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High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.
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http://dx.doi.org/10.3390/s21041231 | DOI Listing |
Front Plant Sci
August 2025
College of Agriculture, Shihezi University, Shihezi, China.
Introduction: Existing facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. These limitations significantly constrain the effectiveness of precise environmental regulation in agricultural facilities.
Methods: To address these challenges, this paper proposes a novel facility environment prediction model (LSTM-AT-DP) integrating Long Short-Term Memory networks with attention mechanisms and advanced data preprocessing.
Rev Sci Instrum
September 2025
Department of Electric Power Engineering, North China Electric Power University, Baoding, Hebei Province 071003, China.
To effectively solve the problems of white noise and periodic narrowband interference in partial discharge detection, this paper proposes a partial discharge denoising method that combines the optimization of successive variational mode decomposition (SVMD) and wavelet threshold denoising. Compared to variational mode decomposition, SVMD does not require the pre-setting of the number of decomposition modes. However, it is affected by the balance parameter.
View Article and Find Full Text PDFIEEE Trans Image Process
August 2025
Due to the lack of prior knowledge about unknown classes during training, existing methods for cross-domain open-set image recognition typically rely on threshold-based solutions. However, such approaches often struggle to capture the complex boundary relationships between known and unknown classes, which can lead to negative transfer effects caused by feature confusion between the two. To address this issue, this paper proposes a graph isomorphic distillation diffusion model (GIDDM) that aims to learn the boundary relationships between known and unknown classes from a closed-set classifier that models predictive uncertainty.
View Article and Find Full Text PDFMikrochim Acta
August 2025
School of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China.
This study presents a wearable multi-parameter electrochemical detection system (WMEDS) designed for the simultaneous monitoring of sweat biomarkers. The system integrates a microcontroller, a dual-mode signal acquisition circuit, and a low-power Bluetooth module. To address the non-stationary electrochemical signals, a preprocessing approach combining Savitzky-Golay filtering with Z-score-difference detection is employed.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
January 2025
One crucial factor for dissimilarity of microbiome studies is the choice between denoising or clusterization algorithms, respectively. Moreover, the robustness, or stability of these algorithms with respect to the number of sequences computed, and its effect on the calculated ecological metrics of the microbiome studied, are currently unknown. In this study, mock communities were used for the investigation of robustness of several denoising and clusterization algorithms.
View Article and Find Full Text PDF