Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about $2.71$ s. The average segmentation errors along the x-direction and y-direction are only $1.6$ pixels and $1.4$ pixels, respectively. The defect detection approach on segmented cells achieves $99.8\%$ accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

There were few papers for interdisciplinary integration of Scratch programming, English learning, and local cultures as far as the digital animated E-book creation of elementary schools was concerned. This article proposes an interdisciplinary innovative teaching mode, with college students as teachers, Scratch programming and English learning as tools, and a local legend The Wind Chasers of Siraya as material for schoolchildren in a remote area of Taiwan. Schoolchildren were divided into four groups, and each group was responsible for a part, and finally these four parts were integrated into a complete animated English picture book with images, sound, music, and games. The purpose is to enhance the acquisition and learning of information technology and English application among schoolchildren in the remote area. Participants were evaluated in accordance with their learning motivation, self-efficacy, and learning effect. The self-efficacy scale was designed from three dimensions of the self-efficacy theory presented by Bandura and the syllabus of five functions in Scratch. The teaching effects were investigated with paired sample $t$ test. The results of these assessments revealed that schoolchildren’s learning motivation has been elevated, with a significant improvement of self-efficacy and overall learning achievement. In addition, the post-test revealed that Scratch programming and English learning from the culture-based illustration book had significant correlation, which suggested that the overall program successfully integrated these two subjects.

In refractive eye surgery using laser, automatically localizing a target eye for surgery and precisely focusing the laser to the corneal surface are crucial to the medical treatment. We therefore propose a new eye detection method based on template matching for automatic localizations of target eyes for refractive eye surgery with excimer laser. In the proposed approach, representative templates of image gradient histograms of eyes are constructed using both open and privately-collected eye datasets. Real-time eye detection that helps guiding the excimer laser to a target eye for surgery can be achieved by the efficient template matching. After the eye localization, image-guided, automatic alignment of positioning marker, i.e., a read line of visible light, is then employed to measure the distance between the corneal surface and the excimer laser. Experimental results show that the proposed eye detection method using template matching outperforms an HoG-based eye detector in miss detection rates based on the same training data for this specific medical application. Also, with the automatic image-guided positioning of laser head, the time consuming process of manual laser focusing can be largely simplified.

We propose a new object packing approach, RePack, to arrange a series of identical image objects to a rectangular canvas densely by a deep CNN with reinforcement learning. In our approach, adding a new object to an image pack of existing objects is modeled as classification of possible pack configurations by a CNN. To iteratively reinforce the CNN, pack trees are built to identify object overlaps and to find denser pack configurations for reinforcement training. Such a reinforcement learning process for enhancing a CNN for dense object packing is rarely seen in previous literature. Preliminary experimental results show that the reinforced deep CNN can generate dense object packs in a sequential manner for circular, triangular and quadrilateral objects.