In this research, we developed a novel successive multistep growth method to synthesize very long silver nanowires (AgNWs) over several hundred micrometers (maximum length of 400−500 μm) and performed a systematic parameter study to optimize the dimension of nanowires synthesized at a large scale. It was demonstrated that AgNWs continued to grow through successive multistep growth as long as Ag ion rich conditions were maintained continuously. We successfully attained an extremely high aspect ratio of 1000−3000 with length of over 300 μm and diameter of less than 150 nm. This value demonstrated an order of magnitude length enhancement from previous AgNW synthesis research. Furthermore, we demonstrated that the very long AgNW mesh can be used for a transparent conductor as an alternative to metal oxide conductors. The production of very long metal nanowires at a large scale has significant impact on their potential application in flexible transparent conductors.

We present a novel and simple method for the patterned growth of ZnO nanowires (NWs) that combines (1) the direct patterning of ZnO nanoparticle (NP) seeds via microcontact printing and (2) subsequent low-temperature hydrothermal growth. The ZnO NPs can be patterned as seed layers for ZnO NW growth on various substrates including flexible polymer films. The NW geometry and configuration can be controlled by varying the printing conditions (time and pressure) and the hydrothermal reaction time. The “needleleaf-like” sharp-tipped ZnO NWs with a radially grown structure were examined at the pattern edges. To assess the possibility of high-performance electronic applications of the patterned ZnO NWs, their field emission characteristics were examined by fabricating a high-performance field emission device with a patterned ZnO NW array. The remarkable enhancement of the field emission properties is attributed to the minimized field emission screening that results from the radial ZnO NW structures and micropatterning. This versatile ZnO NW patterning process is a powerful means for the large-scale and continuous production of functional ZnO NW devices.

As the number of old bridges increases, the number of bridges with structural defects is also increasing. Timely inspection and maintenance of bridges are required because structural degradation is accelerated after bridge damage. Recently, in the field of structural health monitoring, a bridge inspection using an unmanned aerial vehicle system (UAS) is receiving a lot of attention. In this paper, UAS-based automatic damage detection and bridge condition evaluation were performed on existing bridges. From the process of preparing for inspection to the management of inspection data, the entire bridge inspection process was performed through field tests. The necessary element techniques for each stage were explained and the results were confirmed. Finally, UAS-based results were compared with conventional human-based visual inspection results. As a result, it was confirmed that the UAS-based bridge inspection is faster and more objective than the existing technology. Therefore, it was confirmed that the automatic bridge inspection method based on unmanned aerial vehicles can be applied to the field as a promising technology.

Key information for the maintenance and diagnosis of structures including bridges can be obtained from the processing of digital images acquired by unmanned aerial vehicle (UAV). However, lowquality images caused by various problems such as UAV movement, inspection environment, and camera parameters can lead to inappropriate structural evaluation due to the difficulty of digital image processing. Therefore, an appropriate assessment method for image quality considering the deterioration of the inspection image in the structural inspection procedure is required. In this study, a new image quality assessment (IQA) using a convolutional neural network (CNN) is proposed in consideration of various degradation factors that may occur in the structure inspection image. The first stage presents a method to obtain consistent quality against various interference factors of deterioration that may occur in inspection images. Adjusting the camera parameters minimizes the degradation of the inspection image. Subsequently, low-and high-quality images are distinguished according to the proposed image acquisition method. The second stage is the classification of the inspection dataset using the CNN-based image quality classifier model through training of data classified according to their quality. Experimental validation of the proposed method shows that the results are similar to the Human Visual System (HVS), which means subjective quality classification, and that the inspection image can be classified with more accurate and shorter processing time.

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