• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.765-771
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• 2001

Elastic-plastic fracture toughness JIC can be used a s an effective design criterion in elastic-plastic fracture mechanics. Among the JIC test methods approved by ASTM, unloading compliance method was used in this study. In order to examine the relationship between fracture behavior of JIC test and AE signals, the post processing of AE signals has been carried out by Short Time Fourier Transform(STFT), one of the time-frequency analysis methods. The objective of this study is to evaluate the application of characterization of AE signals for unloading compliance method of JIC test. As a result of time-frequency analysis, we could extract the AE from the raw signal and analyze the frequencies in AE signal at the same time. AE signal generated by elastic-plastic fracture of material has some different aspects at elastic and plastic ranges, or the first portion of crack growth by fracture. First of all, increased energy recorded and detected by using AE count method increase rapidly from the start of ductile fracture. The variation of main frequency range with time-frequency analysis method could be confirmed. We could know fracture behavior of interior material by examination AE characteristics generated in real-time when elastic-plastic fracture occurred in material under loading.

• Interaction and multiscale mechanics
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• v.2 no.1
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• pp.91-107
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• 2009

In order to accurately estimate the seismic behavior of buildings, it is important to consider both nonlinear characteristics of the buildings and the frequency dependency of the soil impedance. Therefore, transform methods of the soil impedance in the frequency domain to the impulse response in the time domain are needed because the nonlinear analysis can not be carried out in the frequency domain. The author has proposed practical transform methods. In this paper, seismic response analyses considering frequency dependent soil impedance in the time domain are shown. First, the formulation of the proposed transform methods is described. Then, the linear and nonlinear earthquake response analyses of a building on 2-layered soil were carried out using the transformed impulse responses. Through these analyses, the validity and efficiency of the methods were confirmed.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• Interaction and multiscale mechanics
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• v.5 no.3
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• pp.169-185
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• 2012

The concrete bridge is likely to produce fatigue cracks during long period of service due to the moving vehicular loads and the degeneration of materials. This paper deals with the time-frequency analysis of a coupled bridge-vehicle system. The bridge is modeled as an Euler beam with breathing cracks. The vehicle is represented by a two-axle vehicle model. The equation of motion of the coupled bridge-vehicle system is established using the finite element method, and the Newmark direct integration method is adopted to calculate the dynamic responses of the system. The effect of breathing cracks on the dynamic responses of the bridge is investigated. The time-frequency characteristics of the responses are analyzed using both the Hilbert-Huang transform and wavelet transform. The results of time-frequency analysis indicate that complicated non-linear and non-stationary features will appear due to the breathing effect of the cracks.

• Journal of the Korean Society of Safety
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• v.22 no.2 s.80
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• pp.1-7
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• 2007

A problem of a unsteady time-dependent flow in a channel is of practical importance and widely considered in the design of devices such as heat exchangers, duct, and electronic equipments. The characteristics of fluid flow in channel with oscillating vortex generator was investigated experimentally. The main object of this study was to investigate the effect of the excited frequency, the excited amplitude, and Reynolds numbers on the generated frequency. Flow patterns were visualized using smoke generator and generated frequencies were measured using hot wire anemometer. When the excited frequency is increased, excited amplitude decreased and Reynolds number increased, the strength of PSD of generated frequency is decreased.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.110-114
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• 2012

We studied on the nondestructive evaluation of the elastic wave signal of SiC ceramics and SiC/SiC composite ceramics under monotonic tensile loading. The elastic wave signal of cross and unidirectional SiC/SiC composite ceramics were obtained by pencil lead method and bending test. It was applied for the time-frequency method which used by the discrete wavelet analysis algorithm. The time-frequency analysis provides time variation of each frequency component involved in a waveform, which makes it possible to evaluate the contribution of SiC fiber frequency. The results were compared with the characteristic of frequency group from SiC slurry and fiber. Based on the results, if it is possible to shift up and design as a higher frequency group, we will can make the superior material better than those of exiting SiC/SiC composites.

• Wind and Structures
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• v.38 no.4
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• pp.277-293
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• 2024

The recorded thunderstorm winds at a point contain tri-directional components. The probabilistic characteristics of such recorded winds in terms of instantaneous mean wind speed and direction, and the probability distribution and the time-frequency dependent crossed and non-crossed power spectral density functions for the high-frequency fluctuating wind components are unclear. In the present study, we analyze the recorded tri-directional thunderstorm wind components by separating the recorded winds in terms of low-frequency time-varying mean wind speed and high-frequency fluctuating wind components in the alongwind direction and two orthogonal crosswind directions. We determine the time-varying mean wind speed and direction defined by azimuth and elevation angles, and analyze the spectra of high-frequency wind components in three orthogonal directions using continuous wavelet transforms. Additionally, we evaluate the coherence between each pair of fluctuating winds. Based on the analysis results, we develop empirical spectral models and lagged coherence models for the tri-directional fluctuating wind components, and we indicate that the fluctuating wind components can be treated as Gaussian. We show how they can be used to generate time histories of the tri-directional thunderstorm winds.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Journal of Advanced Navigation Technology
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• v.19 no.3
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• pp.244-249
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• 2015

High-altitude electromagnetic pulse (HEMP) is generated from a nuclear burst at high altitudes above the Earth, the electromagnetic fields reach the ground nearly simultaneously with regard to the operation time of systems. The aim of this analysis is to inquire about HEMP characteristics and to analyze about effect in orthogonal frequency division multiplexing (OFDM) system. Specifically, HEMP characteristics are classified field sources, spatial coverage, time domain behavior, frequency spectrum and field intensities in this study. Bits error rate (BER) of the receiver with the software simulation is confirmed for the HEMP effect. Q-factor made a difference about interference duration by transfer characteristics of system. When Q factor is smaller, the recovery time from HEMP interference is short. To the contrary, if the Q factor is larger, the recovery duration is lasted longer by 300-600%.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.