• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.389-395
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• 2012

In this paper, we analyzed the frequency spectrum of the electromagnetic waves radiated by an electric discharge as a basic study to develop an on-line diagnostic technique for power equipment installed inside closed-switchboards. In order to simulate local and series arc discharges caused by an electric field concentration and poor connections, three types of electrode systems were fabricated, consisting of needle and plane electrodes and an arc generator meeting the specifications of UL 1699. The experiment was carried out in an electromagnetic anechoic chamber, and the measurement system consisted of a PD free transformer, a loop antenna with a frequency bandwidth of 150 kHz-30 MHz, an ultra log periodic antenna with a frequency bandwidth of 30 MHz-2 GHz, and an EMI test receiver with a frequency bandwidth of 3 Hz-3 GHz. According to the experimental results, the frequency spectra of the electrical discharges were widely distributed across a range of 150 kHz-400 MHz, depending on the defects, while commonly found between 150 kHz and 10 MHz. Therefore, considering the ambient noise and antenna characteristics, the best frequency bandwidth for a measurement system to monitor abnormal conditions by detecting electromagnetic waves in closedswitchboards is 150 kHz-10 MHz.

• ETRI Journal
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• v.37 no.4
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.297-305
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• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.335-340
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• 2004

This study was conducted to evaluate the internal quality of Korean red ginseng using VIS/NIR transmittance spectra. To classify the internal qualities, partial least squares(PLS) regression was conducted. The main results are as follows: To develop the PLS model, several wave bands were divided and incorporated into the model. Among the bands, the wavelength range of 550-1,020nm, excluded noise signal, showed the best evaluation results. Effect of step size on the performance of quality evaluation showed optimal at 15 steps. In order to enhance the accuracy of quality evaluation, the abnormal spectrum shape was considered first and then the PLS model was applied. Among the 150 samples, 12 samples were evaluated by the spectrum shape. In this study, to develop the optimal PLS regression model, among the 150 samples, 138 samples was used with exception of 12 samples which could evaluate the spectrum shape. The result of quality evaluation was promising as SEC and correlation coefficient were 1.09 and 0.967, respectively, and SEP and correlation coefficient were 1.04 and 0.958, respectively.

• Journal of Power System Engineering
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• v.18 no.6
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• pp.113-119
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• 2014

Due to the development of the petroleum refining technology and continuously increased demand from markets, a quantity of gasoline and diesel oil produced from a restricted quantity of crude oil has been increasing, and residual fuel to be used at marine diesel engines has been gradually becoming low quality. As a result, it was recently reported that trouble oils which cause abnormal combustion such as knocking with extreme noise and misfire from internal combustion engines were increasing throughout the world. In this study, an author investigated ignitability and combustion quality by using combustion analyzer with constant volume(FCA, Fuel Combustion Analyzer) and middle speed diesel engine about MDO(Marine Diesel Oil), HFO(Heavy Fuel Oil), LCO(Light Cycle Oil) and Blend-HFO which was blended LCO of 1000 liters with HFO of 600 liters. Moreover, for betterment of ignitability and combustion quality of injected fuels, multi-injection experiment was carried out in the diesel engine using Blend-HFO. According to the results of FCA analysis, ignitability and combustion quality was bad in the order of MDO

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.4
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• pp.391-396
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• 2023

Recently, as power and electronic devices have increased in frequency and capacity, it has become a major concern to protect electronic circuits and electronic components used in these devices from abnormal voltages such as various surges and pulse noise. To respond to variously rated voltages applied to power electronic devices, the rated voltages of various varistors can be obtained by controlling the size of internal particles of the varistor or controlling the number of layers of the varistor. During bonding, the problem of unbalanced thermal runaway occurring between the electrode and the varistor interface causes degradation of the varistor and shortens its life of the varistor. In this study, to solve the problem of unbalanced heat distribution of stacked varistors to adjust the operating voltage, the contents of the ZnO-based varistor composition were 96 wt% ZnO, 1 mol% Sb₂O₃, 1 mol% Bi₂O₃, 0.5 mol% CoO, 0.5 mol% MnO, and 1 mol% TiO₂. A multi-layered ZnO varistor was modeled by bonding a single varistor with a composition in three layers according to the operating voltage. The thermal distribution of the triple-layered ZnO varistor was analyzed for the thermal runaway phenomenon that occurred during varistor operation using the finite element method according to Comsol 5.2.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.700-703
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• 2021

Infrared cameras are widely used in recent research for automatic monitoring the abnormal behaviors of the pig. However, when deployed in real pig farms, infrared cameras always get polluted due to the harsh environment of pig farms which negatively affects the performance of pig monitoring. In this paper, we propose a real-time noise-robust infrared camera-based pig automatic monitoring system to improve the robustness of pigs' automatic monitoring in real pig farms. The proposed system first uses a preprocessor with a U-Net architecture that was trained as a GAN generator to transform the noisy images into clean images, then uses a YOLOv5-based detector to detect pigs. The experimental results show that with adding the preprocessing step, the average pig detection precision improved greatly from 0.639 to 0.759.

• Journal of the Korean Geotechnical Society
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• v.33 no.11
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• pp.21-34
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• 2017

The ground penetrating radar (GPR) signal can be measured with different amplitudes according to the directivity, so the directivity of the antenna should be considered. The objective of this study is to investigate the directivity of antenna by analyzing the reflection characteristics of electromagnetic waves radiated from the antenna, and to evaluate effective range of angle that can inspect an abnormal area according to the directivity of antenna. For the measurement of the directivity, a circular metal bar is used as reflector and the signals are measured by changing the angle and the distance between reflector and antenna in the E- and H-plane. The boundary distance between the near field and the far field is determined by analyzing the amplitudes of reflected signals, and two points with different distances from each of near and far fields are designated to analyze radiation patterns in near and far fields. As a result of radiation pattern measurement, in the near field, minor lobes are observed at angle section at more than $50^{\circ}$ in both E- and H-plane. Therefore, antenna has the directivity for the direction of main lobe and minor lobes in near field. In the far field, antenna has the directivity for a single direction of main lobe because minor lobes are not observed. The amplitude of the signal reflected from the near field is unstable, but it can be distinguished from noise. Therefore, in the near field, the ground anomaly can be detected with high reliability. On the other hand, the amplitude of the signal reflected from the far field is stable, but it is hard to distinguish between reflected signal and noise because of the excessive loss of electromagnetic wave. The analyses of directivity in the near and the far fields performed in this study may be effectively used to improve the reliability of the analyses of abnormal area.

• Progress in Medical Physics
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• v.10 no.3
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• pp.125-131
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• 1999

T$_1$/T$_2$ weighted images are being used to give the characteristic contrast among the various tissues and the norma;/abnormal tissues. Abnormalities in tissues, in general, accompany the biochemical changes and eventually structural ones in which results in the change in T$_1$ and T$_2$ relaxation times of water protons. It has been suggested that the mapping of T$_1$/T$_2$ values may serve as a possible tool for the quantitative evaluation of the degree of abnormality. On reconstructing T$_1$/T$_2$ maps(or any other MR parametric map), only corresponding variables are to be varied, such as TE for T$_2$, TI or TR for T$_1$ and b-factor for diffusion images. But often the receiver gain is taken for the optimal usage of A/D converter, so that the set of the image data has different receiver gain. It must be corrected before any attempt to reconstruct the maps. Here we developed method of correcting receiver gain variation effect, using the standard deviation of noise on individual image. The resultant T$_1$ and T$_2$ values were very comparable to the other reported values.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.