• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.301-303
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• 2004

In this work, we develop on-line traffic prediction algorithm for real-time VBR traffic. There are a number of important issues: (i) The traffic prediction algorithm should exploit the stochastic characteristics of the underlying traffic and (ii) it should quickly adapt to structural changes in underlying traffic. GOP ARIMA model effectively addresses this issues and it is used as basis in our bandwidth prediction. Our prediction model deploy Kalman filter to incorporate the prediction error for the next prediction round. We examine the performance of GOP ARIMA based prediction with linear prediction with LMS and double exponential smoothing. The proposed prediction algorithm exhibits superior performam againt the rest.

• Journal of the Korean Society of Safety
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• v.6 no.3
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• pp.40-49
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• 1991

To better understand the phenomena inside an absorber, where heat and mass transfer coexist, this study selected the most baslc absorption model from in whifh water vapor is absorbed on the surface of a stationary lithium bromide absurbent solution. The purpose of this study is to obtain basic knowledge of heat and mass transfer processes as affected by Marangoni convection induced by addition of surfactant. We apply a non-flowing bulk absorption model and assume that dropwise surfactant is fixed on the absorbent surface. Four governing equation-continuity, laminar monentum, energy and diffusion are solved numerically to obtain temperature and concentraion distributions during steam absorption with Marangonl convection.. In conclusion, Sh and Nu genrally increase at the initial absorption stage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1870-1877
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• 2012

Transient signals generally have short duration and variable length with time-varying and non-stationary characteristics. Thus frame-based pattern matching method is useful for classification of transient signals. In this paper, we propose a new method for classification of underwater transient signals using a Gaussian mixture model(GMM). We carried out classification experiments for various underwater transient signals depending upon the types of noise, signal-to-noise ratio, and number of mixtures in the GMM. Experimental results have verified that the proposed method works quite well for classification of underwater transient signals.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.453-458
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• 2002

A recently developed Hilbert-Huang transform (HHT) technique is applied to the detection of the damage locations of bridge structures. The HHT may be used to identify the locations of damages which exhibit nonlinear and non-stationary behavior, since the instantaneous frequency characteristics of the measured signal can be analyzed by the HHT. Numerical simulations were conducted on two bridge systems with damages using controlled excitations with sweeping frequency. Nonlinear plastic model using a gap element is employed to model the behavior of the cracked elements in the numerical simulations. The results indicate that the HHT method can reasonably identify the damage locations based on a limited number of acceleration sensors. Experimental study has been 실so carried out on a steel frame to confirm the applicability of the HHT to detect a structural connection with loosened bolts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.345-346
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• 2006

In this paper, we propose the noise suppressor with the psychoacoustic model and wavelet packet transform. The objective of the scheme is to enhance speech corrupted by colored or non-stationary noise. If corrupted noise is colored, subband approach would be more efficient than whole band one. To avoid serious residual noise and speech distortion, we must adjust the Wavelet Coefficient threshold. In this paper, the subband is designed matching with the critical band. And WCT is adapted by noise masking threshold(NMT) and segmental signal to noise ratio(seg_SNR). Consequently this work improve the PESQ-MOS about 0.23 in the case of coded speech.

• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Water for future
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• v.29 no.5
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• pp.215-222
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• 1996

An expression for the cross covariance of the logconductivity and the head in nonstationary porous formation is obtained. This cross covariance plays a key role in the inverse problem, i.e., in inferring the statistical characteristics of the conductivity field from head data. The nonstationary logconductivity is modeled as superposition of definite linear trend and stationary fluctuation and the hydraulic head in saturated aquifers is found through stochastic analysis of a steady, two-dimensional flow. The cross covariance with a Gaussian correlation function is investigated for two particular cases where the trend is either parallel or normal to the head gradient. The results show that cross covariances are stationary except along separation distances parallel to the mean flow direction for the case where the trend is parallel to head gradient. Also, unlike the stationary model, the cross covariance along distances normal to flow direction is non-zero. From these observations we conclude that when a trend in the conductivity field is suspected, this information must be incorporated in the analysis of groundwater flow and solute transjport.

• Tribology and Lubricants
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• v.26 no.1
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• pp.31-36
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• 2010

The brake system is an important part of the automobile safety system. The disc brake system is divided into two parts: a rotating axi-symmetrical disc, and the stationary pads. The frictional heat, which is generated on the interface of the disc and pads, can cause high temperatures during the braking process. The frictional heat source (the pads) is moving on the disc and the location is time-dependent. Our study applies a moving heat source, which is defined by the time and space variable on the frictional surface, in order to simulate the frictional heat behavior accurately during the braking process. The object of the present work is the determination of the temperature distribution and thermal stress in the solid disc by non-axisymmetric 3D modeling for repeated braking.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.135-146
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• 2013

This paper presents a feedforward control algorithm for the EGR and VGT systems of passenger car diesel engines. The air-to-fuel ratio and boost pressure are selected as control indicators and the positions of EGR valve and VGT vane are used as control inputs of the EGR and VGT controller. In order to compensate the non-linearity and coupled dynamics of the EGR and VGT systems, we have proposed a non-linear model-based feedforward control algorithm which is obtained from static model inversion approach. It is observed that the average modeling errors of the feedforward algorithm is about 2% using stationary engine experiment data of 225 operating conditions. Using a feedback controller including proportional-integral, the modeling error is compensated. Furthermore, it is validated that the proposed feedforward algorithm generates physically acceptable trajectories of the actuator and successfully tracks the desired values through engine experiments.