• 한국지구물리탐사학회:학술대회논문집
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• 2005.09a
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• pp.11-18
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• 2005

Filament inversion routines are highly effective for target definition whenever total-field DHTEM vectors can be obtained using three-component logging tools. However most cross-hole components contain significant noise related to sensor design and errors in observation of probe rotation. Standard stacking methods can be used to improve data quality but additional statistical methods based on cross-correlation and spatial averaging of orthogonal components may be required to ensure a consistent vector migration path. Apart from assisting with spatial averaging, multiple filaments generated for successive time-windows can provide additional imaging information relating to target geometry and current migration. New digital receiver systems provide additional time-windows to provide better tracking options necessary for high-resolution imaging of this type.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.9-18
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• 2007

There have been continuous efforts for automation of joint tracking system. This automation process is mainly used to do in root pass of gas metal arc welding in the field of heavy industry and shipbuilding etc. For automation, it is important using of vision sensor. Welding robot with vision sensor is used for weld seam tracking on welding fabrication. Recently, it is used to on post-weld inspection for weld quality evaluation. For real time seam tracking, it is very important role in vision process technique. Vision process is included in filtering and thinning, segmentation processing, feature extraction and recognition. In this paper, it has shown performance comparison results of seam tracking for real time root pass on gas metal arc welding. It can be concluded better segment splitting method than iterative averaging technique in the performance results of seam tracking.

• Journal of Mechanical Science and Technology
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• v.14 no.2
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• pp.168-176
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• 2000

Related to the error dynamics of an adaptive system, averaging theorems are developed for coupled differential equations which consist of ordinary differential equations and a parabolic partial differential equation. The results are then applied to the convergence analysis of the parameter estimate errors in the model reference adaptive control of a nonautonomous parabolic partial differential equation with lowly time-varying parameters.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.84-89
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• 2014

Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.262-265
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• 2012

In this paper we propose a new deinterlacing method with block classification and motion vector smoothing. The proposed method classifies a block, then depending on the region it belongs to, the motion estimation or line averaging is applied. To classify a block its variance is calculated. Then, for those blocks that belong to simple non-texture region the line averaging is done while motion estimation is applied to complex region. The motion vector field is smoothed using median filter what yields more accurate interpolation. In the experiments for the subjective evaluation, the proposed method bas shown satisfying results in terms of computation time consumption and peak signal-to-noise ratio. Due to the simplicity of the algorithm computation time was drastically decreased.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.75-84
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• 1994

기계가공프로세스에서 절삭공구의 마멸과 채터진동은 공작기계의 가동율과 생산성을 크게 저해하는 요인이 되고 있다. 본 연구에서는 공구마멸과 채터현상이 혼재하는 상황에서, 이들 두 현상을 동시에 검출하는데, AE 및 가속도센서에서 검출된 신호와 AR계수 및 주파수대역 평균에너지를 특징입력으로 하는 인공신경회로망을 이용하였다. 그 결과, 공구마멸과 채터현상에 대응하는 서로 다른 신호특징의 차이를 동시에 식별하는 데 인공신경 회로망의 유용성을 입증하였으며, 시계열모델의 AR계수(70 .approx. 90%)보다는 주파수대역에너지법의 평균에너지 (80 .approx. 100%)를 신경회로망의 특징입력으로 하는 경우가 높은 성공률을 나타내었다.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.55-63
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• 1998

Among the position sensing methods available, the arc sensor which utilizes the electrical signal obtained from the welding arc itself is one of the most prevalently used methods, because it has an advantage that no particular sensing device is necessary and real-time sensing of a groove position is possible directly under the arc. The authors have already developed a seam tracking system that contains a new arc sensor algorithm, which uses the relative welding current variation according to the tip-to-workpiece distance in GMA welding. In this study a torch height control algorithm for automatic weld seam tracking was proposed for completing the previous system, which uses an on-off control technique. To implement the torch height control algorithm during weld seam tracking the system parameters which include 2nd averaging range, weighting factor for 2nd moving averaging, and Z-directional basic compensation distance were determined by experimental analysis. Finally the two different height control methods, one is simple on-off control and the other on-off control using a reference current value , were compared in their tracking abilities.

• ETRI Journal
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• v.43 no.6
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• pp.966-977
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• 2021

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.

• Proceedings of the KIPE Conference
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• 2008.10a
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• pp.13-15
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• 2008

Charging or discharging secondary batteries such as a lithium-ion battery is essential in the stage of production and takes long time over two hours. And the charge/discharge system is operated with high switching frequency over several tens kHz. Therefore, to simulate such a system in the conventional way takes very long time and huge files are produced. Finally, the simulation would be unable with general PC class. In this paper, the lithium-ion battery charge/discharge system is analyzed by using state space averaging method. As a result, the simulation time is reduced dramatically and the charge/- discharge characteristics of the lithium-ion battery can be observed.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.