Lili Bai^1,*, Wenhui Li¹, He Ren^1,2, Feng Li¹, Tao Yan¹, Lirong Chen³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4513-4531, 2024, DOI:10.32604/cmc.2024.051348 - 20 June 2024

Abstract Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery, where weak fault characteristic signals hinder accurate fault state representation, we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform (FAWT) with Nonlinear Quantum Permutation Entropy. FAWT, leveraging fractional orders and arbitrary scaling and translation factors, exhibits superior translational invariance and adjustable fundamental oscillatory characteristics. This flexibility enables FAWT to provide well-suited wavelet shapes, effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults. In our approach,… More >

Xinfei Li², Xiaolan Xie^1,2,*, Yigang Tang², Qiang Guo^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351 - 28 July 2023

Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems.… More >

Wenchao Ma^*

Energy Engineering, Vol.120, No.7, pp. 1685-1699, 2023, DOI:10.32604/ee.2023.025404 - 04 May 2023

Abstract The power output state of photovoltaic power generation is affected by the earth's rotation and solar radiation intensity. On the one hand, its output sequence has daily periodicity; on the other hand, it has discrete randomness. With the development of new energy economy, the proportion of photovoltaic energy increased accordingly. In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation, this paper proposes the short-term prediction of photovoltaic power generation based on the improved multi-scale permutation entropy, local mean decomposition… More >