• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.131-137
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• 2006

We have investigated the properties of the Singular Spectrum Analysis (SSA) coupled with the Linear Recurrent Formula which made it possible to complement the parametric time series model. The SSA has been applied to extract the underlying properties of the principal component of hydrologic time series, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, the prediction by the SSA method can be applied to hydrologic time series governed (may be approximately) by the linear recurrent formulae. This study has examined the forecasting ability of the SSA-LRF model. These methods were applied to monthly discharge and water surface level data. These models indicated that two of the time series have good abilities of forecasting, particularly showing promising results during the period of one year. Thus, the method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.494-500
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• 2008

This paper presents the analyzed result of the series arc fault current by using the discrete wavelet transform. The series arcing is caused by a loose connection in series with the load circuit. The series arc current is limited to a moderate value by the resistance of the device connected to the circuit, such as an appliance or a lighting system. The amount of energy in the sparks from the series arcing is less than in the case of parallel arcing but only a few amps are enough to be a fire hazard. Therefore, it is hard to detect the distinctive difference between a normal current and a intermittent arc current. This paper, presents the variation of the ratio of peak values and RMS values of the series arc fault current, and proposes the novel series arc fault detecting method by using the discrete wavelet transform. Loads such as a CFL lamp, a vacuum cleaner, a personal computer, and a television, which has the very similar normal current with the arc current, were selected to confirm the novel method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.294-297
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• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.353-359
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• 2022

This study investigated via computational analysis the application utility of series-cascaded ring resonators based on silicon-on-insulator (SOI) slot optical waveguides in integrated optical biochemical sensors. The radii of the two rings in the series-cascaded ring resonators were 59.4 ㎛ and 77.6 ㎛ respectively, and the coupling distance was 0.5 ㎛. The series-cascaded ring resonators were computationally analyzed using FIMMProp and PICWave numerical software. The free spectral range (FSR), full width at half maximum (FWHM), sensitivity, and quality-factor (Q-factor) of the series-cascaded ring resonators were 12.2 nm, 0.134 nm, 4100 nm/RIU, and 11580, respectively, and the measurement range was calculated to be slightly smaller than 3×10^-3 RIU. Although the measurement range was smaller than that of the single ring resonator, upon considering other characteristic parameters, the series-cascaded ring resonators are found to be more effective as integrated sensors than single ring resonators.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.3
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• pp.95-101
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• 1986

This paper describes two methods for analyzing distributed parameter systems (DPS) via Walsh series expansions. Firstly, a Walsh-Galerkin expansion approach technique (WGA) introduced by S.G. Tzafestas. is considered. The method which is based on Galerkin scheme, is well established by using Walsh series. But then, there are some difficulty in finding the proper basic functions at each systems. Secondly, a double Walsh series approach technique (DWA) is developed. The essential feature of DWA propoesed here is that it reduces the analysis problem of DPS to that of solving a set of linear algebraic equation which is extended in double Walsh series.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.641-650
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• 2012

Spatial time series data can be viewed as a set of time series simultaneously collected at a number of spatial locations. This paper studies Bayesian inferences in a spatial time bilinear model with a Gibbs sampling algorithm to overcome problems in the numerical analysis techniques of a spatial time series model. For illustration, the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001~2009 are selected for analysis.

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

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.527-534
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• 2020

This study aims to monitor the displacement of the bridges using Stanford Method for Persistent Scatterers (StaMPS) time-series Persistent Scatterer Interferometric Synthetic Aperture Radar analysis. For case study bridges: Kimdaejung bridge and Deokyang bridge, we acquired 60 and 33 Cosmo-Skymed Synthetic Aperture Radar (SAR) data over the Mokpo region and Yeosu region, respectively from 2013 to 2019. With single-look interferograms, we estimated the long-term time-series displacements over the bridges. The time-series displacements were estimated as -8.8 mm/year and -1.34 mm/year at the mid-span over the selected bridges: Kimdaejung and Deokyang bridge, respectively. This time-series displacement provides reliable and high spatial resolution information to monitor the structural behavior of the bridge for preventing structural behaviors.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

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