• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.44-49
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• 2015

A gust response analyses method based on Reduced Order Models (ROMs) was developed in the paper. Firstly, taken random signal as the input signal and adopt Single Input-Multi-Output (SIMO) training fashion, a ROM based on Auto-Regressive and Moving Average model (ARMA) was established and validated with the comparison of CFD/CSD and experiment. Then, by introducing control surface deflection and control laws, flutter active suppress was studied. Lastly, through filtering and transferring function, the gust temporal signal is obtained based on Dryden gust model, and gust response and suppress were simulated.

• Smart Structures and Systems
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• v.30 no.3
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.848-851
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• 2010

IEEE 802.11 DCF(Distributed Coordination Function)의 성능은 채널에 접근하기 위하여 경쟁하는 단말수에 큰 영향을 받는다. 이에 경쟁하는 단말 수를 예측하기 위하여 많은 방법들이 제안되고 있지만 기존의 방법들은 채널 오류를 고려하지 않고 있다. 따라서 본 논문에서는 기존의 제안된 방법들 중 ARMA(Auto Regressive Moving Average) 필터(Filter)가 적용된 경쟁 단말 수 예측 방법을 수정 및 개선하여 채널 오류를 반영한 단말 수 예측 방법을 제시하였다. 시뮬레이션 결과 제안된 방법은 채널 오류가 존재하는 환경에서 효과적으로 경쟁하는 단말 수를 예측할 수 있음을 확인하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.21-27
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• 2011

When execute runoff forecasting, can not remove perfectly uncertainty of forecasting results. But, reduce uncertainty by various techniques analysis. This study applied various forecasting techniques for runoff prediction's accuracy elevation in Gongju basin. statics techniques is ESP, Period Average & Moving average, Exponential Smoothing, Winters, Auto regressive moving average process. Authoritativeness estimation with results of runoff forecasting by each techniques used MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RRMSE (Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC (Theil Inequality Coefficient). Result that use MAE, RMSE, RRMSE, MAPE, TIC and confirm improvement effect of runoff forecasting, ESP techniques than the others displayed the best result.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.691-702
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• 2006

This study introduced a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows Its potential for investigating agricultural drought characteristics. The method was applied to South Korea as a case study. The proposed procedure included Standardized Precipitation Index(SPI) time series, which were linearly transformed by the Empirical Orthogonal Functions(EOF) method. These EOFs were extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.614-618
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• 2005

수문시계열 분석과 예측은 대부분 ARMA(AutoRegressive Moving Average) 형태의 선형적인 추계학적인 모형을 이용하였으나 자현현상이 복잡해지고 비선형적인 특성을 가짐에 따라 선형적인 해석은 수문시계열의 분석과 예측에 있어서 많은 오류를 내포하고 있다. 이와 같은 문제를 해결하기 위한 시도로 Chaos이론이란 개념이 사용되기 시작하였으며, 수자원분야에서는 1980년대 후반부터 물수지 방정식 및 강우유출에 대한 카오스적 특성분석 등 많은 연구가 진행되었다. 본 연구에서는 영산강유역의 본류를 대표하는 나주지점을 대상으로 2003년 1월 1일 00시부터 2004년 12월 31일 23시까지 17,544개의 시수위 자료에 대하여 해당 년도의 Rating-Curve식을 적용 환산한 유출량자료에 데한 카오스적 특성을 분석하였다. 카오스적 특성을 분석하기에 앞서 원자료에 대하여 이동평균법과 Savitzky-Golay Filter를 적용하여 잡음을 제거하였으며, 1차원의 단일변량의 자료에 대한 상태공간(Phase Space)의 재건을 통하여 비교검토 하였다. 이러한 일련의 과정을 거친 자료에 대하여 상관차원법을 이용하여 영산강 유역의 나주지점의 시유출량 자료에 대한 카오스적 특성을 분석한 결과 저차원의 수렴으로 카오스 특성을 가졌다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.