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The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. [ABSTRACT FROM AUTHOR]

The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. [ABSTRACT FROM AUTHOR]

Based on non-affine models of hypersonic space vehicles, the tracking control problem of hypersonic vehicles is studied and analyzed in this article using funnel robust model-free control mechanism considering parametric uncertainty and external disturbances. First, the control system is decomposed into altitude subsystem and velocity subsystem. For altitude subsystem, we propose a concise funnel robust model-free control mechanism based on error driving, and a novel model transformation approach is applied to the controller design. The new model-free controller only contains a Hurwitz stable term and a filtering term, and does not need precise motion model and too much calculation, so it can improve the calculation speed of the system. For velocity subsystem, only a concise proportional-integral controller is needed to meet the tracking requirements. Moreover, the devised controller is capable of guaranteeing funnel performance on the altitude and velocity tracking errors. Finally, numerical simulation results are presented to verify the efficiency of the design. [ABSTRACT FROM AUTHOR]