• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.10
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• pp.469-475
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• 2003

Resistive leakage current flowing ZnO blocks increases with its ages, which is an important indicator of arrester deterioration. However, a complicated circuitry is essential to measure the resistive leakage current included in the total leakage current, and the difficult handling of the measurement makes few applications to the fields. In this paper, we propose a resistive leakage current measurement device which is composed of a current detection circuit and an analysis program operated on a microprocessor. The device samples the input leakage current waveform digitally, and discriminate the zero-cross and the peak point of the waveform to analyze the current amplitude vs. phase. The capacitive leakage current is then eliminated from the total leakage current by using an algorithm to extract the resistive leakage current only. Also, the device can be operated automatically and manually to analyze the resistive leakage current even when the leakage current waveform is distorted due to various types of arrester deterioration. To estimate the performance of the device, we carried out a test on ZnO blocks and lightning arresters. From the results, it is confirmed that the device could analyze most parameters needed for the arrester diagnostics such as total leakage current. resistive leakage current, and the $3^rd$ harmonic leakage current.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.5
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• pp.542-547
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• 2014

When a primary user transmits over some frequency band assigned to it, most of its transmission power tends to be concentrated in the band but a small portion of it spreads into the adjacent bands. Thus the false alarm probability for the adjacent bands can be higher than expected, even though no primary users are active in the bands. A previous work evaluated the effects of the adjacent channel leakage on the performance of an energy detection based spectrum sensing but it did not take into account the fading phenomenon. In this paper, we analyze the effects of the adjacent channel leakage on an energy detection based spectrum sensing scheme in terms of detection probability and false alarm probability in a fading environment.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.433-440
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• 2015

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.5
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• pp.413-417
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• 2010

Line to ground faults by deterioration of insulators has frequently occurred in power system, and the main cause is surface contamination of the insulators. The contamination of insulator is analyzed by monitoring the surface leakage current flowing them. The suspension insulator is monitored by installation of a zero-phase current sensor(ZCT), but the line-post insulator is impossible to apply the same method because of its large diameter structure. This paper proposed a detection method of surface leakage current for a line-post insulator, and it can easily be applied to new and/or built insulators. The leakage current is indirectly calculated from the potential difference between the metal electrode attached on the surface of insulator and the ground connector. To evaluate the performance of the proposed method, the leakage current is compared as a function of contamination condition controlled by the density of NaCl solution. The leakage current is proportioned to the density of NaCl solution, and the voltage detected by the electrode showed the same trend. From the experimental results, we designed and fabricated a monitoring device which is composed of a detection electrode, signal converter, microprocessor, and ZigBee, and its measurement range is $10{\mu}A{\sim}5mA$.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.828-833
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• 2010

Steam leakage is one of the major issues for the structural fracture of pipes of nuclear power plants. Therefore a method to inspect a large area of piping systems quickly and accurately is needed. In this paper, we proposed the method for the detecting steam leakage by using image signal processing. Our basic idea come from heat shimmer which shine with a soft light that looks as if it shakes slightly. To test the performance of this technique, experiments have been performed for simple heat source and steam generator. Results show that the proposed technique is quite powerful in the steam leak detection.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.4
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• pp.299-309
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• 2019

Waterworks facilities inevitably experience some amount of leakage even if there is a lot of investment or state-of-the-art technology that is applied such as DMA(District Metered Area) system construction, leakage detection, repair, pipe rehabilitation, etc. The primary reason is the leakage is naturally restored over time. In the UK, this restoration characteristic is defined as NRR(Natural rate of rise of leakage) and used to decision making for prioritizing active leakage control of DMAs. However, this restoration characteristic is well recognized, but researches on NRR in the water distribution system are insufficient in Korea. In this study, the estimation method of NRR was developed suitable for applicating in Korea considering of SCADA data, water infrastructure, and water usage patterns by modification of the UK's NRR method. The proposed method was applied to 9 DMAs and verified it's applicability by comparing with the other water loss performance indicators. It is expected that the proposed method can be used to support decision making for sustainable NRW(Nor-revenue water) management in the water distribution system.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Journal of Magnetics
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• v.18 no.3
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• pp.357-360
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• 2013

A modified SQI method using magnetic leakage flux (MFL) signal for underground gas pipelines' defect detection and characterization is presented in this paper. Raw signals gathered using MFL signals include many unexpected noises and high frequency signals, uneven background signals, signals caused by real defects, etc. The MFL signals of defect free pipelines primarily consist of two kinds of signals, uneven low frequency signals and uncertain high frequency noises. Leakage flux signals caused by defects are added to the case of pipelines having defects. Even though the SQI (Self Quotient Image) is a useful tool to gradually remove the varying backgrounds as well as to characterize the defects, it uses the division and floating point operations. A modified SQI having low computational complexity without time-consuming division operations is presented in this paper. By using defects carved in real pipelines in the pipeline simulation facility (PSF) and real MFL data, the performance of the proposed method is compared with that of the original SQI.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1044-1053
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• 2022

Time domain reflectometry (TDR) is a diagnostic technique to evaluate the physical integrity of cable and finds application in leak detection and localization of piping system. In this study, a cable-shaped leak detection sensor was proposed using the TDR technique for monitoring leakage detection of ship's engine room seawater piping system. The cable sensor was developed using a twisted pair arrangement and wound by an absorbent material. The availability and performance of the sensor for leak detection and localization were evaluated on a lab-scale pipeline set up. The developed sensor was installed onto the pipes and flanges of the lab-scale set up and various TDR waveforms were acquired and analyzed according to the dif erent variables including the number of twists and sheath thickness. The result indicated that the twisted cable sensor was able to produce clear and smooth signal as compared to the TDR sensor with a parallel arrangement. The optimal number of twist was determined to be above 10 per the unit length. The optimal diameter of sheath thickness that results in the desired sensitivity was determined to be ranging from 80% up to 120% of the diameter of the conductor. The linear regression analysis for estimation of leak localization was carried out to estimate the location of the leakage, and the result was a determination coefficient of 0.9998, indicating a positive relationship with the actual leakage point. The proposed TDR based leak detection method appears to be an effective method for monitoring leakage of ship's seawater piping system.