• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.14 no.5
• /
• pp.7-12
• /
• 2005

This paper presents several time series methods to analyze the chatter mechanics by using the power spectrum of these algorithms considering the cutting dynamics. In this study, several time series models such as AR(burg, forwardbackward, geometric lattice, instrument variable, least square, Yule Walker), ARX(1s, iv4), ARMAX, ARMA, Box Jenkins, Output Error were modeled and compared with one another. Finally, it was proven that time series modelings are also a desirable and reliable algorithm than the other conventional methods(FFT) for the calculation of the chatter mode in turning operation. Also, the spectrum of times series methods is a little bit more powerful than the FFT fer the detection of a high noisy and weak chatter mode. The radial cutting force Fy has been used for spectrum and chatter stability lobe analysis in this study.

• Journal of the Korean Solar Energy Society
• /
• v.37 no.5
• /
• pp.77-84
• /
• 2017

The purpose of this paper is to propose the way of computing conduction time series factors (CTSF) using numerical method. After the accuracy of the numerical solution procedure being verified, the method is applied to the wall type 24 and roof type 14 of ASHARE to find the conduction time series coefficients, so called conduction time series factors. The results agree well with the values presented in the ASHRAE handbook. The method proposed can be easily applied to find unknown CTSF for more complex structures. It provides information about the temperature changes at a given location and time, thus validity of generated CTSF can be checked easily.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2000.10a
• /
• pp.118-122
• /
• 2000

In this study, Ringing is investigated by continuous wavelet transform. Ringing is considered to be one of the typical transient phenomena in the field of ocean engineering. The wavelet analysis is adopted to analyze ringing from the point that wavelet analysis is capable of frequency analysis as well as time domain analysis. The use mother wavelet is the Morlet wavelet. The relation between the frequency of the time series and that of wavelet can be clearly defined with Mor1et wavelet. Experimental data obtained by other researchers was used. The wave height time series and acceleration times series of the surface piercing cylinder were analyzed. The results show that the proposed scheme can detect typical frequency region by the time domain analysis which could hardly be detected if one relied on the frequency analysis.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.1
• /
• pp.73-80
• /
• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Journal of Korean Society for Quality Management
• /
• v.31 no.3
• /
• pp.160-171
• /
• 2003

Criteria for the comparison of quality of asset portfolio management are risk and return. In this paper a method to use structural time-series model to determine an optimal portfolio for the improvement of quality of asset portfolio management is suggested. In traditional mean variance analysis expected return is assumed to be time-invariant. However, it is more realistic to assume that expected return is temporally dynamic and structural time-series model can be used to reflect time-varying nature of return. A data set from an insurance company was used to show validity of suggested method.

• Journal of Industrial Convergence
• /
• v.2 no.2
• /
• pp.127-147
• /
• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Journal of the Society of Naval Architects of Korea
• /
• v.51 no.4
• /
• pp.274-282
• /
• 2014

Time series of the dynamic response of a slender marine structure was predicted using quadratic Volterra series. The wave-structure interaction system was identified using the NARX(Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through the supervised training with the prepared datasets. The dataset used for the network training was obtained by carrying out the nonlinear finite element analysis on the freely standing riser under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of relative velocity between the water particle and structure in Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of response of the structure was predicted using the quadratic Volterra series. In order to check the applicability of the method, the response of structure under the realistic ocean wave environment with given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. It turned out that the predicted time series of the response of structure with quadratic Volterra series successfully captures the slowly varying response with reasonably good accuracy. It is expected that the method can be used in predicting the response of the slender offshore structure exposed to the Morison type load without relying on the computationally expensive time domain analysis, especially for the screening purpose.

• Journal of Internet of Things and Convergence
• /
• v.4 no.1
• /
• pp.1-5
• /
• 2018

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

• Korean Journal of Remote Sensing
• /
• v.29 no.4
• /
• pp.351-360
• /
• 2013

Harmonic analysis enables to characterize patterns of variation in MODIS NDVI time series data and track changes in ground vegetation cover. In harmonic analysis, a periodic phenomenon of time series data is decomposed into the sum of a series of sinusoidal waves and an additive term. Each wave is defined by an amplitude and a phase angle and accounts for the portion of variance of complex curve. In this study, harmonic analysis was explored to tract ground vegetation variation through time for land-cover vegetation change detection. The process also enables to reconstruct observed time series data including various noise components. Harmonic model was tested with simulation data to validate its performance. Then, the suggested change detection method was applied to MODIS NDVI time series data over the study period (2006-2012) for a selected test area located in the northern plateau of Korean peninsula. The results show that the proposed approach is potentially an effective way to understand the pattern of NDVI variation and detect the change for long-term monitoring of land cover.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.5 no.3
• /
• pp.43-50
• /
• 2006

In this successive study, the analytical realization of coupled system was introduced using the times series identification and spectrum analysis, which was compared with conventional FFT spectrum. Also, the numerical responses of second order system, which is coupled, were solved using the numerical calculation of Runge-Kutta Gill method. After numerical analysis, the displacement, velocity and acceleration were acquired. Among them, the response of displacement was used for the analysis of time series spectrum. Among several time series, the ARMAX algorithm was proved to be appropriate for the spectrum analysis of the coupled system. Using the separated response of 1st and 2nd mode, the mode was calculated separately. And the responses of mixed modes were also analyzed for calculation of the mixed modes in the coupled system.