• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.2
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• pp.138-142
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• 2013

As an early warning system to reduce the damage of aquacultured mollusks due to low salinity water, we investigated the possibility of a biomonitoring system measuring the shell valve movement (SVM) of Pacific oyster (Crassostrea gigas) by using the Hall element sensor. In high salinity water of 27 psu, SVMs of Pacific oyster showed spikes which mean a relatively fast closing condition after opened condition of average 10-15 mm, and then the SVM showed back to opening condition slower than closing speed. In water salinity of 20-27 psu, the SVMs were similar to that of 27 psu. However, below 17 psu, it showed abnormal valve movements such as spending more time for shell closure. In 10 psu, we could not detected SVMs due to closed condition during experiment periods. Thus, if we quickly detect abnormal environmental variations like low salinity using bio-monitoring of SVM, it may be contribute to increased productivity by dramatically reducing damages in aquaculture.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1996.10a
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• pp.176-182
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• 1996

This paper describes an empherical equation which is to predict the engine condition of the supersonic aircraft. The equation, which is a function of running time of engine and engine oil, is derived from the trend analysis of JOAP data. Qualitative analysis is carried out to make up for the weak points in the current JOAP system. Also wear debris collected from the abnormal engine is analyzed by EDS to detect the damaged parts of engine.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.05b
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• pp.65-69
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• 2000

The crimping connection is a permanent connection that maintains mechanical and electrical property for a long time by crimping two conductors. In this paper, we have done a basic study to make a decision the normal or abnormal condition depending on crimping. By using PZT piezo-sensor, we have compared and analyzed crimping waveforms of abnormal conditions(core ommiting, cover biting) at the normal crimping height. And hence the normal or abnormal condition of crimping connections in real time could be determined by comparison of crimping waveforms in the cases of normal crimping, core omitting and cover biting.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.75-86
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• 2019

Purpose: It is essential for the steel industry to produce steel products without unexpected downtime to reduce costs and produce high quality products. A hot strip rolling mill consists of many mechanical and electrical units. In condition monitoring and diagnosis, various units could fail for unknown reasons. Methods: In this study, we propose an effective method to detect units with abnormal status early to minimize system downtime. The early warning problem with various units was first defined. An autoencoder was modeled to detect abnormal states. An application of the proposed method was also implemented in a simulated field-data analysis. Results: We can compare images of original data and reconstructed images, as well as visually identify differences between original and reconstruction images. We confirmed that normal and abnormal states can be distinguished by reconstruction error of autoencoder. Experimental results show the possibility of prediction due to the increase of reconstruction error from just before equipment failure. Conclusion: In this paper, hot strip roughing mill monitoring method using autoencoder is proposed and experiments are performed to study the benefit of the autoencoder.

• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Welding and Joining
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• v.20 no.2
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• pp.102-108
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• 2002

Resistance spot welding of aluminum alloys is based upon Joule heating of the components by passing a large current in a short duration. Since aluminum alloys have the potential to replace steels fur automobile body assemblies, it is important to study the process robustness of aluminum spot welding process. In order to evaluate the effects of process parameters on the weld quality, major process variables and abnormal process conditions were selected and analyzed. A newly developed two-stage, sliding-level experiment was adopted fur effective parameter design and analysis. Suitable ranges of welding current and button diameters were obtained through the experiment. The effects of the factors and their levels on the variation of acceptable welding current were considered in terms of main effects. From the results, it is concluded that any abnormal process condition decreases the suitable current range in the weld lobe curve. Pareto analysis of variance was also introduced to estimate the significant factors on the signal-to-noise (S/N) ratio. Among the six factors studied, fit-up condition is found to be the most significant factor influencing the SM ratio. Using a Pareto diagram, the optimal condition is determined and the SM ratio is significantly improved using the optimal condition.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.667-672
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• 2013

This paper presents the development of a condition monitoring system that monitors the operating conditions of a reduction unit, such as the bearing temperature, gearbox vibration, and gear oil deterioration, and notifies the operator of potential problems or abnormal conditions. A series of field tests on high-speed rail and conventional lines was performed to identify the characteristics of temperature rise and vibration levels on the reduction unit during operation. The monitoring system was designed based on the proper sensor selection, measurement method, and signal analysis to optimize the interface with the operating system of high-speed trains. Application of this monitoring system to high-speed trains will play an important role in their proper maintenance and safe operation.

• The Journal of Internal Korean Medicine
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• v.22 no.4
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• pp.547-555
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• 2001

Objectives: Obesity is regarded as the aggregation of needless risk factors, for instance, cardiovascular disease, joint disease, induce cancer. We studied on interrelation between abnormal liver function and obesity. Methods: We analyzed liver function, T.Cholesterol, Triglyceride before and after lose weight treatment. The collateral condition is over 6 weeks period on obesity treatment, no liver injury and no complicated another disease on personal past history and found out abnormal impression on biochemical liver function blood test. Results: The improvement rate of LFT, compare with before treatment is 10.6% in T.Bilirubin, 11.1% in ALP, 21% in AST, 38% in ALT, 37.3% in r-GTP, 9.2% in LDH and decreased 2.7% in T.Protein, increased 2.3% in Albumin. Hyperlipidemia is 19.4% in T.Cholesterol, 42.5% in Triglyceride. Conclusions: LFT and Hyperlipidemia of abnormal liver function patients, caused by obesity, is improved to normal limit in proportion to reduce patient's weight.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2927-2930
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• 2003

This paper has been studied the ballast for multiple lamps. Many fluorescent lamps are demanded in where industrial buildings and public buildings. Using the proposed ballast for multiple lamps, we can decrease the cost and the space for installing. Also in the paper, the protection circuit is introduced for over voltage and current. The general problem of the ballast for multiple lamps is to shut down the ballast circuit in the ease of one abnormal lamp. But the proposed method is not shut down all the circuit even if one lamp is in an abnormal condition.