• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.431-442
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• 2018

This paper highlights the computational explosion issues in the autoregressive moving average approach of frequency estimation of sinusoidal data with a large sample size. A new algorithm is proposed to circumvent the computational explosion difficulty in the conditional least-square estimation method. Notice that sinusoidal pattern can be generated by a non-invertible non-stationary autoregressive moving average (ARMA) model. The computational explosion is shown to be closely related to the non-invertibility of the equivalent ARMA model. Simulation studies illustrate the computational explosion phenomenon and show that the proposed algorithm can efficiently overcome computational explosion difficulty. Real data example of sunspot number is provided to illustrate the application of the proposed algorithm to the time series data exhibiting sinusoidal pattern.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.867-872
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• 2005

This study introduces 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 is applied to Gyeonggi as a case study. The proposed procedure includes Standard Precipitation Index(SPI) time series, which are linearly transformed by the Empirical Orthogonal Functions(EOF) method, These EOFs are 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 Gyeonggi 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.

• 응용통계연구
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• 제29권1호
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• pp.1-12
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• 2016

금융시계열은 일반 시계열과는 차별적으로 stylized facts로 불리는 특징을 가지고 있다. 이 특징들은 급첨 성질, 비정규분포, 변동성 집중 및 비대칭성을 포함한다. 이러한 특징들을 설명하기 위해서는 기존의 선형 ARMA 모형에서 벗어난 특수한 모형이 필요하게 되었다. 본 논문은 변동성 모형인 GARCH 형태의 모형을 중심으로 특수 금융시계열 모형들을 소개하고 연관된 통계적 이슈들에 대해 가능한 최근 연구를 중심으로 폭 넓게 조망하고 있다.

• 한국정밀공학회지
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• 제11권2호
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• pp.85-94
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• 1994

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is, an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identify cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modeling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Date System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequency and damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered

• Journal of Communications and Networks
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• 제5권1호
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• pp.65-72
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• 2003

Internet roundtrip delay/time (RTT) prediction plays an important role in detecting packet losses in reliable transport protocols for traditional web applications and determining proper transmission rates in many rate-based TCP-friendly protocols for Internet-based real-time applications. The widely adopted autoregressive and moving average (ARMA) model with fixed-parameters is shown to be insufficient for all scenarios due to its intrinsic limitation that it filters out all high-frequency components of RTT dynamics. In this paper, we introduce a novel parameter-varying RTT model for Internet roundtrip time prediction based on the information theory and the maximum entropy principle (MEP). Since the coefficients of the proposed RTT model are updated dynamically, the model is adaptive and it tracks RTT dynamics rapidly. The results of our experiments show that the MEP algorithm works better than the ARMA method in both RTT prediction and RTO estimation.

• Smart Structures and Systems
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• 제18권3호
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• 조명전기설비학회논문지
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• 제24권2호
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• pp.133-141
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• 2010

접지시스템은 전기설비의 기준 전위점을 확보할 뿐만 아니라 대지에 낮은 저항으로 고장전류나 과도전류를 흐르게 한다. 주파수에 대한 함수로 접지임피던스는 고장이나 과도전류가 넓은 범위의 주파수 성분을 포함하기 때문에 접지성능을 평가하는데 중요한 역할을 한다. 동봉과 콘크리트봉은 배전계통에서 가장 많이 쓰이고 있는 접지전극이다. 본 논문에서는, 주파수영역과 시간영역의 특성시험으로 동봉과 콘크리트봉의 접지임피던스를 측정하였다. 접지임피던스의 등가 전달함수 모델은 측정된 데이터를 이용하여 ARMA 기법으로 모델링 되었고, 규약 접지임피던스로 비교하여 평가하였다.

• 한국기계가공학회지
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• 제2권2호
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• pp.60-66
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• 2003

For the estimation of chatter lobe boundary it is very important to calculate the natural mode of cutting process. There are many time series algorithms for getting the natural mode of structural endmilling dynamics considering the cutting process. In this study, we have compared several time series methods such as AR algorithm, ARX, ARMAX, ARMA, Box Jenkins, Output Error, Recursive ARX, Recursive ARMAX considering the sampling frequency. As a results, the ARX, ARMAX and IV 4 are more desirable algorithms for the calculation of modal parameters such as natural frequency and damping ratio In endmilling operation. Also these algorithms may be adopted for the natural mode estimation of endmilling operation for chatter lobe prediction.

• 한국수자원학회논문집
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• 제39권8호
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• pp.691-702
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• 2006

본 연구에서는 우리나라 가뭄의 공간적인 특성을 파악하고 가뭄의 진행에 따른 피해규모를 산정하기 위하여 가뭄 심도-영향면적-생기빈도 곡선을 작성하여 제시하였다. 이를 위하여 전국의 기상관측소 지점별로 SPI를 산정하였으며, 산정된 지점별 SPI 자료를 이용하여 EOF 분석을 실시하였다. EOF 분석으로부터 추출된 핵심 공간패턴자료들은 다시 공간적으로는 Kriging 기법을 이용하여 보다 세밀한 공간정보를 갖는 자료로 확장되었으며, ARMA 모형을 이용하여 장기간의 가뭄사상을 모의발생하였다. 모의발생된 공간적인 장기간의 가뭄사상들로부터 특정 가뭄심도별 영향면적별 생기빈도 곡선을 작성할 수 있었다.

• 지능정보연구
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• 제5권1호
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.