• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1443-1449
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• 2009

This paper presents a profiling model of a wide-window superscalar microprocessor using multiple branch prediction. The key idea is to apply statistical profiling technique to the superscalar microprocessor with a wide instruction window and a multiple branch predictor. The statistical profiling data are used to obtain a synthetical instruction trace, and the consecutive multiple branch prediction rates are utilized for running trace-driven simulation on the synthesized instruction trace. We describe our design and evaluate it with the SPEC 2000 integer benchmarks. Our performance model can achieve accuracy of 8.5 % on the average.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.731-737
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• 2012

In this paper the usage of Bluetooth in networked control systems is described. ACL links and commonly used serial port profile built on top of ACL links are analyzed and their problems such as unpredictable latency are discovered. SCO link packet scheduling, latency estimation and setup procedure are examined. SCO link is suggested as proper link for NCS, due to its low latency and low variance. Smith predictor use for latency compensation is described and its impact on control performance is estimated. A number of experiments on DC motor position control are performed and control performance of system utilizing SCO link with and without Smith predictor is proved to be higher than control performance of system utilizing ACL link.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.136-141
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• 1998

In order to enhance the performance of a CELP coder at low bit rates, it would be necessary to make the CELP excitation have the peaky pulse characteristic. In this paper we introduce an excitation signal with peaky pulse characteristic. It is obtained by using a two-tap pitch predictor. Samples of the signal have different gains according to their amplitudes by the predictor. In voiced sound the signal has the desirable peaky pulse characteristic, and its periodicity is well reproduced. Particularly, peaky pulses at voiced onset and a burst of plosive sound are clearly reconstructed.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.143-144
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• 2006

This paper presents an adaptive OFDM system with multistep predictor to effectively compensate multiple feedback delays. The proposed scheme adaptively changes the modulation order per subcarrier based on the predicted CSI to improve data capacity and system performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4A
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• pp.370-377
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• 2004

In this paper, we present an adaptive modulation technique for orthogonal frequency division multiplexing (OFDM) for broadband wireless communications. Also, using improved channel prediction, we enhance the performance of adaptive OFDM in high mobility environments. Adaptive modulation technique has been shown to achieve reliable high-rate data transmission over frequency-selective fading channel when OFDM is employed. This scheme requires the accurate channel information between two stations for a better performance. In an outdoor high mobility environment, most of adaptive OFDM systems have to be given the channel information transmitted from the receiver. Even if it is possible, there is some delay. Moreover, the channel impulse response between two stations is very rapidly varied. If the channel information is obsolete at the time of transmission, then poor system performance will result. In order to solve this problem, we propose adaptive OFDM with improved channel predictor. The proposed bit allocation algorithm has a lower complexity and the proposed scheme mitigates the effect of channel delay. Robust approach is less sensitive to outdated channel information. Performance results show that the proposed scheme can achieve considerable performance enhancement.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.15-26
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• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.1021-1026
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• 2005

In this paper, we present a novel predictive CDMA closed-loop power control (CLPC) method with a multi-step least squares (LS) linear predictor for time-varying fading channels. The proposed method effectively compensates multiple power control group delays and provides significant performance gains over nonpredictive CLPCs as well as conventional predictive CLPCs with one-step linear predictor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.