Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies.

Tail-sitter unmanned aerial vehicles (UAVs) have gained extensive popularity in recent years due to their inherent advantages of both fixed wing and rotary wing UAVs. However, these advantages are accompanied with control challenges because of two different flight regimes and drastically changing dynamics during transition flights. This paper focuses on the design of a unified controller free from cumbersome controller switchings and applicable in all attitude range for a tail-sitter with single thrust-vectored propeller. To achieve this, both thrust vectoring model and full-regime aerodynamics model are built first, after which a complete attitude dynamics model of the tail-sitter is established utilizing the quaternion attitude description to avoid the singularity problem. An adaptive controller is then derived based on a simplified model using the Lyapunov stability theory with unknown system parameters identified online by forgetting factor recursive least square (FF-RLS) method. Flight experiments are conducted to demonstrate the feasibility and effectiveness of the proposed control scheme.