Premise of the StudyVarious nondestructive testing technologies have been used for detecting and visualizing internal defects in urban trees. The results obtained by using different nondestructive testing tools can be interpreted in different ways, which may result in inaccurate assessment of the true condition of the inspected trees. The main objective of this study was to evaluate the reliability of acoustic tomography and ground‐penetrating radar (GPR) technology for detecting internal decay in a number of different tree species.MethodsOne hundred and forty‐seven individual trees of 33 species were inspected at a historic park in Yangzhou, Jiangsu Province, China, using a combination of visual inspection, acoustic tomography, GPR scanning, and resistance micro‐drilling methods. Special image processing procedures were developed to analyze the acoustic and radar images and to estimate the proportion of compromised wood.ResultsThe acoustic tomography tests revealed 10 defective trees with acoustic shadows suggesting compromised wood in more than 10% of the cross‐section area. The actual nature of these defects on living trees can be ascertained by conducting resistance micro‐drilling at selected paths. The Tree Radar Unit (TRU) System produced 85% false positive predictions and thus was not successful in visualizing the true physical conditions of the trees.ConclusionsAcoustic tomography can successfully detect trees with internal decay and cavities. A combination of visual inspection, acoustic tomography, and resistance micro‐drilling is an effective approach to detect, measure, and visualize internal defects on a diversity of tree species. The image processing procedures we developed make possible the quantitative analysis of compromised wood and could improve the science‐based tree risk assessment process. In contrast, the TRU System presented challenges in field applications, particularly on trees with small diameters and irregularly shaped trunks. The images obtained in this study using the TRU system were largely inaccurate and not reliable for tree inspection.

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In plate rolling, the existence of some asymmetry rolling conditions, affects the distribution of transversal thickness of rolling pieces, leading to the occurrence of camber phenomenon, which in turn seriously affect the product's material yield. In this paper, based on the images of plates in the production line collected by camera, taking into account the requirements for on-line measurement, filtering noise from image information, use edge detection operator, Hough transform methods and sub pixel edge location method to identify the steel plates, have achieved in-line measurement of camber curvature of plate side. At the same time, combined with camber control model, we have developed camber curvature measurement and control system, realized the automatic correction control of camber curvature of rolling pieces in the production process. Field applications show that this method can reduce the human intervention, will limit the camber curvature of rolling pieces within a certain range, which has great significance for improving the product’s yield.

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Influence function method is a common method to calculate the roll system deformation. It solves after discretizing the rollers load and elastic deformation, but the traditional influence function method doesn't consider whether the width of the rolled piece is equal to the integral multiple of the divided units length, therefore it only can solve approximately, which affects the calculation accuracy. According to the accuracy loss problem of the traditional algorithm, the paper puts forward a solution with advanced structure to deal with the left rolled pieces after division. The solution self-adapts the width changes of the rolled pieces, which not only avoids the problems of increasing the segmentation unit quantity to reduce the influence by edges and reducing the calculation speed of the traditional influence function method, but also improves the calculation accuracy of the model, and raising the level of material properties control.