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Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net++ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs.

An air-independent propulsion system containing fuel cells is applied to improve the operational performance of underwater vehicles in an underwater environment. Fuel-reforming efficiently stores and supplies hydrogen required to operate fuel cells. In this study, the applicability of a fuel-reforming system using various fuels for underwater vehicles was analyzed by calculating the fuel and water consumptions, the amount of CO2 generated as a byproduct, and the amount of water required to dissolve the CO2 using aspen HYSYS (Aspen Technology, Inc., Bedford, MA, USA). In addition, the performance of the fuel-reforming system for methanol, which occupies the smallest volume in the system, was researched by analyzing performance indicators such as methanol conversion rate, hydrogen, yield and selectivity, and reforming efficiency under conditions at which pressure, temperature, steam-to-carbon ratio (SCR), and hydrogen separation efficiency vary. The highest reforming efficiency was 77.7–77.8% at 260 °C and 270 °C. At SCR 1.5, the reforming efficiency was the highest, which is 77.8%, and the CO2 generation amount was the lowest at 1.46 kmol/h. At high separation efficiency, the reforming efficiency increased due to the reduction of reactants, and a rate at which energy is consumed for endothermic reactions also decreased, resulting in a lower CO2 generation amount.

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