• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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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 Electrical Engineering and Technology
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• 제12권3호
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• 환경영향평가
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• 제23권2호
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• pp.112-118
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• 2014

The application of a 3-d noise prediction model is increasing as a tool for performing actual noise assessment in order to investigate the noise impact of the residential facility around a development region. However, because the appropriate plans of applying a 3-d noise prediction model is insufficient, it is important to secure the reliability of the noise prediction results generated by a 3-d noise prediction model. Therefore, this study is focused on examining a 3-d noise prediction model, and a prediction equation and input data in it. For this, the 3-d noise prediction models such as SoundPLAN, Cadna-A, IMMI is applied in road noise. After the contents of road noise equations, input data of road noise source, and input data of road noise barrier are understood, the road noise prediction results are compared and examined according to the variation of 3-d noise prediction model, road noise equation, and input data of road noise source and road noise barrier.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.225-227
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• 2009

International standard specification, H.264/SVC improved from H.264/AVC, is set up so as to promote free use of huge multimedia data in various channel environments.;H.264/AVC is a international standard speicification for video compression, adopted and commercialized as standard for DMB broadcasting by JVT of ISO/IEC MPEG and ITU-T VCEG. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding. Among prediction technologies, 'inter-layer intra prediction' is to use co-located block of up sampled sublevels as a prediction signal. At this time, application of interpolation is one of the most important factors to determine encoding efficiency. SVC's currently using poly-phase FIR filter of 4-tap and 2-tap respectively to luma components. This paper is written for the purpose of analyzing encoding performance according to the interpolation. For this purpose, we applied poly-phase FIR filter of '2-tap', '4-tap' and '6-tap' respectively to luma components and then measured bit-rate, PNSR and running time of interpolation filter. We're expecting that the analysis results of this paper will be utilized for effective application of interpolation filter. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding.

• 한국IT서비스학회지
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• 제18권1호
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• 산업경영시스템학회지
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• 제45권1호
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• pp.71-82
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• 2022

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

• 환경영향평가
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• 제10권4호
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• pp.297-304
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• 2001

Recently the road traffic noise has appeared as a significant environmental issue because of dramatic increase of vehicles and expansion of newly constructed road. Therefore, this study proposes the method that improves prediction factors and models through analysis of the existing road traffic noise prediction model. Prediction factors can be improved by establishing guideline for diffraction attenuation and applying daily traffic discharge, peak traffic discharge, and average traveling speed through an analysis of level service. Prediction must be made by periods of one or five years during 20 years. Prediction models also can be improved to include better prediction model through setting the database, establishing functional relation between physical properties and noise levels by acoustic analysis, and developing models for road traffic noise prediction in residential areas.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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• pp.709-714
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• 2008

The various computer programs are used in computer simulation of the traffic noise prediction. But the difference or problem of calculation method used for road traffic noise prediction is not exactly investigated. In this paper, Road traffic noise is predicted on the specific regions by using four prediction methods such as XPS31-133 model(France), RLS-90 model(Germany), ASJ RTN model(Japan) and FHWA model(U.S.A.), which are operated by a program named SoundPLAN, a program to predict road traffic noise. Those prediction values are compared with a measurement value. The results show that four prediction values for taraffic noise are a little different, because of various input factors according to the prediction methods.

• 전기학회논문지
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• 제59권6호
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• 한국IT서비스학회지
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• 제10권2호
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.