• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.5
• /
• pp.144-149
• /
• 2000

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.40-45
• /
• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Journal for History of Mathematics
• /
• v.15 no.2
• /
• pp.135-140
• /
• 2002

In this paper, we explain independence and mutually exclusiveness of events. Also we explain independence and uncorrelation of random variables. Finally we compare with differences as examples.

• Electrical & Electronic Materials
• /
• v.9 no.3
• /
• pp.246-250
• /
• 1996

In optical soliton transmission systems with nonlinear dispersive materials, which is utilized for ultra-long and high bit rate transmission, it is shown that the value of initial time difference between adjacent solitons is analyzed for optimum bit rate. The method is inducted by uncorrelation condition with minimum interaction forces in initial covariance coefficient between adjacent solitons. When the initial time difference is 6 times of soliton pulse width by the results, it is shown that the reliability is maintained with more than 90% within transmission length of soliton period. multiplied by 93.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.393-396
• /
• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1996.03a
• /
• pp.119-123
• /
• 1996

The demands for robotic and automatic system are continually increasing in manufacturing fields. There were so many studies to monitor and predict system, but it were mainly relied upon measuring of cutting force, current of motor spindle and using acoustic sensor, etc. In this study digital image of time series sequence was acquired taking advantage of optical technique. Then, mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR model was selected for system model, fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, we found there was a system stability.

• Journal of Communications and Networks
• /
• v.18 no.3
• /
• pp.288-298
• /
• 2016

Proactive relay selection in cognitive radio networks has recently received considerable attention. However, its outage probability analysis is limited to partially-identical fading distributions, uncorrelation among received signal-to-noise ratios (SNRs), and no direct channel. This paper completes this literature deficiency by generalizing the existing analysis for non-identical fading distributions, correlation among received SNRs, and with direct channel. Numerous results demonstrate that relay selection with a direct channel achieves a higher diversity order and superior performance than that without a direct channel at virtually no cost of power and bandwidth. Further, proactive relay selection suffers an error floor at either a large maximum transmit power or large maximum interference power; however, the error floor level can be significantly remedied with an increase in the number of relays.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.6
• /
• pp.988-994
• /
• 1997

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, digital image of time series sequence was acquired by taking advantage of optical technique. Mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR(auto regressive) model was selected for system model and fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, it was found that there was a system stability.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.151-158
• /
• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.