Under the background of collaborative innovation, interdisciplinary research organizations due to its structural advantages should actively target frontier science and the great needs of national development, key research and strategic issues of solving the country's need, prospective issues in the frontier of science and technology and major welfare issues related to people's livelihood. And it requires reasonable localization of interdisciplinary research organizations in universities. Facing the difficulties participated in collaborative innovation, they should establish a perfect operation mechanism, play the role of collaborative innovation platform, and actively transform to collaborative innovation center scientifically and effectively.
Keywords: Collaborative innovation, University interdisciplinary research organizations, Collaborative innovation platform
The advantage of collaborative innovation in university interdisciplinary research organizations
Generating paint gun path for complex free-form surfaces in shipbuilding industry to ensure uniform paint deposition is highly challenging due to their complex geometry, especially for horns surfaces. In this paper, a tool trajectory optimization method was proposed based on dip angle spray in order to solve the problems of poor coating uniformity and material waste for spraying horns surface with curved patches. A dip angle spray model was developed based on the Beta distribution model on the planar surface by principle of differential geometry. The spray path optimization models for dip angle spraying on cylindrical surfaces of three cases were proposed and compared first to define the best case which yields the best painting quality. Then we used the best strategy on a horns surface in a shipyard to evaluate the effectiveness and robustness of designed algorithm. This algorithm can also be extended to other applications.
Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.
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