With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

In this paper, optical infrared thermography simulation using thermal wave imaging technique is performed to analyze the thermal characteristics of delamination defects. In this study, lock-in thermography(LIT) and pulsed thermography(PT) simulation was performed to analyze the samples of european traditional tiles with delamination defects, and the analytical modeler was developed through the ANSYS 19.2 transient thermal analysis tool. Applied sinusoidal heating with modulation frequency according to pulse heating and phase locking technique. The thermal response of the sample surface by heating was recorded and then data analysis was performed. The temperature gradient characteristics of each technique were compared, and phase angle was calculated for the LIT to analyze the parameters for the experiment setting. The simulation model was developed as a useful data for practical optical infrared thermography tests.

Numerous experiments have demonstrated that infrared thermographic methods are effective for detection of subsurface defects in the materials. The response of the material to the thermal stimulus is dependent on the existence of subsurface defects and their features. In order to obtain the information about defects, the material’s response to the thermal stimulus is studied. In this study, image processing was applied to infrared thermography images to detect defects in metals that were widely used in industrial fields. When analyzing experimental data from infrared thermographic testing, thermal images were often not appropriate. Thus, four point method was used for processing of every pixel of thermal images using MATLAB program for quantitative evaluation of defect detection and characterization which increased the infrared non-destructive testing capabilities since subtle defects signature became apparent..

We find evidence of a hard X-ray excess above the thermal emission in two cool clusters (Abell 1750 and IC 1262) and a soft excess in two hot clusters (Abell 754 and Abell 2163). Our modeling shows that the excess components in Abell 1750, IC 1262, and Abell 2163 are best fit by a steep power law indicative of a significant non-thermal component. In the case of Abell 754, the excess emission is thermal, 1 ke V emission. We analyze the dynamical state of each cluster and find evidence of an ongoing or recent merger in all four clusters. In the case of Abell 2163, the detected, steep spectrum, non-thermal X-ray emission is shown to be associated with the weak merger shock seen in the temperature map. However, this shock is not able to produce the flatter spectrum radio halo which we attribute to post-shock turbulence. In Abell 1750 and IC 1262, the shocked gas appears to be spatially correlated with non-thermal emission suggesting cosmic-ray acceleration at the shock front.

Recently, Maintenance and inspection of plant are now being actively studied with the development of plant industry. In this paper, A leak detection in piping facilities using thermal imaging camera is proposed. This method was verified by laboratory experiment. In future, Appropriate algorithm will be applied to this method for real time detection and finally applied to the plant that is the ultimate goal of this study.