• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.69-78
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• 2010

The irreversible 9/7 JPEG2000, which is one of sub-band coding techniques, has a problem of severe picture quality distortion at the edge and the background caused by the quantization error below 0.15bpp. In this paper, to solve such problems we propose a VQ(Visual Quantizer) based on L-pdf(Laplace probability density function) statistical characteristics of high frequency sub-bands. The proposed VQ is designed by visual parameter for improving the subjective quality and weighting parameter for increasing the compression ratio. A proposed method, based on 9/7 JPEG2000 scheme, gives the high subjective quality to reconstructed images below 0.15bpp and provides minimum MSE(Mean-Squared Error) regardless of the compression ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2451-2458
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• 1997

Adaptive channel equalization complished without resorting to a training sequence is known as blind equalization. In this paper, in order to increase the speed of the convergence and to reduce the steady-state mean squared error simulatneously, we propose the multi-stage DD(decision-direct) algorithm derived from the combination of the Sato algorithm and the decision-directed algorithm. In the starting stage, the multi-stage DD algorithm is identical to the Sato algorithm which guarantees the convergence of the equalizer. As the blind equalizer converges, the number of the level of the quantizers is increased gradally, so that the proposed algorithm operates identical to the decision-directed algorithm which leads to the low error power after the convergence. Therefore, the multi-stage DD algorithm obtains fast convergence rate and low steady state mean squared error.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.6
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• pp.6-13
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• 2011

Multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) with space-time coding (STC) is a promising technology for future wireless communication systems. However, MIMO-OFDM systems are greatly impaired by large cochannel interference (CCI) from the multiple transmitters. In this paper, we propose pre-fast Fourier transform (FFT) multibeamforming based on MMSE(minimizing the mean squared error) for a MIMO-OFDM system to preserves the STC diversity and to remove the CCI. The improvement in bit error rate is investigated through computer simulation of a MIMO-OFDM system in a multipath channel with CCI.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.89-94
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• 2022

This paper proposes a computationally efficient 2-D direction-of-arrival (DoA) estimation method with a uniform rectangular array (URA). This method is called the direct signal-based method in the sense that it is based directly on the phase relationships among the signals arriving at each antenna of an antenna array rather than their correlation matrix. According to the simulation results, it has be shown that the direct signal-based method, with much less computations than any existing methods, yields the performance comparable to that of the MUSIC (MUltiple SIgnal Classification) method in terms of the root-mean-squared error (RMSE) and the maximum absolute error.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.883-885
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• 2013

Image restoration refers to removing different kinds of noise added to image, and to reducing effect of noise upon image. For image restoration, some methods such as mean filter, median filter and weighted filter were proposed, but the existing methods have poor denoising and edge-reserved performance. Therefore, in this paper modified median filter algorithm was proposed that enlarges mask size according to median value of mask in order to remove noise efficiently. And, it was compared by simulation to the existing methods, and MSE(mean squared error) was used on a criterion of evaluation.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.51-60
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• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.915-918
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• 2001

In this paper we study a new convergence behavior of the least mean fourth (LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.895-904
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• 1998

Two stage shrinkage testimator is a kind of adaptive estimators based on a test on an initial estimate of parameter. Since weighing factor plays an important roll in assessing the properties of testimator, its choice is extremely crucial in two stage testimation. Adke, Waikar and Schuurmann(1987) proposed a testimator for the mean of an exponential distribution defined with their own weighing factor. Two alternative testimators obtained using changed weighing factors are presented, and their Mean squared error(MSE) formulae are provided in this paper. Their properties are compared with those of existing one by means of MSE.