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Variations in friction velocity with wind speed and height are studied under moderate (≥9 m s−1)-to-strong onshore wind conditions caused by three landfalling typhoons. Wind data are from a coastal 100-m tower equipped with 20-Hz ultrasonic anemometers at three heights. Results show that wind direction affects variations in friction velocity with wind speed. A leveling off or decrease in friction velocity occurs at a critical wind speed of ~20 m s−1 under strong onshore wind conditions. Friction velocity does not always decrease with height in the surface layer under typhoon conditions. Thus, height-based corrections on friction velocities using the model from Anctil and Donelan may not be reliable. Surface-layer heights predicted by the model that are based on Ekman dynamics are verified by comparing with those determined by a proposed method that is based on the idea of mean boundary layer using wind-profile data from one of the landfalling typhoons. Friction velocity at the top of the surface layer is then estimated. Results show that friction velocity decreases by about 20% from its surface value and agrees well with previous results of Tennekes.

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In recent decades, computer graphics have developed rapidly. With the development of computer graphics, some related technologies have also appeared one after another or have made great progress on the basis of the original. 3d laser scanner obtains the point cloud data of objects in a non-contact way, and the modelling can truly restore the original appearance of objects, so it has a great impact on urban architecture, power grid design and manufacturing. This paper introduces the collection and processing of point cloud data, and optimizes the processing efficiency of point cloud data, which greatly improves the processing speed of point cloud data and the accuracy of data display. Promote the application of point cloud data in various professional directions.

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Systems subjected to switching random excitations are practically significant because they include many safety-critical systems such as power plants and communication networks. In this paper, the reliability of multi-degree, nonlinear, non-integrable Hamiltonian systems subjected to switching random excitations is investigated. Such a system is formulated as a continuous-discrete hybrid based upon the Markov jump theory. Stochastic averaging is applied to suppress the rapidly varying parameters of the Markov jump process in order to generate a probabilityweighted diffusion equation. The associated backward Kolmogorov equation is then set up, from which the approximate reliability function and probability density of first passage time are obtained. The utility and accuracy of this approximate procedure are demonstrated by two examples.

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