JWJun Wu
Jun Wu
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jun Wu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ji Wang
School of Liberal Arts and Humanities, Sichuan Vocational College of Finance and Economics, Chengdu 610101, China
1 shared publication - Jinyu Xu
Hubei Univ Automot Technol, Coll Elect & Informat, 167 Chechengxi Rd, Shiyan 442002, Hubei, Peoples R China
1 shared publication - Yufeng Chen
Hubei University of Automotive Technology
1 shared publication - Zelin Zhang
School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
1 shared publication
Latest Publications
- Open Access
Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy
As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the and are higher than , which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy.Open AccessDistinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy
As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the and are higher than , which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy.