This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.
This study aims to investigate burnout and study engagement among medical students at Sun Yat-sen University, China.A cross-sectional survey was conducted among undergraduate medical students of Sun Yat-sen University, China. A total of 453 undergraduate students completed a self-administered, structured questionnaire between January and February, 2016. Burnout and study engagement were measured using the Maslach Burnout Inventory-Student Survey (MBI-SS) and the UTRECHT Work Engagement Scale-Students (UWES-S), respectively. Subjects who scored high in emotional exhaustion subscale, high in cynicism subscale, and low in professional efficacy subscale simultaneously were graded as having high risk of burnout. Independent sample t tests and chi-square tests were used to compare the differences in burnout and work engagement between genders, majors, and grade levels.The means (standard deviations) of the MBI-SS subscales were 3.42 (1.45) for emotional exhaustion, 2.34 (1.64) for cynicism, and 3.04 (1.30) for professional efficacy. The means (standard deviations) of the UWES-S subscales were 3.13 (1.49) for vigor, 3.44 (1.47) for dedication and 3.00 (1.51) for absorption. Approximately 1 in 11 students experienced a high risk of burnout. There were no statistically significant gender differences in burnout and study engagement. There were also no statistically significant differences in burnout and study engagement subscales according to student major. Students in higher grades displayed increased burnout risk, higher mean burnout subscale score of cynicism, lower mean burnout subscale score of professional efficacy, and decreased mean study engagement subscale scores of dedication and absorption. There were strong correlations within study engagement subscales.Chinese medical students in this university experience a high level of burnout. Students at higher-grade level experience more burnout and decreased study engagement compared with students in lower level.
In this paper, a low-power high-response wireless structural health monitoring system (WSHMS) is designed, implemented and experimentally evaluated for impact detection in composite airframes. Due to the rare, random and transitory nature of impacts, an event-triggered mechanism is adopted for allowing the system to exhibit low power consumption when no impact occurs and high performance when triggered. System responsiveness, robustness and energy efficiency are considered and modelled. Based on system requirements and functions, several modules are proposed, including filtering, impact detecting, local processing and wireless communicating modules. The filtering module increases the system robustness by attenuating background vibration noises. The impact detection module monitors impact categories, and when the impact energy is above a certain threshold, it generates a trigger (wake-up) signal for the local processing module. The local processing module is required to be responsive to impact events, capable of processing multiple sensing inputs and energy-efficient when no impact occurs. The wireless module transmits the processed data to the host station for impact evaluation. The whole design was implemented on a printed circuit board (100 × 65 mm). The response time is around 12 µs with an average current consumption lower than 1 mA when the impact activity is lower than 0.1%. The system exhibits high robustness to ambient vibration noises and is also capable of accurately and responsively capturing multiple sensing input channels (up to 24 channels). This work presents a lowlatency energy-aware WSHMS for impact detection of composite structures. It can be adapted to monitor of other rare, random and ephemeral events in many Internet of Things applications.
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