Since cable members are the major structural components in cable bridges, they should be properly inspected for surface damage as well as inside defects such as corrosion and/or breakage of wires. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs (MLTM) was initiated to develop the cable inspection robot. In this study, only the vision-based surface damage detection system based on image processing techniques is addressed. The damage detection algorithm combines some image enhancement techniques with principal component analysis (PCA) to detect damages on cable surface. The images from three cameras attached to the cable climbing robot are wirelessly transmitted to the server computer at the cable support. They are processed with image enhancement method together with noise removal technique to improve overall image quality. Then they are projected into PCA sub-space. Finally, the Mahalanobis square distances of the projected images to all sample patterns are calculated. The smallest distance is found to be the match for the input image. The proposed damage detection algorithm was verified through laboratory tests on three types of cables.

For sensitive and accurate gene expression analysis, normalization of gene expression data against housekeeping genes is required. There are conventional housekeeping gene (e.g. ACT) that primarily function as an internal control of transcription. In this study, we performed an in silico analysis of 278 rice gene expression samples (GSM) in order to identify the gene that is most consistently expressed. Based on this analysis, we identified novel candidate housekeeping genes that displayed improved stability among the cross experimental conditions. Furthermore four of the most conventional housekeeping genes were included in our 30 other housekeeping genes among the most stable genes. Therefore, these 30 genes can he used to normalize transcription results in gene expression studies on rice at a broad range of experimental conditions.