This study was carried out to establish various physiological changes according to soil water stress and to compare the degree of water stress between two species of grapevines (‘Jinok’ as a new breeding cultivar and ‘Campbell Early’ as a control) using thermography. Soil water potentials were treated at -70, -30, and -5 kPa with waterlogging for 7 days. Regarding the photosynthetic rates (A) of the two cultivars, they showed an order of –30 kPA > -5 kPa > -70 kPa in order. With -70 kPa and waterlogging treatments, a decrease of photosynthetic rate was observed at 3 days after treatment, with a more significant decrease accumulating over time. At 7 days after treatment, photosynthetic rates of ‘Campbell Early’ (33.3, 45.6%) and ‘Jinok’ (56.6, 57.3%) grapes decreased compared to those with -30 kPa treatment. H2O2 and proline synthesis were the highest with the waterlogging treatment. In terms of proline synthesis, ‘Campbell Early’ had a relatively higher rate than ‘Jinok’. Leaf and stem water potential were the lowest with the -70 kPa treatment and the highest with the - 30 kPa treatment f or both cultivars. Crop water stress index (CWSI) showed the following order: waterlogging > -70 kPa > -5 kPa > -30 kPa, which was the opposite result of water vapor transfer (IG). As a result of correlation analysis between factors, photosynthetic rate showed negative correlations with the water potential of leaf and stem and crop water stress index but a positive correlation with the relative water content of leaves. Thus, tolerance to water stress of ‘Campbell Early’ was relatively stronger than that of ‘Jinok’ grape. It is possible to compare water stress using infrared imaging.

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

This study is corroborated as a fundamental resource to develop lightweight foamed concrete. Weight of unit volume decreased until 0.8% dosage of foaming agent is put cement paste. After that, Mechanical properties of cement paste with foaming agent is verified to use FEM analysis based on picture image. Finally, It is compared with compressive strength of experiment and estimation from picture image.