• 한국항행학회논문지
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• 제12권1호
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• pp.22-27
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• 2008

본 논문에서는 기존의 MMOSPRED(MultiMedia One Step Prediction)에 의한 멀티미디어 호의 자원 요구량(채널 수)의 예측방법을 개선한 적응 MMOSPRED 기법을 제안하고, 이 기법을 사용한 멀티미디어 무선망의 호 수락제어의 성능을 분석한다. 제안된 적응기법은 자원 요구량의 예측시간 동안 고정된 표준 정규분포의 확률변수 값을 갖는 기존의 MMOSPRED 방법과는 다르게 LMS 알고리즘을 사용하여 자원의 예측 오차량을 최소화시킨다. 시뮬레이션을 통하여 제안된 방법에 의한 자원의 예측 오차량이 기존의 방법보다 감소함을 보이고, 제안된 적응예측기법을 사용한 호 수락제어는 기존의 방법보다 미래의 핸드오프 호 가 요구하는 자원의 양을 상대적으로 정확히 예측함으로써, 원하는 핸드오프 호 손실확률에서 신규 호의 수락율을 증가시킴으로써 호 수락제어의 성능이 향상됨을 보인다.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Geomechanics and Engineering
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• 제13권5호
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• pp.845-860
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• 2017

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

• 한국지반환경공학회 논문집
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• 제20권10호
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• pp.29-38
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• 2019

연약지반 안정 및 침하관리에 있어 침하예측기술은 지속적으로 발전되어 공사비 절감과 정확한 토지사용 시기를 확인하는데 활용하고 있으나, 기존 예측방법인 쌍곡선법, Asaoka법, Hoshino법 등은 많은 계측기간이 경과되어야 정확한 침하예측이 가능하여 압밀초기 신속한 예측이 어려운 실정이다. 기존 예측방법이 침하곡선으로부터 산정한 기울기의 비례성 가정을 통해 장래침하량을 추정하는 사유로 판단된다. 본 연구에서는 시계열 분석기술 중 ARIMA 기법을 도입하여 기존예측방법과 비교 분석하였다. ARIMA 기법은 지반조건 구분 없이 예측 가능하였으며, 기존방법과 유사한 결과를 조기에 예측(최종침하) 할 수 있었다.

• 방송공학회논문지
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• 제17권5호
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• pp.781-788
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• 2012

HEVC에서는 움직임 예측 시, 복잡도를 줄이기 위해 고속 탐색 기법이 사용된다. 고속 탐색 기법에는 SAD 계산 복잡도를 줄인 부-화소 단위 SAD 계산 기법(sub-sampled SAD)과 양-예측시의 단-예측 반복횟수를 줄인 간소화된 양-예측 기법으로 이루어져 있다. 고속 탐색 기법으로 인해 복잡도는 크게 줄었지만 부호화 이득 역시 감소하였다. 본 논문에서는 감소된 부호화 효율을 보상하기 위해 간소화된 양-예측을 확장하였고 확장된 양-예측으로 증가된 복잡도를 줄이기 위해 예측 비용 기반의 복잡도 감소 기법들을 제안한다. 예측 비용 기반의 복잡도 감소 기법은 양-예측 조기 종료 기법과 양-예측 생략 기법으로 이루어져 있다. HM 6.0 참조 소프트웨어와 비교하여 확장된 양-예측 기법과 예측 비용 기반의 복잡도 감소 기법으로 복잡도의 증가 없이 평균 0.42%의 BD-bitrate을 감소시켰다.

• 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.

• 유통과학연구
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• 제17권11호
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• 응용통계연구
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• 제24권6호
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• 한국경영과학회지
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• 제33권2호
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.489-506
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• 2007

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.