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171.
172.
杨爱荣 《中国民航学院学报》1997,(5)
在外语教学中,语言测试是不可缺少的环节之一。本文主要论述语言测试理论,如语言测试的目的、语言测试的类型、语言测试的标准;分析并总结了常见的语言测试项目的设计方法,如:多项选择题的设计、词汇测试的设计、完形测试的设计、阅读理解的设计、听力测试的设计、写作测试的设计;并对测试后得分的计算和分析进行解释。最后提出语言教师了解一点语言测试的知识是很有必要的。 相似文献
173.
一种先进复合材料受力件的制品,如何检测内部是否有分层脱胶,曾遇到很大的困难。本文阐述如何结合实际解决这一难题,从而免受重大损失。 相似文献
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本文介绍了一种新型窄频带超声检漏系统,它能明显地区分环境干扰噪声与泄漏信号,其性能明显优于宽频带系统,在泄漏检测中更具实用价值。 相似文献
176.
论述了把火箭发动机作为激励源对铁路桥梁桥墩施加动载荷以检测和评估桥梁桥墩质量的研究情况,给出了有关的实验和测量结果。 相似文献
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《中国航空学报》2022,35(8):249-264
Currently, industrial robots are considered as an alternative towards traditional machine tools. Especially for the large-scale parts milling, robotic flexibility and low cost make it possess the irreplaceability. However, the milling chatter caused by its weak rigidity hampers robotic application and promotion severely in aviation industry. Rotary ultrasonic milling (RUM) technology with one-dimensional axial vibration has been proven and approved on avoiding robotic chatter. Based on this, the research of project team demonstrates that longitudinal-torsional composite ultrasonic milling (CUM-LT) involving separation characteristic has a greater advantage than RUM in terms of chatter suppression. Thereby, the CUM-LT as a new means is applied to avoid processing vibration of robotic milling system. And its influence mechanism on stability improvement of weak stiffness processing system is clarified. Meanwhile, the approaches to strengthen separation effect are provided innovatively. Moreover, a new analysis method of robotic CUM-LT (RCUM-LT) stability is proposed on the basis of ultrasonic function angles. The simulation and experimental results indicate that compared with robotic RUM (RRUM), stability regions of separated RCUM-LT (SRCUM-LT) and unseparated RCUM-LT (URCUM-LT) are improved by 124.42% and 39.20%, respectively. The addition of torsional ultrasonic energy has a wonderful effect on the milling chatter suppression of low stiffness robots. 相似文献
180.
《中国航空学报》2022,35(10):254-264
Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection. This paper proposes a vision-based method of pixel-level defect detection, which is based on the Mask Scoring R-CNN. First, an attention mechanism and a feature fusion module are introduced, to improve feature representation. Second, a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed, to reduce the influence of information around the area of the defect. Third, to evaluate the proposed method, a dataset of aircraft skin defects was constructed, containing 276 images with a resolution of 960 × 720 pixels. Experimental results show that the proposed classifier head improves the detection and segmentation accuracy, for aircraft skin defect inspection, more effectively than the attention mechanism and feature fusion module. Compared with the Mask R-CNN and Mask Scoring R-CNN, the proposed method increased the segmentation precision by approximately 21% and 19.59%, respectively. These results demonstrate that the proposed method performs favorably against the other two methods of pixel-level aircraft skin defect detection. 相似文献