• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.2
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• pp.301-314
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• 2015

Aiming at accurately distinguishing modeless component and natural vibration mode terms from data series of nonlinear and non-stationary processes, such as Vortex-Induced Vibration (VIV), a new empirical mode decomposition method has been developed in this paper. The key innovation related to this technique concerns the method to decompose modeless component from non-stationary process, characterized by a predetermined 'maximum intrinsic time window' and cubic spline. The introduction of conceptual modeless component eliminates the requirement of using spurious harmonics to represent nonlinear and non-stationary signals and then makes subsequent modal identification more accurate and meaningful. It neither slacks the vibration power of natural modes nor aggrandizes spurious energy of modeless component. The scale of the maximum intrinsic time window has been well designed, avoiding energy aliasing in data processing. Finally, it has been applied to analyze data series of vortex-induced vibration processes. Taking advantage of this newly introduced empirical decomposition method and mode identification technique, the vibration analysis about vortex-induced vibration becomes more meaningful.

• 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.16 no.5
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• pp.919-935
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• 2015

The Hilbert-Huang transform (HHT) consists of empirical mode decomposition (EMD) and Hilbert spectral analysis. EMD has been successfully applied for identification of mode shapes of structures based on input-output approaches. This paper aims to extend application of EMD for output-only identification of mode shapes of linear structures. In this regard, a new simple and efficient method based on band-pass filtering and EMD is proposed. Having rather accurate estimates of modal frequencies from measured responses, the proposed method is capable to extract the corresponding mode shapes. In order to evaluate the accuracy and performance of the proposed identification method, two case studies are considered. In the first case, the performance of the method is validated through the analysis of simulated responses obtained from an analytical structural model with known dynamical properties. The low-amplitude responses recorded from the UCLA Factor Building during the 2004 Parkfield earthquake are used in the second case to identify the first three mode shapes of the building in three different directions. The results demonstrate the remarkable ability of the proposed method in correct estimation of mode shapes of the linear structures based on rather accurate modal frequencies.

• 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.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1807-1813
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• 2018

In this paper, the fault line selection and location problems of single line-to-ground (SLG) fault in distribution network are addressed. Firstly, the adaptive filtering property for empirical mode decomposition is formulated. Then in view of the different characteristics showed by the intrinsic mode functions(IMF) under different fault inception angles obtained by empirical mode decomposition, the sign of peak value about the low-frequency IMF and the capacitance transient energy is chosen as the fault line selection criteria according to the different proportion occupied by the low-frequency components. Finally, the fault location is determined based upon the comparison result with adjacent fault passage indicators' (FPI) waveform on the strength of the interaction between the distribution terminal unit(DTU) and the FPI. Moreover, the logic nodes regarding to fault line selection and location are newly expanded according to IEC61850, which also provides reference to acquaint the DTU or FPI's function and monitoring. The simulation results validate the effectiveness of the proposed fault line selection and location methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.8
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• pp.887-895
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• 2012

Lamb waves have received a great attention in the structural health monitoring (SHM) societies because they can propagate over relatively large distances in wave guides such as thin plates and shells. The time-of-flights of Lamb waves can be used to detect damages in a wave guide. However, due to the inherent dispersive and multi-mode characteristics of Lamb waves, one must decompose the Lamb wave modes into the symmetric and anti-symmetric modes for SHM applications. Thus, this paper proposes a decomposition method for the two-mode Lamb waves based on two rules: the group velocity ratio rule and the mode amplitude ratio rule. The group velocity ratio rule means that the ratio of the group velocities of fundamental symmetric and anti-symmetric modes is constant, while the mode amplitude ratio rule means that the magnitude of the fundamental symmetric modes of all measured response signals should be always larger than those of the anti-symmetric mode once the input signal is applied so that the magnitude of fundamental symmetric mode of excited Lamb-wave is larger than that of anti-symmetric mode, and vice versa. The proposed method is verified through the experiments ducted for an aluminum plate specimen.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.264-268
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• 2007

Proper orthogonal decomposition(POD) is a method for extracting bases for modal decomposition from the ensemble of signals. We verified the connection of the proper orthogonal modes(POMs) and the linear normal modes(LNMs) through MATLAB simulation for the simple cantilever and AFM microcantilever models. Using the POMs, we can analyze and model effectively the dynamic mode of AFM microcantievers.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.9
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• pp.675-681
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• 2014

This paper proposes a fault diagnosis system for an induction motor. This system uses empirical mode decomposition(EMD) to extract fault signatures and multi-layer perceptron(MLP) neural network to facilitate an accurate fault diagnosis. EMD can not only decompose a signal adaptively but also provide intrinsic mode functions(IMFs) containing natural oscillatory modes of the signal. However, every IMF does not represent fault signature, an IMF selection algorithm based on harmonics and their energy of each IMF is proposed. The selected IMFs are utilized for fault classification using MLP and this system shows approximately 98 % diagnosis accuracy for the fault vibration signal of the induction motor.