Highlights: â€¢ The wear of bare silicon against SiO interface and induce a more serious tribochemical wear on the bare silicon surface. Role of water in the tribochemical removal of bare siliconĮnergy Technology Data Exchange (ETDEWEB)Ĭhen, Cheng Xiao, Chen [Tribology Research Institute, National Traction Power Laboratory, Southwest Jiaotong University, Chengdu 610031 (China) Wang, Xiaodong [Center of Micro/Nano Science and Technology, Jiangsu University, Zhenjiang 212013 (China) Zhang, Peng Chen, Lei Qi, Yaqiong [Tribology Research Institute, National Traction Power Laboratory, Southwest Jiaotong University, Chengdu 610031 (China) Qian, Linmao, E-mail: [Tribology Research Institute, National Traction Power Laboratory, Southwest Jiaotong University, Chengdu 610031 (China) In this proof-of-principle study, the preliminary experimental results suggest that the fiber-tip-based near-field FCS might be a good alternative to realize localized analysis at the single-molecule level. Related factors influencing the near-field FCS performance are investigated and discussed in detail. The detection volume under control of the SNOM system has been reduced over one order of magnitude compared to that of the conventional confocal FCS. Single-molecule FCS analysis at 100 nM Rhodamine 6G has been achieved by using bare chemically etched, tapered fiber tips. This near-field FCS is based on near-field illumination by coupling a scanning near-field optical microscope (SNOM) to a conventional confocal FCS.

Lu, Guowei Lei, Franck H Angiboust, Jean-FranÃ§ois Manfait, MichelĪ fiber-tip-based near-field fluorescence correlation spectroscopy (FCS) has been developed for confining the detection volume to sub-diffraction-limited dimensions.

We develop a methodology for automatic in-line flaw detection in industrial woven fabrics.Confined detection volume of fluorescence correlation spectroscopy by bare fiber probes. Vision-based in-line fabric defect detection using yarn-specific shape features Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision.

Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. First, the proposal regions are generated by RPN (regional proposal Network). In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. Liu, Zhoufeng Liu, Xianghui Li, Chunlei Li, Bicao Wang, Baorui Fabric defect detection based on faster R-CNN

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