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.

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.

The most common symptoms of COVID-19 are high fever, cough, headache, and fever. These symptoms may vary from person to person, but checking for “fever” is the government’s most basic measure. To confirm this, many facilities use thermographic cameras. Since the previously developed thermographic camera measures body temperature one by one, it takes a lot of time to measure body temperature in places where many people enter and exit, such as multi-use facilities. In order to prevent malfunctions and errors and to prevent sensitive personal information collection, this research team attempted to develop a facial recognition thermographic camera. The purpose of this study is to compensate for the shortcomings of existing thermographic cameras with disaster safety IoT integrated solution products and to provide quarantine systems using advanced facial recognition technologies. In addition, the captured image information should be protected as personal sensitive information, and a recent leak to China occurred. In order to prevent another case of personal information leakage, it is urgent to develop a thermographic camera that reflects this part. The thermal imaging camera system based on facial recognition technology developed in this study received two patents and one application as of January 2022. In the COVID-19 infectious disease disaster, ‘quarantine’ is an essential element that must be done at the preventive stage. Therefore, we hope that this development will be useful in the quarantine management field.

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.

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.

목적: 백내장 환자를 대상으로 수술 전과 후의 안구표면의 온도변화 양상을 열화상카메라의 서모그래피를 이용하여 연구 하고자 하였다. 방법: 본 연구는 백내장 수술을 받은 환자 50-79세까지 75명 75안의 환자 군을 대상으로 하였다. 과거에 굴절교정수술, 각막관련 수술을 받은 자와 콘택트렌즈를 사용하는 자, 눈물관 이상자, 전신질환 치료 약물을 복용하는 자 등 눈물분비와 눈물막에 영향을 줄 수 있는 자는 연구 대상자에서 제외하였고 눈물막파괴시간 검사(Tear Break Up Time, BUT), 쉬르머 검사(Schirmer’s Test), 맥모니테스트(Mcmonnies questionnaire)를 시행한 후 열화상카메라(Cox CX series, Answer., Korea)를 이용하여 안구표면의 온도변화를 실시간으로 측정하였다. 결과: 전체 대상자의 술 전 안구표면 온도는 35.20±0.54 ℃이었고 술 후에는 35.30±0.53 ℃로 표면온도가 상승하였으나 유의한 차이를 보이지 않았다. 안구표면 온도변화는 술전에서 -0.12±0.08 △(℃/sec)에서 술 후 -0.18±0.07 △(℃/sec)로 통계학적으로 유의한 결과를 나타냈다. 연령 별 비교에서는, 50 대군은 백내장 술 전 대상자의 안구표면 온도변화가 -0.14±0.09 △(℃/sec)에서 -0.19±0.05 △(℃/sec)으로 나타났고 60 대군에서는 -0.12±0.08 △(℃/sec)에서 -0.15±0.07 △(℃/sec)으로 나타났으며 70 대군에서는 술 전 대상자의 안구표면 온도변화는 -0.12±0.08 △(℃/sec)에서 -0.18±0.07 △(℃/sec)으로 전 연령에서 모두 유의한 안구표면 온도변화를 보였다. 결론: 백내장 술 후에는 안구건조증 평가지표가 모두 감소하였고 안구표면 온도변화가 유의함을 보였다. 안구표면의 서모그래피 기술은 비침습적으로 안구건조증을 평가하는데 용이하였고 객관적으로 수치화할 수 있는 장점이 있어 다양한 안구건조증 연구에 활용 될 것으로 기대된다.

Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. In most of the safety assessment and fracture mechanic applications proposed that these cracks and defects eventually will grow and will have potential lead to in-service failure. Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. Currently, non-destructive methods are getting popular in the field of inspecting defects in structure and one of them in trends is that using the thermographic image to detect hidden effects. However, the accuracy of the thermal camera, also called resolution, is highly dependent on camera variables such as lens, detector, sensitivity etc. Also, the most important question that needs to be answered for this research is what happens to the image in fog, rain or other climatic conditions where the camera detects crack which exceptionally smaller than most thermographic applications detects. This paper investigates the accuracy of thermal images obtained by the thermal camera under various weather condition and aims at providing information about optimum choice of environmental condition where the more favorable thermal images can be obtained and increase survey reliability and accuracy of the analysis.

Thermographic imaging based non-destructive evaluation proceeds measuring the defect or internal condition of civil structures. To demonstrate the feasibility of the method, the infrared camera system is applied to various specimens. The technique described in this paper may allow us to find their numbers, locations, and the extent of damaged steels from the measurement in the long distance. In addition, a reinforced concrete specimens with different penetration depths is tested using the active thermography method, and to study the influence on the detecting internal rebar in cooled and heated conditions. The experimental results demonstrate that thermographic imaging method is useful for detecting the damage of steel structures and the unknown rebar in the concrete.

Since the expectations and demand for higher ride comfort of the customers (driver and passengers) have been dramatically increased, new vehicle model launched on the market have not only better performance and design-wise appealing, but also ride comfort has to be increasingly better than its predecessor. Automotive manufactures have focused on the increasing human thermal comfort. To achieve a high thermal comfort, most manufacturers provide a system for their cars to ensure ventilation, heating and cooling air in the passenger compartment. As results, the influence of the seats and situations in the thermal human comfort are considered. And, the temperature distribution pattern on the human face is acquired at natural condition, both warm condition on which seat is managed around 30℃ and hot condition on which seat is managed around 50℃.

The purpose of this paper is to find an limitation to detect the defect of damaged asphalt pavement structures for infrared thermography. We use heat source of a natural light to detect the defect efficiently. The heat source was applied to the asphalt specimens. Four asphalt specimens were used: one was the asphalt containing depth of 1cm internal timber, two was the asphalt containing depth of 2cm internal void, Three was the asphalt containing depth of 3cm internal timber and four was not the asphalt containing internal timber. It was found that the depth of 3cm internal timber could be detected by this method. In addition, we used the image processing to make the damage zone displayed clear in the image obtained from the thermographic operation.