• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.305-316
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• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1888-1894
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• 2009

This paper performs TDR real test for measurement of propagation velocity on #1 Pole of HVDC submarine cable section between Jeju and Haenam, and then measured velocity is compared with theoretical value and velocity provided from manufacturer. The measured velocity is also validated from theoretical process based on CIGRE simplified approach. In this paper, the fault location algorithm using multi-scale correlation of SWT(stationary wavelet transform) and travelling wave is additionally proposed for HVDC submarine cable system, it includes fault signal filter for noise cancellation. Finally, the measured velocity is validated from proposed fault location algorithm test once more.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.651-662
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• 1996

This paper concerns with preventive replacement under periodic inspections for an item (system) which is in a state of preparedness. The item is subject to wear. The item fails randomly but the failure rate depends on the accumulated wear. The item is preventively replaced if it survives a certain wear limit at periodic inspections. The foiled item is, however, replaced at periodic inspections. Given the costs for replacements and inspections, and the penalty cost of the time elapsed between failure und its detection, the optimal wear limit according to the long-run expected cost per unit time criterion is derived. It has been proved that the optimal wear limit is unique if an item has increasing weer-dependent failure rate. A numerical example for a stationary gamma wear process with Weibull distributed failure is given to show its applicability.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.458-466
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• 2012

We have developed a weapon effectiveness evaluation model for top-attack smart munitions(WEEM/TASM), which is a many on many Monte Carlo Model evaluating the effectiveness of top-attack smart munitions against armoured ground vehicles. In this model the battle is reduced to a one-sided battle situation in that the target vehicles are regarded as being stationary and passive. It can simulate the whole attack process of smart munitions from firing artillery dispenser to sensing and hitting processes after dispense. It can also calculate the probability of kill of each target and the numbers of rounds required to fulfill the degree of damage in statistical manners. In this paper, we describe the basis for our design concepts reflected in the model to simulate the weapon effectiveness of top-attack smart munitions and provide simulation results for an example case.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.674-679
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• 1994

Acoustic signal such as speech and scattered sound, are generally a nonstationary process whose frequency contents vary at any instant of time. For time-varying signal, whether a nonstationary or a deterministic transient signal, a traditional frequency domain representation does not reveal the contents of signal characteristics and may lead to erroneous results such as the loss of desired characteristics features or the mis-interpretation for a wrong conclusion. A time-frequency domain representation is needed to characterize such signatures. Pseudo Wigner-Ville distribution (PWVD) is ideally suited for portraying nonstationary signal time-frequency domain and carried out by adapting the fast Fourier transform algorithm. In this paper, the important properties of PWVD were investigated using both stationary and nonstationry signatures by numerical examples PWVD was applied to acoustic sigtnatures to demonstrate its application for time-ferquency domain analysis.

• Ocean Systems Engineering
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• v.2 no.4
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• pp.257-269
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• 2012

This paper provides a practical stochastic method by which the maximum equilibrium scour depth around spherical bodies exposed to long-crested (2D) and short-crested (3D) nonlinear random waves can be derived. The approach is based on assuming the waves to be a stationary narrow-band random process, adopting the Forristall (2000) wave crest height distribution representing both 2D and 3D nonlinear random waves, and using the regular wave formulas for scour and self-burial depths by Truelsen et al. (2005). An example calculation is provided.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Korean Journal of Mathematics
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• v.29 no.2
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• pp.371-386
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• 2021

This paper investigates two discrete time queueing inventory models with positive service time and lead time. Customers arrive according to a Bernoulli process and service time and lead time follow geometric distributions. The first model under discussion based on replenishment of order upto S policy where as the second model is based on order placement by a fixed quantity Q, where Q = S - s, whenever the inventory level falls to s. We analyse this queueing systems using the matrix geometric method and derive an explicit expression for the stability condition. We obtain the steady-state behaviour of these systems and several system performance measures. The influence of various parameters on the systems performance measures and comparison on the cost analysis are also discussed through numerical example.