• Transactions on Electrical and Electronic Materials
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• v.7 no.5
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• pp.252-256
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• 2006

When a partial discharge takes place at the stator of a generator, the electrical pulse will propagate along the stator bars and the capacitor chains formed by the end part of the stator winds. On the first path, the pulse propagates as a travel wave at slow speed. On the second path, the pulse propagates at quick speed. Based on the data of the experiments on a real 50 MW steam generator, the author has found the pulses can propagate by magnetic field of the stator winding. It was studied that how to locating the partial discharge by signals coming from the different paths, including the features of signals on the two paths at time domain and frequency domain, the measurement frequency rang of the signals, the blind area, the advantage and disadvantage of this method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.82-86
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• 2004

A fuzzy rule-based hand-motion estimation algorithm is proposed for a 6 dimensional spatial tracker in which low cost accelerometers and gyros are employed. To be specific, beginning and stopping of hand motions needs to be accurately detected to initiate and terminate integration process to get position and pose of the hand from accelerometer and gyro signals, since errors due to noise and/or hand-shaking motions accumulated by integration processes. Fuzzy rules of yes or no of hand-motion-detection are here proposed for rules of accelerometer signals, and sum of derivatives of accelerometer and gyro signals. Several experimental results and shown to validate our proposed algorithms.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1207-1210
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.883-884
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• 2008

This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

• KIEE International Transactions on Electrophysics and Applications
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• v.2C no.6
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• pp.297-303
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• 2002

We have developed an on-line ultrasonic detector to monitor partial discharge in an operating transformer. The ultrasonic sensor has 150[KHz] resonance frequency and contains a pre-amplifier with 60[㏈] gain. The on-line ultrasonic detector has 50~300[KHz] frequency band-pass filter to remove electrical and mechanical noises from the transformer. This detector has an ultrasonic signal discrimination algorithm which discriminates ultrasonic signals due to partial discharge in a transformer. A moving average method of ultrasonic signal number was employed to effectively monitor the increasing trend of the partial discharge. This paper describes an experience of partial discharge detection in a 154[㎸] operating transformer using an ultrasonic detector. With regards to gas analysis in oil, C2H2 gas was produced with a warning level in this transformer We detected ultrasonic signals on the transformer steel wall, and estimated the position of partial discharge. With further inspection, we found carbonized marks due to partial discharge on the supporting bolt which fastens the windings.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1995.10a
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• pp.85-95
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• 1995

This paper describes a diagnosis of transformer by ultrasonic signal due to partial discharge. The ultrasonic signal can be measured when partial discharge is generated in a real transformer. The trend of partial discharge in transformer was estimated by counting the number of ultrasonic signals until insulation paper in the point-to-plane electrode is destroyed. The number of ultrasonic signals is closely related to the number of the electrical signals by partial discharge. The trend of the ultrasonic signal number could easily be distinguished by taking moving average.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.04a
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• pp.1-4
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this anslysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.575-578
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• 2003

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.