Blade surface reconstruction is one of the necessary steps in blade adaptive machining system. The majority of the current reconstruction methods are based on point-cloud, which is obtained using noncontact measurements. Correspondingly, these methods are invalid when the points are obtained using contact measurements. In this paper, we studied the method of surface reconstruction for thin features based on measured contact points. Owing to the random order of the measured points, and the fact that when the distance between two measured points is larger than the wall thickness of the blade will lead to interpolation errors, a quad tree-based region division method, and a points sorting algorithm, are proposed for correctly ordering the points. On this basis, inverse interpolation of cross-sectional curves using a cubic NURBS curve is used to fit them and reconstruct surfaces. Surface reconstruction examples are presented herein, and a comparison between the reconstructed and designed surfaces is used to verify the validity of our method.
The effect of non-acoustical factors including the type of visual environment on human noise perception has been found. Therefore, the perception of noise in the urban residential can be mitigated by the design of landscapes in the urban environment. This paper investigated the influence of visual environment on perceived loudness of urban noise to people. The experiment was under the virtual reality(VR) technique. A total of 80 audio-visual stimuli with 40 360° panoramic photos and two loudness ranges traffic noise (a low decibel range 50dB to 60dB and a high decibel range 60dB to 70dB) were presented by a virtual reality helmet and a headset. The 30 participants were asked to evaluate the visual environments and then listen to the audio stimuli. An loudness matching task was perform immediately afterwards by pressing increase or decrease button embedded in a neutral background without any visual scene. The difference of perceived loudness and actual loudness (overestimation or underestimation in dB) was recorded as dependent variable, whilst greening rate, degree of spatial openness, waterscape rate, artificial landscape rate, hue, colour saturation and lightness were calculated to be used as independent variable. The statistical results show that loudness difference is correlated with colour saturation (p=0.035) and greening rate (p=0.007) in the visual environments. People assessed to higher greening rate and colour saturation may be more sensitive to noise. In addition, initial results of linear fitting indicate that 17% increase of green rate might be linked with 1 dB overestimation of sound pressure level (SPL).
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