• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1856-1861
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• 2011

As a secondary suspension, the air spring has not good lateral stiffness characteristics. In order to make up for this weak point, lateral damper is used between bogie and carbody. The lateral vibration of carbody can be reduced by the lateral damper. When the damping force of lateral damper becomes worse, the running stability and ride comfort of the railway vehicle go down. Simultaneously the lateral motion of carbody is increased. In this study, the lateral displacement of carbody was studied by the multibody dynamic analysis in accordance with lateral damping force to find the cause of abnormal noise(impact noise) when the vehicle is running. The suitable lateral damping force was reviewed in order not to generate abnormal noise.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.117-129
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• 2018

In this paper, we suggest a new system that detects abnormal region of skim. First, an illumination elimination algorithm which uses LAB color model is processed on input facial image to obtain robust facial image for illumination, and then gabor filter is processed to detect the reactivity of discontinuity. And last, the density-based spatial clustering of applications with noise(DBSCAN) algorithm is processed to classify areas of wrinkles, dots, and other skin diseases. This method allows the user to check the skin condition of the images taken in real life.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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• pp.171-177
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• 2016

Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.269-270
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.231-232
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.248-249
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.163-165
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• 2013

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3996-4004
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• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.65-66
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• 2010

To improve the gear noise quality, gear tooth grinding machine are widely used in automotive industry. While using the gear profile grinding machine to improve the gear tooth quality of the transmission, several defects such as chattering, tooth waves that cause the gear noise occasionally happened. But it is very difficult to solve that problem, because there is no one who knows the setting up the optimal grinding condition appropriately. The abnormal manufacturing conditions which make the gear noise make the engineer to spend a lot of time, effort, and money. Due to demands for solving the serious abnormal gear noise happened in the new FF 6th stage automatic transmission in the mass product stage, the vibration checking process in the worm wheel axis, work rotation and fixed axis of the grinding machine were adapted to find the root causes. As a result, gear profile wave are affected by the work rotation axis's unbalance which is caused by worm wheel feeding speed. And a primary and the secondary grinding feeding speed, cutting oil, work fixed forces are also proved as the important factors. After setting up the grinding condition reported in this paper, it was adapted successfully to the grinding machine to manufacture the new FF 6th speed automatic transmissions' output gear. The gear noise was dramatically disappeared and the process and results will offer good guides to the engineers who manufacture the gear with the grinding machine.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.7
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• pp.665-671
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• 2010

To improve the gear noise quality, gear tooth grinding machine are widely used in automotive industry. While using the gear profile grinding machine to improve the gear tooth quality of the transmission, several defects such as chattering, tooth waves that cause the gear noise occasionally happened. But it is very difficult to solve that problem, because there is no one who knows the setting up the optimal grinding condition appropriately. The abnormal manufacturing conditions which make the gear noise make the engineer to spend a lot of time, effort, and money. Due to demands for solving the serious abnormal gear noise happened in the automatic transmission in the mass product stage, the vibration checking process in the worm wheel axis, work rotation and fixed axis of the grinding machine were adapted to find the root causes. As a result, gear profile wave are affected by the work rotation axis's unbalance which is caused by worm wheel feeding speed. And a primary and the secondary grinding feeding speed, cutting oil, work fixed forces are also proved as the important factors. After setting up the grinding condition reported in this paper, it was adapted successfully to the grinding machine to manufacture the new automatic transmissions' gear. The gear noise was dramatically disappeared and the process and the results will offer good guides to the engineers who manufacture the gear with the grinding machine.