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Traditional noise barriers are often designed only by considering its noise reduction effects, but designer ignores that it may transfer too much aerodynamic force to the bridge. In order to meet the wind resistance and noise reduction requirements of the elevated lines crossing an urban area at the same time, a new type of wind–noise barrier (NT-WNB) is proposed. The noise reduction effect is evaluated by a numerical method, and the influence of the wind–noise barriers’ rotation angle on the aerodynamic characteristics of a train–bridge system was studied by sectional model wind tunnel tests. The results show that the NT-WNB has effective noise reduction in the frequency range of 500–1600 Hz, and the noise reduction can be increased when install barriers with upward incline blade. Although an angle combination type of wind–noise barrier can optimize the lateral force and the lift of the train at the same time, which may cause high turbulence in the corresponding area. The NT-WNB can reduce the wind load of the bridge–barrier system by 22%, which is more conducive to the safety of the bridge and the barrier.

One critical issue for existing face recognition (FR) systems is to ensure its accuracy and robustness, which calls for the development of face anti-spoofing (FAS) algorithms to work against presentation attacks (PA). This letter proposes a novel Multi-level Attention Constraint Network with a Refined Triplet Loss (MACN-RTL) for the task of FAS. Specifically, an MACN which consists of two components is designed, that is, Multi-level Attention Network (MAN) and Distribution Constraint (DC). MAN aims to exploit effective information from different levels, while DC helps to learn a more compact and discriminative feature embedding for classification. Besides, a Refined Triplet Loss for better model optimisation is devised. Compared with existing FAS works, MACN is designed for better feature extraction and a better solution for optimisation by RTL. Experimental results demonstrate the superiority of the proposed approach.

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