• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.1 s.94
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• pp.53-62
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• 2005

This study presents the NDE (non-destructive evaluation) technique for detecting the loosened bolts on joint steel structures on the basis of TOF (time of flight) and amplitudes of Lamb waves. Probabilistic neural network (PNN) technique which is an effective tool for pattern classification problem was applied to the damage estimation using PZT induced Lamb waves. Two kinds of damages were introduced by dominant damages (DD) which mean loosened bolts within the Lamb waves beam width and minor damages (MD) which mean loosened bolts out of the Lamb waves beam width. They were investigated for the establishment of the optimal decision boundaries which divide each damage class's region including the intact class. In this study, the applicability of the probabilistic neural networks was identified through the test results for the damage cases within and out of wave beam path. It has been found that the present methods are very efficient and reasonable in predicting the loosened bolts on the joint steel structures probabilistically.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.12
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• pp.815-821
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• 2015

Maritime safety has been more critical recently due to the occurrence of shipboard accidents involving prime movers. As such, the propulsion shafting design and construction plays a vital role in the safe operation of the vessel other than focusing on being cost-efficient. Smaller vessels propulsion shafting system normally install high speed four-stroke diesel engine with reduction gear for propulsion efficiency. Due to higher cylinder combustion pressures, flexible couplings are employed to reduce the increased vibratory torque. In this paper, an actual vibration measurement and theoretical analysis was carried out on a propulsion shafting with V18.3L engine installed on small car-ferry and revealed higher torsional vibration. Hence, a rubber-block type flexible coupling was installed to attenuate the transmitted vibratory torque. Considering the flexible coupling application factor, reduction gear stability due to torque variation was analyzed in accordance with IACS(International Association of Classification Societies) M56 and the results are presented herein.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.1
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• pp.44-49
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• 2002

In recent years, diagnostic techniques have been investigated to detect a partial discharge(PD) in a high voltage electrical equipment. Because PD signal is very sensitive and difficult to suppress strong noises such as narrow-band radio frequency noise and random noise, the accuracy and credibility of PD measurement might be influenced by surrounding interference. Using the technique of PD detection by electromagnetic wave, we have studied the characteristics of both PD and substation-in interference signal. Also, we propose a wavelet packet transform based technique to perform a feature extraction from the interference and PD signal and a classification of the extracted features. The results show that time-frequency characteristics between PD and interference can be obviously distinguished. It is helpful for the development of the insulation diagnosis technique.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.175-178
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• 2004

선박의 수중방사소음은 다양한 기계류나 추진기 혹은 선체와 유체간의 상호 작용으로 인하여 여러 형태의 특성신호로 나타나게 된다. 이는 선박의 운용조건, 장비 회전특성 및 내부구조에 따라 스펙트럼상에 상이한 주파수로 확인됨은 물론, 신호의 출현 형태에도 다양성을 보이고 있다. 일반적으로 선박소음은 속력 종속적인 추진 계통 성분과 비종속적인 보기류 신호로 구분되나 다수의 신호성분이 혼재되어 발생기원을 분류하는 것은 복잡한 과정을 거쳐야 한다. 본 연구에서는 이러한 점을 해결하기 위해 선박의 Tonal성 신호를 자동으로 탐지하고 분류하기 위해 규준화된 스펙트로그램 상에서 연속되는 신호에 가중치를 주어 지속성 신호여부를 판별한 후에 정해진 임계치를 초과하는 성분을 Tonal로 선정하였다. 선정된 Tonal에 대해 주파수선의 대역특성 및 시간 변동성에 대한 패턴인식 방법을 적용하여 Tonal의 발생기원이 속력 종속/비종속적인지를 자동으로 판별하는 알고리즘의 유용성에 대한 결과를 기술하였다.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.1
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• pp.14-19
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• 2017

Fuel oil consumption cost varies depending on every ship operation and this roughly amounts to 70 % of shipping companies' total revenue. As such, efforts towards improved fuel economy are being pursued. An annual 1 % reduction in fuel consumption is perceived to result in saving tens million US dollars on the global fleet operation. One approach is the application of power take-off configurations which are seen to increase fuel oil economy and are suitable for power generation. In this study, the dynamic properties of a shaft generator coupled on a 10S90ME main engine of an 18 600 TEU container vessel is presented. The vibratory torque and angular velocity variation was examined through theoretical analysis and actual vibration measurement. The result of the study suggests a review on existing classification rules for generator design and the lowering of vibratory torque and angular velocity variation guideline.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.224-229
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• 2011

In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.