• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.326-334
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• 2008

The resistivity method has been used to image the electrical properties of the subsurface. Especially, this method has become suitable for monitoring since data could be rapidly and automatically acquired. In this study, we developed a time-lapse inversion algorithm for the interpretation of resistivity monitoring data. The developed inversion algorithm imposes a big penalty on the model parameter with small change, while a minimal penalty on the model parameter with large change compared to the reference model. Through the numerical experiments, we can ensure that the time-lapse inversion result shows more accurate and focused image where model parameters have changed. Also, applying the timelapse inversion method to the leakage detection of an embankment dam, we can confirm that there are three major leakage zones, but they have not changed over time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.417-423
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• 2015

In this study, Magnetic Flux Leakage(MFL)-based inspection system was applied to detect the local fault of wire rope. To verify the feasibility of the proposed damage detection technique, an 4-channel MFL sensor head prototype was designed and fabricated. A wire rope with several types of cross-sectional damages were fabricated and scanned by the MFL sensor head to measure the magnetic flux density of the wire rope specimen. To interpret the condition of the wire rope, magnetic flux signals were used to determine the locations of the flaws. To improve the resolution of signal, the instantaneous variation value of magnetic flux was utilized. Measured signals from the damaged specimen were compared with thresholds set for objective decision making. Finally, the results were compared with information on actual inflicted damages to confirm the accuracy and effectiveness of the proposed cable monitoring method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.763-773
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• 2017

In recent years, personal information leakage and technology leakage accidents are frequently occurring. According to the survey, the most important part of this spill is the 'insider' within the organization, and the leakage of technology by insiders is considered to be an increasingly important issue because it causes huge damage to the organization. In this paper, we try to learn the normal behavior of employees using machine learning to prevent insider threats, and to investigate how to detect abnormal behavior. Experiments on the detection of abnormal behavior by implementing an Autoencoder composed of Recurrent Neural Network suitable for learning time series data among the neural network models were conducted and the validity of this method was verified.

• Journal of Sensor Science and Technology
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• v.23 no.4
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• pp.266-271
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• 2014

Chemical sensors are used in various industrial facilities such high-risk and prevent the leakage of substances, important in life and environmental protection and the safe use of industry, used for management. In particular, high-temperature environments such as power generation equipment of the rotating part due to leakage generated by the various oil, power plants Shut Down, fire, work environment (exposure to various chemical solution and gas leak) and various water, air and soil pollution causes. Thus, over the long term through various channels such as crops and groundwater contamination caused by the slow, serious adverse effect on the ecosystem. In this paper, synthetic lube oil and high pressure hydraulic fluid leakage and immediately detect a new Printed Electronic implementation of technology-based film-type sensors, and its performance test. Thus, industrial accidents and environmental pollution and for early detection of problems, large accidents can be prevented. Experimental results of the synthetic lube oil and high pressure hydraulic fluid solution after the contact time depending on the experiment and the oil solution of the sensor material of the conductive porous PE resistance value by a chemical reaction could be confirmed that rapid increase. Also implemented in the film-type oil sensor electrical resistance change over time of the reaction rate and the synthetic lube oil is about 2 minutes or less, the high pressure hydraulic fluid is less than about 1 minute was. Therefore, more high-pressure hydraulic fluid such as a low volatility synthetic lube oils are the resistance change and the reaction rate was confirmed to be the slowest.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.165-169
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• 2014

Digital load is increasing suddenly for various reasons, such as easy control and management. Accordingly, a consumption pattern of load is becoming DC. However, the power supply is supplied by AC power. The load power supply substantially needs DC power. AC power has to be converted to DC power. Renewable energy sources like solar, wind, fuel cells are DC power generation, but the transfer needs to through by AC power, thus DC power has to be converted to AC power. Resultantly, a multi-stage conversion loss is constantly increasing. The power distribution system of DC-based is required for effective use of these energy sources. This requires a DC load, as well as is necessary to develop DC ELCB which are able to detect DC leakage current for implementing protection. In this study, it realize detection algorithm about DC leakage current to verify the performance of the sensor and apply it to the ELCB which is based on DC. Therefore, it is expected to protect operating of DC power distribution system.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1677-1688
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• 2020

In the sodium-cooled fast reactor, the steam generator is a heat exchange device between sodium and water, which may cause leakage, resulting in a sodium-water reaction accident, which in turn affects the safe operation of the entire nuclear reactor. To this end, the electromagnetic vortex flowmeter is used to detect leakage of the steam generator and its signal processing method is studied in this paper. The hydraulic experiment was carried out by using water instead of liquid sodium, and the sensor output signal of the electromagnetic vortex flowmeter under different gas injection volumes was collected. The bubble noise signal is reflected by the base line of the sensor output signal. According to the relationship between the proportion of the bubble noise signal in the sensor output signal and the gas injection volume, a signal processing method based on the energy ratio calculation is proposed to detect whether the water contains bubbles. The gas injection experiment of liquid sodium was conducted to verify the effectiveness of the signal processing method in the detection of bubbles in sodium, and the minimum detectable leak rate of water in the steam generator was detected to be 0.2 g/s.

• Ocean Systems Engineering
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• v.8 no.1
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• pp.41-55
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• 2018

Accidental oil and gas leak is a critical concern for the offshore industry because it can lead to severe consequences and as a result, it is imperative to evaluate the probabilities of occurrence of the consequences of the leakage in order to assess the risk. Event Tree Analysis (ETA) is a technique to identify the consequences that can result from the occurrence of a hazardous event. The probability of occurrence of the consequences is evaluated by the ETA, based on the failure probabilities of the sequential events. Conventional ETA deals with events with crisp failure probabilities. In offshore applications, it is often difficult to arrive at a single probability measure due to lack of data or imprecision in data. In such a scenario, fuzzy set theory can be applied to handle imprecision and data uncertainty. This paper presents fuzzy ETA (FETA) methodology to compute the probability of the outcomes initiated due to oil/gas leak in an actual offshore-onshore installation. Post FETA, sensitivity analysis by Fuzzy Weighted Index (FWI) method is performed to find the event that has the maximum contribution to the severe sequences. It is found that events of 'ignition', spreading of fire to 'equipment' and 'other areas' are the highest contributors to the severe consequences, followed by failure of 'leak detection' and 'fire detection' and 'fire water not being effective'. It is also found that the frequency of severe consequences that are catastrophic in nature obtained by ETA is one order less than that obtained by FETA, thereby implying that in ETA, the uncertainty does not propagate through the event tree. The ranking of severe sequences based on their probability, however, are identical in both ETA and FETA.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.715-726
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• 2018

Various methods, such as geophysical exploration, temperature measurement, and fiber optics, have been developed for detecting the seepage at a dam. In this study, in order to investigate the possibility of leakage detection using dielectric constant of FDR sensor, a physical model consisting of weak and no-weak zones is fabricated and the sensors for dielectric constant, temperature and pore water pressure measurements are installed. As a leakage happens, the dielectric constant changes more rapidly through a weak zone than no-weak zone. In addition, comparing three factors (dielectric constant, temperature, and pore water pressure), the response of dielectric constant to seepage is fast and it is easily recognized even at the end measurement point. Considering these features, it is concluded that it could be possible to cope with the leakage detection quickly and efficiently if the dielectric constant is measured at the downstream slope of a dam.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.53-60
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• 2018

To diagnosis the local damages of the steel plates, magnetic flux leakage (MFL) method that is known as a adaptable non-destructive evaluation (NDE) method for continuum ferromagnetic members was applied in this study. To analysis the sensitivity according to thickness of steel plate in MFL method based damage diagnosis, several steel plate specimens that have different thickness were prepared and three depths of artificial damage were formed to the each specimens. To measured the MFL signals, a MFL sensor head that have a constant magnetization intensity were fabricated using a hall sensor and a magnetization yoke using permanent magnets. The magnetic flux signals obtained by using MFL sensor head were improved through a series of signal processing methods. The capability of local damage detection was verified from the measured MFL signals from each damage points. And, the peak to peak values (P-P value) extracted from the detected MFL signals from each thickness specimen were compared each other to analysis the MFL based local damage detection sensitivity according to the thickness of steel plate.