Extraction of Bridge Frequencies Inclusive of the Higher Modes by the ESMD Using the Contact-Point Response

2020 ◽  
Vol 20 (04) ◽  
pp. 2050045 ◽  
Author(s):  
Y. B. Yang ◽  
F. Xiong ◽  
Z. L. Wang ◽  
H. Xu
Keyword(s):  
Extreme Point ◽  
Systematic Study ◽  
Contact Point ◽  
Effective Procedure ◽  
Symmetric Mode ◽  
University Campus ◽  
Intrinsic Mode Functions ◽  
Higher Modes ◽  
Mode Decomposition ◽  
Better Than

An effective procedure is proposed for extracting bridge frequencies including the higher modes using the vehicle collected data. This is enabled by the use of the contact-point response, rather than the vehicle response, for processing by the extreme-point symmetric mode decomposition (ESMD). The intrinsic mode functions (IMFs) so decomposed are then processed by the FFT to yield the bridge frequencies. A systematic study is conducted to compare the proposed procedure with existing ones, while assessing the effects of various parameters involved. The proposed procedure was verified in the field for a two-span bridge located at the Chongqing University campus. It was confirmed to perform better than the existing ones in extracting bridge frequencies inclusive of the higher modes. The following are the reasons: (1) the ESMD is more efficient than the EMD in that remarkably less IMFs are generated; (2) the modal aliasing problem is largely alleviated, which helps enhancing the visibility of bridge frequencies in general; and (3) the contact-point response adopted is free of the vehicle frequency, which makes the higher frequencies more outstanding and detectable.

scholarly journals A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xianglei Liu ◽  
Mengzhuo Jiang ◽  
Ziqi Liu ◽  
Hui Wang
Keyword(s):  
Extreme Point ◽  
Filter Method ◽  
Signal Denoising ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Important Indicator ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Microwave Interferometry ◽  
Morphology Filter

Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

scholarly journals EXTREME-POINT SYMMETRIC MODE DECOMPOSITION METHOD FOR DATA ANALYSIS

2013 ◽  
Vol 05 (03) ◽  
pp. 1350015 ◽  
Author(s):  
JIN-LIANG WANG ◽  
ZONG-JUN LI
Keyword(s):  
Extreme Point ◽  
Instantaneous Frequency ◽  
Extreme Points ◽  
Least Square Method ◽  
Least Square ◽  
Symmetric Mode ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Better Than

An extreme-point symmetric mode decomposition (ESMD) method is proposed to improve the Hilbert–Huang Transform (HHT) through the following prospects: (1) The sifting process is implemented by the aid of 1, 2, 3 or more inner interpolating curves, which classifies the methods into ESMD_I, ESMD_II, ESMD_III, and so on; (2) The last residual is defined as an optimal curve possessing a certain number of extreme points, instead of general trend with at most one extreme point, which allows the optimal sifting times and decompositions; (3) The extreme-point symmetry is applied instead of the envelop symmetry; (4) The data-based direct interpolating approach is developed to compute the instantaneous frequency and amplitude. One advantage of the ESMD method is to determine an optimal global mean curve in an adaptive way which is better than the common least-square method and running-mean approach; another one is to determine the instantaneous frequency and amplitude in a direct way which is better than the Hilbert-spectrum method. These will improve the adaptive analysis of the data from atmospheric and oceanic sciences, informatics, economics, ecology, medicine, seismology, and so on.

scholarly journals Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1388 ◽  
Author(s):  
Dongyong Sun ◽  
Hongbo Zhang ◽  
Zhihui Guo
Keyword(s):  
Climate Change ◽  
Correlation Coefficient ◽  
Extreme Point ◽  
Time Scales ◽  
Approximate Entropy ◽  
Monthly Precipitation ◽  
Symmetric Mode ◽  
Mode Decomposition ◽  
Different Time Scales ◽  
The Imf

Many regional hydrological regime changes are complex under the influences of climate change and human activities, which make it difficult to understand the regional or basin al hydrological status. To investigate the complexity of precipitation and the runoff time series from 1960 to 2012 in the Jing River Basin on different time scales, approximate entropy, a Bayesian approach and extreme-point symmetric mode decomposition were employed. The results show that the complexity of annual precipitation and runoff has decreased since the 1990sand that the change occurred in 1995. The Intrinsic Mode Function (IMF)-6 component decomposed by extreme-point symmetric mode decomposition of monthly precipitation and runoff was consistent with precipitation and runoff. The IMF-6 component of monthly precipitation closely followed the 10-year cycle of change, and it has an obvious correlation with sunspots. The correlation coefficient is 0.6, representing a positive correlation before 1995 and a negative correlation after 1995. However, the IMF-6 component of monthly runoff does not have a significant correlation with sunspots, and the correlation coefficient is only 0.41, which indicates that climate change is not the dominant factor of runoff change. Approximate entropy is an effective analytical method for complexity, and furthermore, it can be decomposed by extreme-point symmetric mode decomposition to obtain the physical process of the sequences at different time scales, which helps us to understand the background of climate change and human activity in the process of precipitation and runoff.

scholarly journals Extreme-Point Symmetric Mode Decomposition to Define the Turbulence Characteristics of a Flume Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhi-Peng Shi ◽  
Ting-Ting He ◽  
Gen-Guang Zhang
Keyword(s):  
Extreme Point ◽  
Instantaneous Velocity ◽  
Symmetric Mode ◽  
Modal Decomposition ◽  
Two Phase ◽  
Turbulence Characteristics ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Two Stages ◽  
Solid Liquid

Turbulence is a key feature of solid-liquid two-phase flows, and the pulsating velocity is the basis for calculating turbulence characteristics. In general, the method of mathematical expectation is used to calculate pulsating velocity. However, this method does not reflect the fluctuating state of the instantaneous velocity. Therefore, the method of extreme-point symmetric mode decomposition (ESMD) is adopted to calculate pulsating velocity and turbulence characteristics. The ESMD involves two stages, namely, modal decomposition and time-frequency analysis. The optimal adaptive global mean (AGM), which is the result of modal decomposition, can accurately reflect the fluctuation state of the instantaneous velocity, and the theory of the pulsating velocity defined on this basis is reasonable. Moreover, the flow pattern and turbulence behaviour of a two-phase flow can be predicted using the calculated turbulence characteristics. The method is used to analyse the pulsating velocity of the flume, and its rationality in theoretically predicting the turbulence behaviour of flume flows is demonstrated.

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