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Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial-Temporal Features Fusion. [PDF]
Sensors (Basel)Wang J, Yuan Y, Shen F, Chen C.
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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction. [PDF]
Front Public HealthGuo LR, Tan J, Hughes MC.
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Variational Mode Decomposition
IEEE Transactions on Signal Processing, 2014During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical
Dominique Zosso
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Difference mode decomposition for adaptive signal decomposition
Mechanical Systems and Signal Processing, 2023Bingchang Hou, Dong Wang, Tangbin Xia
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Dynamic mode decomposition with memory
Physical Review E, 2023This study proposed a numerical method of dynamic mode decomposition with memory (DMDm) to analyze multidimensional time-series data with memory effects. The memory effect is a widely observed phenomenon in physics and engineering and is considered to be the result of interactions between the system and environment.
Ryoji, Anzaki +4 more
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The Journal of the Acoustical Society of America, 1990
A method of mode decomposition by averaging over broadband components of impulse signals propagated over long ranges is investigated here for simulations and explosive data collected in the Arctic on a vertical array. This method is based on the assumption that cross-modal terms of the covariance matrix are minimized when averaging over broadband ...
R. Kille, T. C. Yang
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A method of mode decomposition by averaging over broadband components of impulse signals propagated over long ranges is investigated here for simulations and explosive data collected in the Arctic on a vertical array. This method is based on the assumption that cross-modal terms of the covariance matrix are minimized when averaging over broadband ...
R. Kille, T. C. Yang
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Shunt sound decomposition by empirical mode decomposition
2020 IEEE REGION 10 CONFERENCE (TENCON), 2020Unpredictable disequilibrium syndrome and blood pressure fluctuations can occur during hemodialysis therapy. We have proposed a method for analyzing shunt sounds using EMD to predict these symptoms. The shunt sound is the sound of turbulent blood flow generated in the shunt, which can be measured from the puncture needle of the dialyzer.
Yuki Otake, Osamu Sakata
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WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2011The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely ...
Faltermeier, R +4 more
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Symbolic extended dynamic mode decomposition
Chaos: An Interdisciplinary Journal of Nonlinear ScienceIn this paper, we present a new method of performing extended dynamic mode decomposition (EDMD) on systems, which admit a symbolic representation. EDMD generates estimates of the Koopman operator, K, for a dynamical system by defining a dictionary of observables on the space and producing an estimate, Km, which is restricted to be invariant on the span
Connor Kennedy +2 more
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