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Underlying Trend Extraction via Joint Ensemble Intrinsic Timescale Decomposition Algorithm and Matching Pursuit Approach

Published on Feb 22, 2019in Circuits Systems and Signal Processing1.92
· DOI :10.1007/s00034-019-01069-2
Xiaoling Wang (GDUT: Guangdong University of Technology), Bingo Wing-Kuen Ling15
Estimated H-index: 15
(GDUT: Guangdong University of Technology)
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Abstract
Time series usually consist of an underlying trend and the irregularities. Therefore, the underlying trend extraction plays an important role in the analysis of the time series. This paper proposes a method which combines the ensemble intrinsic timescale decomposition (EITD) algorithm and the matching pursuit (MP) approach for performing the underlying trend extraction. In order to extract the underlying trend, the EITD algorithm is applied to obtain a set of components. Then, the first component which contains most of the noise is removed. Next, some appropriate components are selected by the MP approach. In particular, the total number of components that composites of the underlying trend is minimized. To guarantee that the underlying trend tracks the signal, it is required to impose a constraint on the maximum absolute error between the underlying trend and the denoised signal. As the selection of the EITD components is binary, this component selection problem is formulated as an \( L_{0} \)-norm binary programming problem subject to the specification on the maximum absolute error between the denoised signal and the underlying trend. This optimization problem is further solved by the MP approach. Finally, the underlying trend is constructed. Compared with the conventional empirical mode decomposition algorithm and the ensemble empirical mode decomposition algorithm, our proposed approach could extract a better underlying trend for some random time series.
  • References (23)
  • Citations (0)
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References23
Newest
Xiangpeng Xie12
Estimated H-index: 12
(NUPT: Nanjing University of Posts and Telecommunications),
Dong Yue48
Estimated H-index: 48
(NUPT: Nanjing University of Posts and Telecommunications)
+ 1 AuthorsYusheng Xue3
Estimated H-index: 3
(EPRI: Electric Power Research Institute)
This paper investigates the problem of robust fault estimation (FE) observer design for discrete-time Takagi–Sugeno fuzzy systems via homogenous polynomially parameter-dependent Lyapunov functions. First, a novel framework of the fuzzy FE observer is established with the help of a maximum–minimum-priority-based switching mechanism. Then, for every activated switching case, a targeted result is achieved by the aid of exploring an important property of improved homogenous polynomials. Since the he...
Published on Jul 1, 2017 in SMC (Systems, Man and Cybernetics)
Xiangpeng Xie12
Estimated H-index: 12
(NUPT: Nanjing University of Posts and Telecommunications),
Dong Yue48
Estimated H-index: 48
(NUPT: Nanjing University of Posts and Telecommunications),
Songlin Hu9
Estimated H-index: 9
(NUPT: Nanjing University of Posts and Telecommunications)
Robust fault estimation (FE) observer designs for discrete-time nonlinear system are deeply discussed via a joint real-time scheduling law. Different from previous results involved in the field, a fresh FE observer is produced by means of intermittently launching a joint real-time scheduling law in the light of updated multi-instant information. Thanks to the overall vision of system information across several adjacent sampled points, less conservative result is favorably acquired. Second, the g...
Muhammad S. Ullah2
Estimated H-index: 2
(UMKC: University of Missouri–Kansas City),
Masud H. Chowdhury11
Estimated H-index: 11
(UMKC: University of Missouri–Kansas City)
Continuous shrinking of the size of CMOS technology leads to extremely fast devices, but the resulting interconnect structures impose so many parasitic effects that the advantage of extremely scaled and ultrahigh-speed transistors would be completely overshadowed if appropriate remedial steps are not taken. This requires an accurate and efficient estimation of interconnect parasitics and analysis of their impact on integrated circuit performance. This paper proposes a new delay model for RLC int...
Published on Feb 1, 2017in Journal of Zhejiang University Science C
Jun-hong Zhang1
Estimated H-index: 1
(TJU: Tianjin University),
Yu Liu1
Estimated H-index: 1
(TJU: Tianjin University)
Targeting the mode-mixing problem of intrinsic time-scale decomposition (ITD) and the parameter optimization problem of least-square support vector machine (LSSVM), we propose a novel approach based on complete ensemble intrinsic time-scale decomposition (CEITD) and LSSVM optimized by the hybrid differential evolution and particle swarm optimization (HDEPSO) algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mi...
Published on Jun 1, 2016in Iet Signal Processing1.75
Ya Li2
Estimated H-index: 2
(GDUT: Guangdong University of Technology),
Langxiong Xie2
Estimated H-index: 2
(GDUT: Guangdong University of Technology)
+ 2 AuthorsQingyun Dai4
Estimated H-index: 4
(GDUT: Guangdong University of Technology)
This study proposes an iterative method to approximate an N-dimensional optimisation problem with a weighted L p and L 2 norm objective function by a sequence of N independent one-dimensional optimisation problems. Inspired by the existing weighted L 1 and L 2 norm separable surrogate functional (SSF) iterative shrinkage algorithm, there are N independent one-dimensional optimisation problems with weighted L p and L 2 norm objective functions. However, these optimisation problems are non-convex....
Published on Nov 1, 2015in Renewable Energy5.44
Aijun Hu1
Estimated H-index: 1
(NCEPU: North China Electric Power University),
Xiaoan Yan1
Estimated H-index: 1
(NCEPU: North China Electric Power University),
Ling Xiang1
Estimated H-index: 1
(NCEPU: North China Electric Power University)
In this paper an ensemble intrinsic time-scale decomposition (EITD) method based on the cubic spline interpolation and linear transformation of intrinsic time-scale decomposition (ITD) was proposed, which can restrain the end effect and avoid the signal distortion. Combining ensemble intrinsic time-scale decomposition (EITD) with wavelet packet transform (WPT) and correlation dimension (CD), a novel method for decomposing nonstationary vibration signal and diagnosing wind turbine faults is prese...
Published on Aug 14, 2014in New Journal of Physics3.77
Juan M. Restrepo18
Estimated H-index: 18
(UA: University of Arizona),
Shankar C. Venkataramani14
Estimated H-index: 14
(UA: University of Arizona)
+ 1 AuthorsHermann Flaschka16
Estimated H-index: 16
(UA: University of Arizona)
We propose criteria that define a trend for time series with inherent multi-scale features. We call this trend the tendency of a time series. The tendency is defined empirically by a set of criteria and captures the large-scale temporal variability of the original signal as well as the most frequent events in its histogram. Among other properties, the tendency has a variance no larger than that of the original signal; the histogram of the difference between the original signal and the tendency i...
Published on Dec 1, 2013in Science China-mathematics1.03
Thomas Y. Hou41
Estimated H-index: 41
(California Institute of Technology),
Zuoqiang Shi11
Estimated H-index: 11
(THU: Tsinghua University)
Adaptive data analysis provides an important tool in extracting hidden physical information from multiscale data that arise from various applications. In this paper, we review two data-driven time-frequency analysis methods that we introduced recently to study trend and instantaneous frequency of nonlinear and nonstationary data. These methods are inspired by the empirical mode decomposition method (EMD) and the recently developed compressed (compressive) sensing theory. The main idea is to look...
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