Wavelet Decomposition Reconstruction Research Articles

Research on noise reduction and data mining techniques for pavement dynamic response signals

Purpose This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals. Design/methodology/approach The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently. Findings The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present. Originality/value The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Study on Detection Method of Electrical Shock Current Based on Sliding Window and Wavelet Transform

With the popularization of the rural electricity utilization, the rural electric safety was still an immediate problem to be solved. It was difficult to exactly detect and judge electric shock signals in the summation leakage current on the low-voltage electric power grid. A detection method of electrical contact signals based on sliding window and wavelet multi-resolution method was proposed. Under the different signal-to-noise ratio level, the summation leakage current was reconstructed by wavelet decomposition reconstruction algorithm. According to the characteristic of the slow change of the normal leakage current and the rapid change of the electric shock current within a short time, the electric shock current were extracted from the restructured summation leakage current signal by sliding window method. The mean square error and correlation analysis between the extracted signal and the actual testing results were studied. The analysis results show that the proposed method could identify electric shock current in the summation leakage current among noise, and its detection precision was superior to single sliding window method.

Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

The Runoff Forecasting Model Based on Wavelet Adaptive Neural Fuzzy Inference System

Considering various the wavelet decomposition reconstruction technology and training cycle of adaptive neural fuzzy inference system, this article propose four runoff forecast model of wavelet analysis and adaptive neural fuzzy inference system integration, such as the long cycle based on Mallat algorithm in runoff prediction, the long cycle based on wavelet packet algorithm in runoff prediction, the short cycle based on Mallat algorithm in runoff prediction, the short cycle based on wavelet packet algorithm in runoff prediction, and illuminate the model of the principles, structures and procedures. This model is used in Tangnaihe station monthly runoff forecast which lies in the Huanghe source area. Simulation results are evaluated by the cycle decomposition coefficients and Nash-Sutcliffe coefficient; it shows that the long cycle based on Mallat algorithm is best, the short cycle based on wavelet packet algorithm is worst. The author analyzes the reason and makes some proposal.