• Journal of applied mathematics & informatics
• /
• 제28권5_6호
• /
• pp.1157-1169
• /
• 2010

This paper presents the analytical solution of the fractional Fokker-Planck equation by Adomian decomposition method. By using initial conditions, the explicit solution of the equation has been presented in the closed form and then the numerical solution has been represented graphically. Two different approaches have been presented in order to show the application of the present technique. The present method performs extremely well in terms of efficiency and simplicity.

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

• 대한기계학회논문집A
• /
• 제27권4호
• /
• pp.585-592
• /
• 2003

Among various sensitivity evaluation techniques, semi-analytical method(SAM) is quite popular since this method is more advantageous than analytical method(AM) and global finite difference method(FDM). However, SAM reveals severe inaccuracy problem when relatively large rigid body motions are identified fur individual elements. Such errors result from the numerical differentiation of the pseudo load vector calculated by the finite difference scheme. In the present study, an iterative method combined with mode decomposition technique is proposed to compute reliable semi-analytical design sensitivities. The improvement of design sensitivities corresponding to the rigid body mode is evaluated by exact differentiation of the rigid body modes and the error of SAM caused by numerical difference scheme is alleviated by using a Von Neumann series approximation considering the higher order terms for the sensitivity derivatives.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제7권2호
• /
• pp.301-314
• /
• 2015

Aiming at accurately distinguishing modeless component and natural vibration mode terms from data series of nonlinear and non-stationary processes, such as Vortex-Induced Vibration (VIV), a new empirical mode decomposition method has been developed in this paper. The key innovation related to this technique concerns the method to decompose modeless component from non-stationary process, characterized by a predetermined 'maximum intrinsic time window' and cubic spline. The introduction of conceptual modeless component eliminates the requirement of using spurious harmonics to represent nonlinear and non-stationary signals and then makes subsequent modal identification more accurate and meaningful. It neither slacks the vibration power of natural modes nor aggrandizes spurious energy of modeless component. The scale of the maximum intrinsic time window has been well designed, avoiding energy aliasing in data processing. Finally, it has been applied to analyze data series of vortex-induced vibration processes. Taking advantage of this newly introduced empirical decomposition method and mode identification technique, the vibration analysis about vortex-induced vibration becomes more meaningful.

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

• 응용통계연구
• /
• 제19권2호
• /
• pp.319-330
• /
• 2006

현실세계에서 관찰되는 시그널(signal)은 다양한 주파수(frequency)들의 시그널로 혼합되어 있는 경우가 많다. 예를 들어 태양 흑점 자료의 경우 약 11년 주기와 85년 주기로 변동한다는 사실은 널리 알려져 있다. 또한 경제 시계열 자료의 경우는 통상적으로 계절요인(seasonal component), 순환요인(cyclic component) 그리고 장기적인 추세요인(long-term trend)으로 분해하여 분석한다. 이러한 시계열 자료를 구성요소별로 분해하는 것은 오래된 주제중 하나이다. 전통적인 시계열자료 분석기법으로 스펙트럴 분석기법 등이 널리 사용되고 있으나 시계열 자료들이 비정상(nonstationary)일 경우에는 적용하기 어렵다. Huang et. al(1998)은 경험적 모드분해법(empirical mode decomposition)이라고 하는 자료적응적인(data-adaptive) 방법을 제안하였는데, 비정상성(nonstationarity)에 대한 강건성(robustness)으로 여러 분야에 널리 응용되고 있다. 그러나 Huang et. at(1998)은 잡음(error)에 의해 오염된 자료에 대한 구체적인 처리방법은 제시하지 못하고 있다. 본 논문을 통하여 효율적인 잡음제거 방법을 제안하고자 한다.

• Journal of applied mathematics & informatics
• /
• 제27권5_6호
• /
• pp.1265-1277
• /
• 2009

In this paper, we apply a new decomposition method for solving initial and boundary value problems, which is due to Noor and Noor [18]. The analytical results are calculated in terms of convergent series with easily computable components. The diagonal Pade approximants are applied to make the work more concise and for the better understanding of the solution behavior. The proposed technique is tested on boundary layer problem; Thomas-Fermi, Blasius and sixth-order singularly perturbed Boussinesq equations. Numerical results reveal the complete reliability of the suggested scheme. This new decomposition method can be viewed as an alternative of Adomian decomposition method and homotopy perturbation methods.

• Journal of applied mathematics & informatics
• /
• 제25권1_2호
• /
• pp.525-531
• /
• 2007

We newly introduce the concept of summable in measure and investigate on some its properties. In addition to this, we consider a size of given series by means of we are giving Lebesgue measure to an associated series.

• Kyungpook Mathematical Journal
• /
• 제51권2호
• /
• pp.233-239
• /
• 2011

In this article we study the noton of I-monotonic sequences. We prove the decomposition theorem and generalize some of the results on monotonic sequences. We also introduce I-convergent series and studied some results.

• Journal of the Korean Statistical Society
• /
• 제23권2호
• /
• pp.503-503
• /
• 1994