• Korean Journal of Medicinal Crop Science
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• v.21 no.4
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• pp.289-295
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• 2013

This study was carried out to investigate the cause of leaf discoloration occurring frequently in paddy cultivation. Chemical property of soil and inorganic nutrient component of leaf were analyzed on abnormal fields of 7 regions where leaf discoloration occurred severely and normal fields of 7 regions among ginseng garden. The pH of abnormal fields was strong acidic condition (pH 5.51) compare to normal fields of slightly acid condition (pH 6.42). Calcium and magnesium content in abnormal fields were lower distinctly than that of normal fields, while EC, organic matter, phosphate, and potassium content showed not distinct difference between abnormal and normal fields. Whereas calcium and magnesium content were distinctly high in normal fields, both of potassium and iron content of ginseng leaf were distinctly high in abnormal fields. In particular, iron content of abnormal fields was more 1.94 times in soil, and 3.03 times in leaf than that of normal fields. In soil chemical property, there were significant negative correlation between leaf discoloration ratio and soil pH, and there were also significant positive correlation between leaf discoloration ratio and iron content. In ginseng leaf, there were highly significant negative correlation between leaf discoloration ratio and calcium content, and there were also highly significant positive correlation between leaf discoloration ratio and iron content.

• Journal of Power System Engineering
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• v.17 no.4
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• pp.17-22
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• 2013

Diesel generator of nuclear power plant has a role for supply of emergency electric power to protect reactor core system in event of loss of off-site power supply. Therefore diesel generator should be tested periodically to verify the function that can supply specified frequency and voltage at design power level within limited time. For this purpose, appropriate maintenances in case that abnormal conditions were found are required in allowed time. In this paper, results of development of engine condition diagnosis technology and study on power plant of its technology for diesel generator are described.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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• pp.125-129
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• 2004

For the diagnosis of compressor abnormal condition in air-conditioner, AE signal which is derived from wear condition, compressed air and assembly error is analyzed experimentally. The burst and continuous type AE signal occurred by metal contact and compressed air and AE raw signal of compressors were directly acquired in production line. After extracting samples according to waveforms, Early Life Test(ELT) is conducted and classified to normal and abnormal waveform. The efficient parameters of waveform pattern are investigated in time and frequency domain and the diagnosis algorithm of air-conditioner by Neural Network estimation is suggested.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.207-215
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• 2018

In this study, targeting KOSPI and KOSDAQ listed companies, the relationship between the abnormal investment of companies and analyst earnings forecasts was empirically analyzed. The analysis period of this study spanned from 2003 to 2015 (with that of dependent variables spanning from 2004 to 2016) based on the variables of interest, and among the companies whose earnings per share forecasts were announced by financial analysts, the final sample of 4,917 companies/year that meets the research condition was selected as the target analysis. The results of the empirical analysis are as follows. First, it turned out that the more total abnormal investment, abnormal R&D and abnormal CAPEX investment, the more accurate were analyst earnings forecasts. Second, the more total abnormal investment, abnormal R&D, abnormal CAPEX investment, the more pessimistic analyst earnings forecasts tended to be. Further analysis has shown that these results came more from over investment groups than under investment groups. The results of this study are expected to make additional contributions to the existing studies in that the abnormal investment is considered as a determinant of analyst earnings forecasts.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.123-129
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• 2005

The unbalance of the power output, noise, and vibration is happened by the disproportionate pressure variation in the cylinder. For this reason, decrease of the pressure in the cylinder and increase of the residual gas effect on the engine performance. If the abnormal combustion is continued, the crack would be occurred in the engine block. And it could be broken down. For the normal combustion of the SI engine, it is important to supply the balanced mixture by each operating condition. In this study, it was tested the combustion characteristics in the cylinder according to the abnormal adjusting of intake & exhaust valve. This test is willing to set a basic data's analysis fur developing an automotive diagnosis system by analyzing the pressure in the cylinder, the output signal of MAP sensor, the exhaust gas, etc.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.332-333
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• 2018

This paper presents an advanced abnormal over-current protection with $SuperFET^{(R)}$ 800V MOSFET in Flyback converter. In advanced abnormal over-current protection, digital pattern generator is proposed to detect a steep di/dt current condition when secondary rectifier diode or the transformer is shorted. If current sensing signal is larger than current limit during consecutive switching cycle, Gate signal will be stopped for 7 internal switching periods. If the abnormal over-current maintains pattern, the controller goes into protection mode. The Advanced over-current protection has been implemented in a 0.35um BCDMOS process (ON Semiconductor process).

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.47-54
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• 2015

The most important function in distribution automation system (DAS) is the service restoration. KEPCO's current service restoration provides a very effective restoration service. However, it has been developed without the consideration of unexpected abnormal conditions that may occur while processing the sequence of switching operations. The objective of this paper is to provide practical service restoration schemes under consideration of abnormal conditions. The proposed service restoration schemes have been integrated to a branch office (B/O) in KEPCO. The proposed method strongly supports the conventional service restoration and adds to its value.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1233-1242
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• 2017

Various attempts have been made to prevent crime risk. One of the cases where outdoor pedestrians are attacked by criminals is the abnormal health condition. When a mental or mental condition that can not sustain normal walking due to drunkenness is exposed, the case of being a crime is revealed through crime case analysis. In this study, we propose a method for estimating the state of an individual that can be detected in outdoor activities. In order to avoid the inconvenience of installing a separate terminal for event information transmission of sensors and sensors, it is possible to estimate an abnormal state by using a 3-axis acceleration sensor built in a smart phone. The state of the user can be estimated by analyzing the momentum of the user and analyzing it with the passage of time. It is possible to distinguish the flow of time at regular intervals, to recognize the activity patterns in each time band, and to distinguish between normal and abnormal. In this study, we have evaluated the total amount of kinetic energy and kinetic energy in each direction of the acceleration sensor and the Fourier transformed value of the total energy amount to distinguish the abnormal state.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.607-610
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• 2009

The abnormal combustion in the running engine results to knocking which increases the pressure and temperature in the cylinder, thereby decreasing the generated power by reducing the thermal efficiency. When the temperature and pressure in the cylinder increased rapidly by knocking, abnormal combustion takes place and the engine power is decreased. To investigate the knocking phenomenon, accelerometers are installed in the cylinder head to monitor and diagnose the vibration signal. As method of signal analysis, the time-frequency analysis method was adapted for acquisition of vibration signal and analyzes engine combustion in the short time. In this experiment, after analyzing time data which is stored in the signal recorder in one unit work (4 strokes: 2 revolutions), the signal with frequency and Wavelet methods with extracted one engine combustion data was also analyzed. Then, normal condition with no knocking signal is analyzed at this time. Hereafter, the experiments made a standard for distinguishing normal and abnormal condition to be carried out in acquisition of vibration signal at all cylinders and extracting knocking signal. In addition, analyzing methods can be diverse with Symmetry Dot Patterns (SDP), Time Synchronous Average (TSA), Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP) in the cold test [2]. With signal processing of vibration from engine knocking sensor, the authors adapted a part of engine /rotor vibration analysis and monitoring system for marine vessels to prevent several problems due to engine knocking