• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• ETRI Journal
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• v.38 no.4
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• pp.695-702
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• 2016

A hierarchical approach for fast bi-dimensional empirical mode decomposition (B-EMD) is proposed. The presented approach utilizes an efficient window size determination scheme that enables the multi-level computation of the order statistics filter (OSF). Our detailed experiments show that the proposed OSF computation approach allows a significantly faster computation of an EMD without degrading the decomposition accuracy.

• Proceedings of the KIEE Conference
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• 2008.10a
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• pp.89-90
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• 2008

Pulsed power technologies in variable fields require the pulsed high power sources. We fabricated the pulsed power generator, named EMD Pulse Generator(EPG), by using Marx circuit with 200 kV high, 50 ns fast rise time. In this paper, we described about the performances.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1359-1360
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• 2015

본 논문에서는 FAST 코너점과 EMD를 이용한 다수의 이동물체 식별 알고리즘을 제안한다. 제안하는 방법은 먼저 영상 내의 이동물체를 추출하기 위한 기법으로 GMM을 기반으로 배경을 모델링 하며, 모델링 된 배경에서 추출된 이동물체를 인식하기 위해 라벨링 기법을 수행한다. 그 다음 인식된 다수의 이동물체 식별을 위해 FAST 코너점과 색상 기반의 EMD 알고리즘을 융합한 다수의 이동물체 식별 방법을 제안하며, 최종적으로, 실내 환경 내에서의 실험을 통해 제안한 방법의 응용 가능성을 증명한다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.481-482
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• 2008

We have designed a Marx Generator, named EMD Pulse Generator(EPG), which makes steep front high voltage, 50ns rise time and 200kV high. It was designed as coaxial type and in small size, 70cm high and 20cm in diameter. The firing system is trigatron type and the spark gap switches in each stage are coaxial with the axis of the system. In this paper we mention about the characteristics of EPG and several experimental data.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.121-121
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• 2010

As the industrial technology is getting higher, the pulsed power technology is required from various fields such as thermonuclear fusion energy sources, military applications, electric power distribution, and a variety of new specialized needs. This technology deals with the generation of very high power electromagnetic pulses through fast switching. We fabricated a pulsed power generator, named EMD pulse generator, by using Marx circuit with 200 kV high, 50 ns fast rise time. In this paper, we described about an effect of stray capacitance of coaxial Marx generator, EPG-AM200k, and a comparing the results of experiments and circuit analysis.

• Bulletin of the Korean Chemical Society
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• v.12 no.3
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• pp.309-315
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• 1991

The thermal transport coefficients-the self-diffusion coefficient, shear viscosity, and thermal conductivity-of liquid argon at 94.4 K and 1 atm are calculated by non-equilibrium molecular dynamics (NEMD) simulations of a Lennard-Jones potential and compared with those obtained from Green-Kubo relations using equilibrium molecular dynamics (EMD) simulations and with experimental data. The time-correlation functions-the velocity, pressure, and heat flux auto-correlation functions-of liquid argon obtained from the EMD simulations show well-behaved smooth curves which are not oscillating and decaying fast around 1.5 ps. The calculated self-diffusion coefficient from our NEMD simulation is found to be approximately 40% higher than the experimental result. The Lagrange extrapolated shear viscosity is in good agreement with the experimental result and the asymptotic formula of the calculated shear viscosities seems to be an exponential form rather than the square-root form predicted by other NEMD studies of shear viscosity. The agreement for thermal conductivity between the simulation results (NEMD and EMD) and the experimental result is within statistical error. In conclusion, through our NEMD and EMD simulations, the overall agreement is quite good, which means that the Green-Kubo relations and the NEMD algorithms of thermal transport coefficients for simple liquids are valid.

• Journal of Power Electronics
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• v.19 no.1
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• pp.307-315
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• 2019

Condition monitoring has been recognized as an effective and low-cost method to enhance the reliability and improve the maintainability of power electronic converters. In power electronic converters, high-frequency oscillation occurs during the switching transients of power transistors, which is known as ringing. The ringing frequency mainly depends on the values of the parasitic capacitance and stray inductance in the oscillation loop. Although circuit stray inductance is an important factor that leads to the ringing, it does not change with transistor aging. A shift in either the inside inductance or junction capacitance is an important failure precursor for power transistors. Therefore, ringing frequency can be used to monitor the health of power transistors. However, the switching actions of power transistors usually result in a dynamic behavior that can generate oscillation signals mixed with background noise, which makes it hard to directly extract the ringing frequency. A frequency extraction method based on empirical mode decomposition (EMD) and Fast Fourier transformation (FFT) is proposed in this paper. The proposed method is simple and has a high precision. Simulation results are given to verify the ringing analysis and experimental results are given to verify the effectiveness of the proposed method.