This paper adopts a combined optimization approach using Simulated Annealing and Genetic Algorithm based on the travel purpose as a determining criterion. It selects one destination among multiple travel destinations and derives the optimal route based on the latitude and longitude information of the destination. The research aims to overcome the limitations of traditional navigation software, which requires explicit destination input to plan a route, and achieve intelligent route planning based on travel purposes. By identifying the travel purpose and selecting appropriate destinations to form the optimal solution, this study successfully addresses the limitations of conventional travel planning. The combined algorithm makes full use of the global search capability of Simulated Annealing and the optimization effectiveness of Genetic Algorithm, achieving superior travel routes by optimizing the initial solution. Experimental results and comparative analysis demonstrate that the travel route planning method based on the combination of Simulated Annealing and Genetic Algorithm exhibits significant advantages in solving complex travel problems, providing users with intelligent and efficient travel decision support. This research contributes to the innovation and improvement of travel planning technology, bringing greater convenience and comfort to the travel experience.
Currently, the detection of wind turbine blade damage mainly relies on regular plan-based maintenance and manual inspections. In this study, a method for extracting audio features and detecting damage in wind turbine blades with wavelet denoising is proposed. This method first uses wavelet denoising to process the original audio signal, the denoised audio is then split into frames with Hamming windowing function. After that, multi-scale features are extracted in both time and frequency domains. Principal component analysis is used to reduce the dimensionality of the features, and clustering canters are obtained through K-means clustering analysis. Finally, Gaussian distribution outlier detection is used to detect audio signals from damaged blades. Experimental results using lab-generated audio data show that the proposed method has high accuracy and strong robustness in detecting wind turbine blade damage.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.