• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.8 no.3
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• pp.7-12
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• 2012

Steam generator tubes are critical boundary of the primary and secondary side in nuclear power plants. Eddy current testing is commonly used as the method of non-destructive testing for the safety and integrity of steam generator tubes in the nuclear power plants. Changes in the geometric shape act as a stress concentration factor likely to cause a defect during the steam generator operation. The mixed-signals with the geometric shape are distorted and attributes that are difficult to detect signals. An example is bending stress due to compression process at a U-bend occurring in the intrados region which has a small radius of curvature. The resulting change in the geometric shape may lead to a dent like occurrences. The dent can cause stress concentration and generates stress corrosion cracks. In this study, the steam generator tubes of nuclear power plant were selected to study for analysis of mixed-signal containing dent and stress corrosion cracks.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.28-35
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• 2022

This study is aimed to develop a safety diagnosis system for fire pumps that detects normal and abnormal signals for the stable operation of the system. Hence, the following activities were carried out: first, a threshold value was identified for the normal operation and six abnormal operations (adherence of impeller, absence of water source, separation of pump and motor, run-stop operation, air inflow into the casing, and reverse-phase loss of the power line) reflecting changes in the current, flow and pressure of fire pumps; secondly, based on the identified signals, an algorithm capable of detecting three abnormal signals was developed and in terms of hardware, a current, pressure and flow sensor suitable for the analogue input values of NI-6009 was designed and installed. This combination of the hardware and software is applicable as a diagnosis system to ensure the stable operation of fire pumps.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.5 s.347
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• pp.1-6
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• 2006

By comparing video-overlay method where digital and SCM signal are modulated in different optical source, we experimentally investigated the performance of simultaneous modulation of data and broadcasting signals using a single LD for broadcasting in PON based optical access network. The experimental results show that by increasing the minimum value of injected current for LD modulation above about 1.5 times of threshold current of LD, data and broadcasting signals can modulate without mutual interference. We also proposed and demonstrated the scheme of optical transmitter optimized for simultaneous modulation of data and broadcasting signals where anode and cathode of LD are separately used as signal interface for data and broadcasting signals, respectively.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.1-7
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• 1999

In power transformers, vibration signals can occur at winding and core due to the change of current, voltage, and temperature and the deformation of winding and core. The deformation of winding and core occurs electromagnetic force induced by fault current in power systems. There firem the changes of vibration signals can be very different in normal or fault states of power transformers. We edtect and analyze the changes of vibration signals and use them as a tool for fault diagnosis of power transformers. This paper presents fault discriminating polliblility using the changes of fundamental waves and higher harmonics in power transformers. We showed the fault discriminating functions that are made at each case ; normal state and fault state. These functions are tested by the detected vibration signals, and we showed that the proposed method can discriminate the state of power transformers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.155-162
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• 2004

This paper concentrated on lip synchronization (lip sync) test for DTV with respect to audio and video signals using a finite digital bitstream In this paper, we propose a new lip sync test method which does not effect on the current program by use of the transient effect area test signals (TATS) and audio-video time index lip sync test signals (TILS).the experimental result shows that the time difference between audio and video signal can be easily measured by captured oscilloscope waveform at any time.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.127-131
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• 1999

In this paper, using a wide-band AE sensor with the frequency range from 100[kHz], the frequency spectra of AE signals generated from the corona discharges of the needle-plane electrode was analyzed to determine the proper ultrasonic sensor. We also examined the relationship between the magnitude of corona discharge and the magnitude of AE signals in peak-to-peak value under the application of 60[Hz] AC high-voltage in oil. From these results, the main frequency spectra of AE signals emitted from the corona discharges of the needle-plane gap were found to be 130[kHz] by the fast fourier transform. The magnitude of AE signals was proportional to the magnitude of corona discharge and discharge current pulse with increasing the applied voltages. Also the detection of corona discharge point location by AE signals was found to be possible by using two sensors.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1243-1257
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• 2019

As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.195-201
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• 2016

Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.132-140
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• 2019

In this paper, we propose a classification method for radar signals depending on the type of threat by applying machine learning to parameter data of radar signals. Currently, the army uses a library of mapping relations between the parameters and the types of threat to recognize threat signals. This approach has certain limitations when classifying signals and recognizing new types of threat or types of threat that do not exist in the current libraries. In this paper, we propose an automatic radar signal classification method depending on the type of threat that uses only parameter data without a library. A convolutional neural network is used as the classifier and machine learning is applied to train the classifier. The proposed method does not use a library, and hence, can classify threat signals that are new or do not exist in the current library.