The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

No abstract

Four new medermycin-type naphthoquinones, strepoxepinmycins A-D (1-4), and one known compound, medermycin (5), were identified from Streptomyces sp. XMA39. Their structures were elucidated by analysis of HRESIMS, 1D and 2D NMR spectroscopic data, and ECD calculations. Among these compounds, strepoxepinmycin A (1) represents a rare 5,10-oxepindione ring system typically formed by a Baeyer-Villiger oxidation, and strepoxepinmycin B (2) is an isolation artifact derived from 1. Bioactivity evaluations of these compounds showed that compounds 3 and 4 exhibited cytotoxicity against HCT-116 and PC-3 cancer cell lines and 4 exhibited moderate inhibition of ROCK 2 protein kinase. In addition, all of the new compounds showed antibacterial activity against Escherichia coli and methicillin-resistant Staphylococcus aureus and antifungal activity against Candida albicans.