• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1147-1157
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• 2005

Bonus-Malus system is generally constructed based on claim frequency and Bayesian credibility model is used to represent claim frequency distribution. However, there is a problem with traditionally used credibility model for the purpose of constructing bonus-malus system. In traditional Bonus-Malus system adopted credibility model, individual estimates of premium rates for insureds are determined based solely on the total number of claim frequency without considering when those claims occurred. In this paper, a new model which is a modification of structural time series model applicable to counting time series data are suggested. Based on the suggested model relatively higher premium rates are charged to insured with more claim records.

• 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.

• 풍력에너지저널
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• 제14권1호
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• Journal of the Korean Data and Information Science Society
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• 제12권1호
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• pp.71-78
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• 2001

Three test statistics are proposed for the presence of seasonality in multiplicative seasonal time series models. Further their common limiting distribution is derived under some assumptions.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 통합자연과학논문집
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• 제7권3호
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• pp.193-199
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• 2014

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.609-617
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• 2007

The goal of this study is to forecast the trend of stream quality and to suggest some policy alternatives in Gumbo river. It used the five different monthly time series data such as BOD, COD, T-N and EC of the nine of Gumbo River measurement points from Jan. 1998 to Dec. 2006. Water pollution is serious at Gumbo2 and Palgeo stream measurement points. BOD, COD, T-N and EC data are analyzed with the exponential smoothing model and the trend is forecasted until Dec. 2009.

• 대한임베디드공학회논문지
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• 제19권4호
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• pp.195-201
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• 2024

In this paper, we propose an improved technique called HAB (Huffman encoding Aware Bitpacking) that enhances the Sprintz method, which is a representative time series data compression technique. The proposed technique boosts compression rates by incorporating Huffman encoding-aware bitpacking in the secondary compression stage. Additionally, it can be applied to terminal nodes or edge nodes with limited available resources, as it does not require separate parameters or storage space for compression. The proposed technique is a lossless method and is suitable for fields that require the generation of artificial intelligence models and accurate data analysis. In the experimental evaluation, the proposed HAB showed an average improvement of 14.7% compared to the existing technique in terms of compression rate.

• 한국컴퓨터정보학회논문지
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• 제14권5호
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• pp.19-28
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• 2009

시계열의 예측에 대한 문제는 오랫동안 많은 연구자들의 연구의 대상이었으며 예측을 위한 많은 방법이 제안되었다. 본 논문에서는 은닉 마코프 모델(Hidden Markov Model)과 우도(likelihood)를 사용한 유사도 검색을 통하여 향후 시계열 데이터의 운행 방향을 예측하는 방법을 제안한다. 이전에 기록된 시계열 데이터에서 질의 시퀸스(sequence)와 유사한 부분을 검색하고 유사 부분의 서브 시퀸스를 사용하여 시계열을 예측하는 방법이다. 먼저 주어진 질의 시퀸스에 대한 은닉 마코프 모델을 작성한다. 그리고 시계열 데이터에서 순차적으로 일정 길이의 서브 시퀸스를 추출하고 추출된 서브 시퀸스와 작성된 은닉 마코프 모델과의 우도를 계산한다. 시계열 데이터로부터 추출된 서브 시퀸스 중에서 우도가 가장 높은 시퀸스를 유사 시퀸스로 결정하고 결정된 부분 이후의 값을 추출하여 질의 시퀸스 이후의 예측 값을 추정한다. 실험 결과 예측 값과 실제 값이 상당한 유사성을 나타내었다. 제안된 방법의 유효성은 코스피(KOSPI) 종합주가지수를 대상으로 실험하여 검증한다.

• 응용통계연구
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• 제26권6호
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• pp.1019-1032
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• 2013

최근 급격한 기대수명의 증가에 따라 미래 복지정책 등에 커다란 영향을 주는 장래 사망력의 정확한 예측은 중요한 이슈가 되고 있다. 사망력의 정확한 예측을 위하여 최적의 추정모형의 선택도 중요하지만 사망력에 대한 시계열 적용기간도 매우 중요한 이슈다. 이는 우리나라의 사망률 시계열이 짧고, 특히 1982년 이전 자료가 다소 불완전해서 이에 대한 고려가 필수적이기 때문이다. 본 논문에서는 우리나라 사망력 시계열을 기간에 따라 2개의 그룹(1976~2005년, 1983~2005년)으로 나누어서, 남녀별로 LC모형과 LC 코호트효과 확장모형에 대한 모수 추정값, 사망력지수와 코호트지수의 모형화 및 예측, 장래 기대수명의 예측 적합력을 각각 분석한 후 향후에 장래 기대수명 추계시 고려할 시사점을 제시하고자 한다.