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• W4385482492 abstract "A real-time health structural diagnosis system for drones is becoming a key factor in developing safe drone operations and technologies. This study is dedicated to building one. Drone health status can be real-time diagnosed as non-fault or potential fault status. This study adds extra IMU sensors to collect the vibration signal of different drone structural faults and extracts the time domain and frequency domain features of the signal through feature engineering methods. Then, a Support Vector Machine (SVM) model is trained with those features. Feature selection and hyper-parameter tuning methods have been applied during model training to prevent model overfitting. This study also integrates a Window Sliding Technique and MAVLink to improve the real-time health diagnosis system." @default.
• W4385482492 created "2023-08-03" @default.
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• W4385482492 date "2023-06-28" @default.
• W4385482492 modified "2023-09-26" @default.
• W4385482492 title "Application of Support Vector Machine for Near Real-Time Health Structural Diagnosis for Drones" @default.
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• W4385482492 doi "https://doi.org/10.1109/aim46323.2023.10196114" @default.
• W4385482492 hasPublicationYear "2023" @default.
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