Short isometric shapelet transform for binary time series classification

Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot

Temperature changes are a major challenge in outdoor guided wave structural health monitoring of rails. Temperature variations greatly impact the waveform of guided wave signals, making it challenging to diagnose and characterize defects. Traditional temperature compensation methods, such as signal stretch and scale transform, are restricted to use in regular structures, such as plates and pipes. To solve the temperature compensation problem in long rails with serious mode conversion and complex structure echo, we propose a temperature compensation and defect monitoring method, namely, sliding window dynamic time-series warping (SWDTW), which overcomes the challenges of mass computation and overcompensation of dynamic time-series warping (DTW). The basic idea of SWDTW is to utilize sliding windows to accelerate the computation and identify defects from subsequence scales. Then, an index, window subsequence Teager energy (WSTE), is used to indicate the local abnormality of guided wave signals, and a sliding window net (SWnet) is devised to monitor the occurrence of defects automatically. Outdoor monitoring of turnout rails showed that the proposed method can effectively reduce the temperature noise and recognize an artificial defect with 1.16% and 0.36% cross-sectional change rates (CSCRs) on the switch and stock rails, respectively, at different temperatures; moreover, the defect signals processed by SWDTW showed better defect identification performance than those processed by scale transform and DTW.

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

Aiming at some existing issues in the sports industry, the existing model is optimized by deep learning and time series theory based on the relevant algorithm, and the scale of the sports industry is analyzed and predicted by the model. The results show the following: (1) Based on the single-step prediction of time series, MNTS structural algorithm can be used to describe and study the sports industry scale with the single factor, and the correlation fitting degree is high. (2) Curves of different evaluation methods can include parts linear stage and nonlinear stage according to the magnitude of change. (3) Seen from the optimization model in this paper, the proposed method can describe both global and local trends of data. (4) It can be seen from the prediction curve that the overall state of fluctuation indicates that time will have a great impact on the relevant scale of the sports industry. Compared with single-step prediction, the accuracy of multistep prediction is higher, and the multistep prediction model based on time series can well characterize and predict the scale of the sports industry. By using the relevant time algorithm, the sports industry scale can be predicted and analyzed so as to provide theoretical support for the formulation and implementation of relevant policies.