• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.4
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• pp.550-563
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• 1999

A wavelet-Galerkin scheme based on the time-dependent Maxwell's equations is presented. Daubechies wavelet with two vanishing wavelet moments is expanded for basis function in spatial domain and Yee's leap-frog approach is applied. The shifted interpolation property of Daubechies wavelet family leads to the simplified formulations for inhomogeneous media without the additional matrices for the integral or material operator. The stability condition is formulated. The dispersion characteristics are analyzed and compared with those of finite difference time domain and multiresolution time domain methods. The analyses show the excellent trade-off between the regularity and the support width of the basis function. Although the basis function has only two vanishing wavelet moments, it is enough to provide negligible dispersive error in the numerical analysis and its compact support enables only several involved terms per nodes. The storage effectiveness, execution time reduction and accuracy of this scheme are demonstrated by calculating the resonant frequencies of the homogeneous and inhomogeneous cavities.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.36-44
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• 2003

In this paper, an analytic solution method is proposed to overcome the numerical problems when the slewing dynamics of hybrid coordinate systems is investigated via time finite element analysis. It is shown that the dynamics of the hybrid coordinate systems is governed by the coupled dual differential equations for both slewing and structural modes. Structural modes are transformed into the time-based modal coordinates and analytic spatial propagation equations are derived for each space-dependent time mode. Slew angle history is obtained analytically by appropriate applications of the boundary conditions and structural propagation is re-calculated using the slew angle. Numerical examples are demonstrated to validate the proposed analytic method in comparison to the existing state transition matrix method.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.335-342
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• 2013

In this study, reliability software cost model considering logarithmic fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software cost model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. In this research, Software developers to identify the best time to release some extent be able to help is considered.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.2
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• pp.143-153
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• 2002

Convergence Confinement Method (CCM) makes a more simple judgement in a ground-support reaction than numerical method. Also this method is good for the applicability of construction feedback and the analysis of field measurement. However, there has been little research with respect to the application of CCM in tunnel construction. One of the problems in CCM is a decision of the time to support installation. To decide a reasonable supporting installation time, support characteristic curve and displacement characteristic curve considering construction stage are proposed. In addition, to predict displacement distribution ratio and load distribution ratio, the time dependent support reaction curve is used. Finally, through a comparison of the result between CCM and numerical analysis, the trust of this study is proved and the practical application is proposed to control resonable tunnel construction management.

• Tribology and Lubricants
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• v.23 no.5
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• pp.219-227
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• 2007

The dynamic performance of air foil bearings relies on a coupling between a thin air film and an elastic foil structure. A number of successful analytical techniques to predict dynamic performance have been developed. However, the evaluation of its dynamic characteristic is still not enough because of the mechanical complexity of the foil structure and strong nonlinear behavior of friction force. This work presents a nonlinear transient analysis method to predict dynamic performance of foil bearings. In this method, time dependent Reynolds equation is used to calculate pressure distribution and a finite element method is used to model the bump foil structure. The analysis is treated with a direct implicit integration technique that can handle nonlinear problems and the stick-slip algorithm is used to consider friction force. Using this method the response to the mass unbalance excitation is investigated for various design parameters and operating conditions. The results of analysis show that foil bearing is very effective on the restriction of vibration at the resonance frequency compared to the rigid surface bearings and the effectiveness depends on the operating conditions, static load and a amount of mass unbalance. In addition, there exist optimum values of friction coefficient, bump foil stiffness and number of circumferential slit with regards to minimizing dynamic response at the resonance frequency. These optimum values are system dependent.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09b
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• pp.61-67
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• 2010

Thick soft clay deposits which are generally located at the west and south coast of the Korean peninsula have complicated characteristics according to their orientation and formation history. Thus, several geotechnical problems could possibly occur when those soft clay deposits are used as foundations for marine structures. Deep cement mixing (DCM) method is one of the most widely used soft soil improvement method for various marine structures, nowadays. DCM method injects binders such as cement into the soft ground directly and mixes with the in-situ soil to improve the strength and other geotechnical properties sufficiently. However, the natural impacts induced by dynamic motions such as ocean waves, wind, typhoon, and tusnami give significant influences on the stability of marine structures and their underlaying foundations. Thus, the dynamic properties become important design criteria to insure the seismic stability of marine structures. In this study, the dynamic behavior of cemented Busan clay is evaluated. Laboratory unconfined compression test and resonant column test are performed on natural in-situ soil and cement mixed specimens to confirm the strength and strain-dependent dynamic behavior variation induced by cement mixing treatment. Results show that the unconfined compressive strength and shear modulus increase with curing time and cement content increment. Finally, the optimized cement mixing ratio for sufficient dynamic stability is obtained through this study. The results of this study are expected to be widely used to improve the reliability of seismic design for marine structures.

• Journal of Korea Water Resources Association
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• v.38 no.1
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• pp.59-71
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• 2005

Mountain watersheds are a lot of problems about soil erosion because of frequent wildfire occurrence. Runoff and soil erosion caused by the rain on a hillslope after wildfire are dependent on cover factor. And these has been a decrease by the cover factor recovery following time passage. The present paper defines the dynamic sensitivity of runoff and soil erosion that is the rate of runoff volume and soil erosion weight to rainfall energy and analyzes characteristics of the sensitivity for variation of cover factor, In according to the correlation analysis between other parameters and sensitivities, the sensitivity is the most dependent on the cover factor and the relation is exponential. The sensitivities after wildfire have suitable relation with treatment method for the mitigation of burnt forest and wildfire intensity. It was confirmed that the variation of soil erosion sensitivities come upon the range of stability in 5 years after wildfire.