scholarly journals Multi-scale fluctuation analysis of precipitation in Beijing by Extreme-point Symmetric Mode Decomposition

2018 ◽  
Vol 379 ◽  
pp. 187-192
Author(s):  
Jiqing Li ◽  
Zhipeng Duan ◽  
Jing Huang
Keyword(s):  
Water Resources ◽  
Extreme Point ◽  
Global Climate ◽  
Fluctuation Analysis ◽  
Symmetric Mode ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Scale Fluctuation ◽  
Variance Contribution ◽  
Modal Component

Abstract. With the aggravation of the global climate change, the shortage of water resources in China is becoming more and more serious. Using reasonable methods to study changes in precipitation is very important for planning and management of water resources. Based on the time series of precipitation in Beijing from 1951 to 2015, the multi-scale features of precipitation are analyzed by the Extreme-point Symmetric Mode Decomposition (ESMD) method to forecast the precipitation shift. The results show that the precipitation series have periodic changes of 2.6, 4.3, 14 and 21.7 years, and the variance contribution rate of each modal component shows that the inter-annual variation dominates the precipitation in Beijing. It is predicted that precipitation in Beijing will continue to decrease in the near future.

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 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 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 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.

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 Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lei Yafei ◽  
Jiang Wanlu ◽  
Niu Hongjie ◽  
Shi Xiaodong ◽  
Yang Xukang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Extreme Point ◽  
Random Forests ◽  
Piston Pump ◽  
Support Vector ◽  
Symmetric Mode ◽  
Axial Piston Pump ◽  
Mode Decomposition ◽  
Axial Piston

Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. Firstly, the vibration signal of the axial piston pump was decomposed by ESMD to get several intrinsic mode functions (IMFs) and an adaptive global mean curve (AGMC) on the local side. Secondly, the total energy was selected as the data of feature extraction by analyzing the whole oscillation intensity of the signal. Thirdly, the data were preprocessed and the labels were set, and then, they were adopted as the training and testing set of machine learning samples. Lastly, the RFs model was created based on machine learning service (MLS) to diagnose the faults of the axial piston pump on the cloud. Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with classification and regression trees (CART) and support vector machine (SVM).

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