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This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization-back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal.
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Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time-frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time-frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/âHz to 6.65894 × 10-3°/h/âHz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.
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[This corrects the article DOI: 10.1039/D0RA10336E.].
RESUMEN
In this work, based on the electrospinning method, pure Co3O4, pure MnO2, and Co3O4 composite nano-fiber materials doped with different ratios of Mn4+ were prepared. XRD, XPS, BET and SEM tests were used to characterize the composition, structure and morphology of the materials. An electrochemical workstation was used to test the electrochemical performance of the materials. The results showed that the material properties had greatly improved on doping Mn4+ in Co3O4 nano-fibers. The relationship between the amount of Mn4+ doped in the Co3O4 composite nano-fiber material and its electrochemical performance was also tested and is discussed in this report. The results show that when n Co : n Mn = 20 : 2, the Co3O4 composite nano-fiber material had a specific surface area of 68 m2 g-1. Under the current density of 1 A g-1, the 20 : 2 sample had the maximum capacitance of 585 F g-1, which was obviously larger than that of pure Co3O4 nano-fibers (416 F g-1). After 2000 cycles of charging/discharging, the specific capacitance of the 20 : 2 sample was 85.9%, while that of the pure Co3O4 nano-fiber material was only 76.4%. The mechanism of performance improvement in the composite fibers was analyzed, which demonstrated concrete results.