution systems using wavelet transform. Two HIFs on dry soil and sandy soil were simulated on various load conditions in 22.9 kV distribution systems using EMTP, and the current wavelets were decomposed by wavelet transform. The current root mean square(rms) change, the index change rate and the relative amplitude change were used as the multi-criteria for a HIF detection. The index change rate and the relative amplitude were made using the wavelet coefficients.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.7
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• pp.81-90
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• 2014

This paper proposes new arc algorithm to detect series quasi-arc. This algorithm analyzes odd and even harmonics until 9th using discrete fourier transform (DFT) and detect series arc comparing RMS values of load current. Resistors, lights, dimmer and vacuum cleaner which can be distinguished linearity load and quasi arc load are adopted to perform experiments. This algorithm is confirmed to emulate arc detecting with measuring current data.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.31-32
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• 2007

In this paper, a fault section detection method is proposed for ungrounded system in the case of a single line-to-ground fault. A conventional method is used for faulted feeder selection according to the angular relationship between zero-sequence currents of the feeders and zero-sequence voltage of the system. Fault section detection is based on the comparison of phase angle of zero-sequence current. Proposed method has been testified in a demo system by Matlab/Simulink simulations. Based on Distribution Automation System(DAS), Feeder Remote Terminal Unit(FRTU) is used to collect those necessary data, at present a demo system is under developing using Manufacturing Message Specification (MMS) in IEC61850 standard.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.24-27
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• 2005

The NDT(Non-Destructive Testing) is valid fur the defect detection of rolling stocks because it can be used to detect defects in invisible places. For example, in case of wheelsets fatigue cracks are initiated in the wheel seat that suffers from fretting fatigue damage. But the conventional ICFPD method can not be applied to detect such cracks in press-fit area of the axle by some technical problems. In this study, we introduced a new ICFPD (Induced Current Focusing Potential Drop) method that can be applied in press-fit area of the axle. And we performed the finite element analysis of the new ICFPD method using measured electromagnetic properties of the wheel and axle. It seems that our approach is very useful f3r the detection of defects in invisible places.

• Land and Housing Review
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• v.11 no.4
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• pp.99-104
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• 2020

This paper presents the recent research developments identified through a review of literature on the application of artificial intelligence in developing automated designs of fire protection facilities. The literature review covered research related to image recognition and applicable neural networks. Firstly, it was found that convolutional neural network (CNN) may be applied to the development of automating the design of fire protection facilities. It requires a high level of object detection accuracy necessitating the classification of each object making up the image. Secondly, to ensure accurate object detection and building information, the data need to be pulled from architectural drawings. Thirdly, by applying image recognition and classification, this can be done by extracting wall and surface information using dimension lines and pixels. All combined, the current review of literature strongly indicates that it is possible to develop automated designs for fire protection utilizing artificial intelligence.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Bulletin of the Korean Chemical Society
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• v.33 no.8
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• pp.2499-2507
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• 2012

A novel and convenient electrochemical method has been developed for sensitive determination of melamine (MEL) using ascorbic acid (AA) as the recognition element. The working electrode employed in this method was modified with the nanocomposite of hydroxyapatite/carbon nanotubes to enhance the current signal of recognition element. The interaction between MEL and AA was investigated by fourier transform infrared spectroscopy and cyclic voltammetry, and the experimental results indicated that hydrogen bonding was formed between MEL and AA. Because of the existing hydrogen bonding and electrostatic interaction, the anodic peak current of AA was decreased obviously while the non-electroactive MEL added in. It illustrated that the MEL acted as an inhibitor to the oxidation of AA and the decreasing signals can be used to detect MEL. Under the optimal conditions, the decrease in anodic peak current of AA was proportional to the MEL concentrations ranging from 10 to 350 nM, with a detection limit of 1.5 nM. Finally this newly-proposed method was successfully employed to detect MEL in infant formula and milk, and good recovery was achieved.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1085-1091
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• 2014

A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.