• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Smart Structures and Systems
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• v.2 no.2
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• pp.141-154
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• 2006

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.283-294
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• 2020

In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.127-132
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• 2021

This paper applies a machine learning model to forecasting freight rates in dry bulk and tanker markets with wavelet decomposition and empirical mode decomposition because they can refect both information scattered in the time and frequency domain. The decomposition with wavelet is outperformed for the dry bulk market, and EMD is the more proper model in the tanker market. This result provides market players with a practical short-term forecasting method. This study contributes to expanding a variety of predictive methodologies for one of the highly volatile markets. Furthermore, the proposed model is expected to improve the quality of decision-making in spot freight trading, which is the most frequent transaction in the shipping industry.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.395-404
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• 2008

We explore the linkage between the long term variability of the Sun and earth's climate change by analysing periodicities of time series of solar proxies and global temperature anomalies. We apply the power spectral estimation method named as the periodgram to solar proxies and global temperature anomalies. We also decompose global temperature anomalies and reconstructed total solar irradiance into each local variability components by applying the EMD (Empirical Mode Decomposition) and MODWT MRA (Maximal Overlap Discrete Wavelet Multi Resolution Analysis). Powers for solar proxies at low frequencies are lower than those of high frequencies. On the other hand, powers for temperature anomalies show the other way. We fail to decompose components which having lager than 40 year variabilities from EMD, but both residuals are well decomposed respectively. We determine solar induced components from the time series of temperature anomalies and obtain 39% solar contribution on the recent global warming. We discuss the climate system can be approximated with the second order differential equation since the climate sensitivity can only determine the output amplitude of the signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.351-358
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• 2017

A new method to decompose the footprints of marine atmosphere boundary layer (MABL) on Synthetic Aperture Radar (SAR) imagery into characteristic spatial scales is proposed. Using two-dimensional Empirical Mode Decomposition (EMD) we obtain three Intrinsic Mode Functions (IMFs), which mainly present longitudinal rolls, three-dimensional cells and atmospheric gravity waves (AGW). The rolls and cells have spatial scales between 3.0 km and 3.8 km and between 5.3 km and 7.1 km, respectively. Based on previous observations and mixed-layer similarity theory, we estimated MABL's depths that vary from 0.95 km to 1.2 km over the rolls and from 3.0 km to 3.8 km over the cells. The AGW has maximum spectrum at 14.3 km wavelength. The method developed in this work can be used to decompose other satellite imageries into individual features through characteristic spatial scales.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.315-341
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• 2007

A sudden change of stiffness in a structure, associated with the events such as weld fracture and brace breakage, will cause a discontinuity in acceleration response time histories recorded in the vicinity of damage location at damage time instant. A new damage index is proposed and implemented in this paper to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. The proposed damage index is suitable for online structural health monitoring applications. It can also be used in conjunction with the empirical mode decomposition (EMD) for damage detection without using the intermittency check. Numerical simulation using a five-story shear building under different types of excitation is executed to assess the effectiveness and reliability of the proposed damage index and damage detection approach for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also examined. The results from this study demonstrate that the damage index and damage detection approach proposed can accurately identify the damage time instant and location in the building due to a sudden loss of stiffness if measurement noise is below a certain level. The relation between the damage severity and the proposed damage index is linear. The wavelet-transform (WT) and the EMD with intermittency check are also applied to the same building for the comparison of detection efficiency between the proposed approach, the WT and the EMD.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.109-117
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• 2008

In this study, a method predicting the displacement responseof structures from the measured dynamic strain signal is proposed by using a mode decomposition technique. Dynamic loadings including wind and seismic loadings could be exerted to the bridge. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. To overcome such a problem, a mode decomposition technique was used in this study. The measured strain signal is decomposed into each modal component by using the empirical mode decomposition(EMD) as one of mode decomposition techniques. Then, the decomposed strain signals on each modal component are transformed into the modal displacement components. And the corresponding mode shapes can be also estimated by using the proper orthogonal decomposition(POD) from the measured strain signal. Thus, total displacement response could be predicted from combining the modal displacement components.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.