• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.8
• /
• pp.34-41
• /
• 2017

Vehicle to Infrastructure(V2I) communication means the technology between the vehicle and the roadside unit to provide the Intelligent Transportation Systems(ITS) and Telematic services. The vehicle collects information about the probe data through the evolved Node B(eNodeB) and after that eNodeB provides road conditions or traffic information to the vehicle. To provide these V2I communication services, we need a link adaptation technology that enables reliable and higher transmission rate. The receiver transmits the estimated Channel State Information(CSI) to transmitter, which uses this information to enable the link adaptation. However, due to the rapid channel variation caused by vehicle speed and the processing delay between the layers, the estimated CSI quickly becomes outdated. For this reason, channel interpolation and prediction scheme are needed to achieve link adaptation in V2I communication system. We propose the Advanced Discrete Prolate Spheroidal Sequence(ADPSS) channel interpolation and prediction scheme. The proposed scheme creates an orthonomal basis, and uses a correlation matrix to interpolate and predict channel. Also, smoothing is applied to frequency domain for noise removal. Simulation results show that the proposed scheme outperforms conventional schemes with the high speed and low speed vehicle in the freeway and urban environment.

• Nuclear Engineering and Technology
• /
• v.52 no.9
• /
• pp.1945-1954
• /
• 2020

Subchannel code is one of the effective simulation tools for thermal-hydraulic analysis in nuclear reactor core. In order to reduce the computational cost and improve the calculation efficiency, empirical correlation of turbulent mixing coefficient is employed to calculate the lateral mixing velocity between adjacent subchannels. However, correlations utilized currently are often fitted from data achieved in central channel of fuel assembly, which would simply neglect the wall effects. In this paper, the CFD approach based on spectral element method is employed to predict turbulent mixing phenomena through gaps in 3 × 3 bare tight lattice rod bundle and investigate the flow pulsation through gaps in different positions. Re = 5000,10000,20500 and P/D = 1.03 and 1.06 have been covered in the simulation cases. With a well verified mesh, lateral velocities at gap center between corner channel and wall channel (W-Co), wall channel and wall channel (W-W), wall channel and center channel (W-C) as well as center channel and center channel (C-C) are collected and compared with each other. The obvious turbulent mixing distributions are presented in the different channels of rod bundle. The peak frequency values at W-Co channel could have about 40%-50% reduction comparing with the C-C channel value and the turbulent mixing coefficient β could decrease around 25%. corrections for β should be performed in subchannel code at wall channel and corner channel for a reasonable prediction result. A preliminary analysis on fluctuation at channel gap has also performed. Eddy cascade should be considered carefully in detailed analysis for fluctuating in rod bundle.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.26 no.5
• /
• pp.24-34
• /
• 2022

In order to predict the cooling performance of a regenerative cooling channel using hydrocarbon fuel operating in the supercritical region, it is essential to predict the thermodynamic properties. In this study, a comparative analysis was performed on two-parameter equations of state (SRK(Soave-Redlich-Kwong), PR(Peng-Robinson) equations of state) and three-parameter equations of state (RK-PR equations of state) to appropriately predict density and specific heat according to the critical compressibility factor of polymer hydrocarbons. Representatively, n-dodecane fuel with low critical compressibility factor and JP-10 fuel with high critical compressibility factor were selected, and an appropriate equation of state was presented when predicting the thermodynamic properties of the two fuels. Finally, the prediction results of density and specific heat were compared and verified with NIST REFPROP data.

• Proceedings of the Korea Contents Association Conference
• /
• 2005.11a
• /
• pp.340-344
• /
• 2005

For the next generation wireless systems, multimedia services will be highly demanded and needed to transmit the cost-efficient packet data. With this regard, the adaptive resource management methods are preferable to optimally control the various traffic classes in OFDMA (WiBro) systems. We have developed a sub-channel state prediction method based on the past sub-channel state dependency. Using the method, we are developed a scheduling algorithm that can efficiently control multimedia traffic downlink transmissions.

• Nuclear Engineering and Technology
• /
• v.53 no.6
• /
• pp.1796-1809
• /
• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.20 no.12
• /
• pp.1287-1296
• /
• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• Water for future
• /
• v.23 no.2
• /
• pp.213-225
• /
• 1990

A mathematical model is presented for predicting the steady state two-demensional distribution of solute concentration in the meandering nonuniform natural channel. The dispersion equation derived herein employs the transverse cumulative discharge as an independent variable replacing the transverse distance and that it is developed in an orthogonal curvilinear coordinnate system which follows the flow direction of natural channel. The prediction from the results of numerical model are compared with laboratory experiment data. It is found that results from simulation and experiments are in good agreement.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
• /
• 1995.05a
• /
• pp.113-116
• /
• 1995

Simplified approach has been adopted for the prediction of the thermal behavior of CANDU reactor core during power transients. Based on the assumption that the ratio of mass flow rate for each core channel does not vary during the transient, quasy-steady state analysis technique is applied with predicted core inlet boundary conditions(total mass flow rate and specific enthalpy). For restricted transient case, the presented method shows functionally reasonable estimation of core thermal behavior which could be implemented in the fast running reactor simulation program.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.43 no.3 s.345
• /
• pp.49-58
• /
• 2006

In this paper, a curve-fitting channel estimation method is proposed for orthogonal frequency division multiplexing (OFDM) system in a time-varying frequency-selective fading channel. The method can greatly improve channel state information (CSI) estimation accuracy by performing smoothing and interpolation through consecutive curve-fitting processes in both time domain and frequency domain. It first evaluates least-squares (LS) estimates using pilot symbols and then the estimates are approximated to a polynomial with proper degree in the LS error sense, starting from one preferred domain in which pilots we densely distributed. Smoothing, interpolation, and prediction are performed subsequently to obtain CSI estimates for data transmission. The channel estimation processes are completed by smoothing and interpolating CSI estimates in the other domain once again using the channel estimates obtained in one domain. The performance of proposed method is influenced heavily on the time variation and frequency selectivity of channel and pilot arrangement. Hence, a proper degree of polynomial and an optimum approximation interval according to various system and channel conditions are required for curve-fitting. From extensive simulation results in various channel environments, we see that the proposed method performs better than the conventional methods including the optimal Wiener filtering method, in terms of the mean square error (MSE) and bit error rate (BER).

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2815-2817
• /
• 2001

This study describes the emotion classification using two different feature extraction methods for four-channel EEG signals. One of the methods is linear prediction analysis based on AR model. Another method is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with a neural network. Comparing the results of two methods, it seems that the linear predictor coefficients produce the better results than the cross-correlation coefficients of frequencies for-emotion classification.