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.

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.

IR camera has been used widely for the temperature measurement and fault detection of the moving bodies and rotating bodies. The high-speed performance of the IR camera and a reliable thermal analysis method are required for the condition monitoring of the railway vehicle running at high speed. The effective fault detection method using a thermal image analysis could make a real time monitoring of the high speed train possible. Therefore the investigation of the performance of the thermal image analysis method was performed to find the effective thermal image data analysis method. The results suggested that the comparison of the characteristics of the temperatures obtained at different conditions and a continuous temperature subtraction method could be used as a useful analysis method for detecting abnormal temperature condition and histogram equalization could also help to enhance the fault detectability by increasing the contrast of the thermal image

기후변화에 따라 도시열섬현상이 지속적으로 발생하고 있다. 이러한 기후변화의 영향과 더불어 도시화의 진행과정에서 도시열섬현상의 강도는 더욱 심각하게 나타나고 있다. 이러한 관점에서 본 연구는 도시열섬현상이 도시 공간구조에 따라 어떠한 강도를 띠고 나타나는지를 확인하기 위해 기초연구를 진행하는데 목적을 두고 있다. 따라서 도시열섬현상의 특성을 파악하기 위하여 다음의 두 가지의 분석에 초점을 맞춘다. 첫째, 이동식차량디지털온도계(Portable Vehicle Digital Thermometer: PVDT)를 기반으로 가능한 동시간 대에 도시기온을 측정하면서 이동경로를 중심으로 등온선의 형성구조를 파악한다. 둘째, 이러한 열섬현상의 분석을 바탕으로 등온선의 변화가 나타나는 주요 원인을 파악하고자 디지털열화상캠코더(Digital Infrared Camcoder: DIC)를 활용하여 도시기온을 상승시키는 시설물을 선별하여 표적탐구를 시행하였다. 실험의 결과 도시열섬에 영향을 끼치는 핵심적 요인은 도로표면, 간판 등 표지판, 건축물의 지붕과 외벽 및 차량 바퀴와의 접촉면인 것으로 파악된다.

In this study, an analysis were conducted to utilize the thermal infrared image using drone to present the temperature correction method of thermal infrared image and the thermal environment by the type of land cladding. The analysis was applied to the temperature correction of the thermal infrared image and total eight thermal infrared images were produced based on the land surface temperature. The thermal infrared image compared accuracy through RMSE calculation. Based on the result of RMSE, the thermal infrared image corrected by the land surface temperature was relatively accurate and contained at 2.26 to 3.58. According to the results, it is expected that the aggregation and waters will perform the functions of the green park sufficiently to improve the thermal comfort and improve the microclimate stability using the thermal infrared image and the reclassified land cover map. The results of this study obtained by Drone and the usability of the drone thermal infrared image in the detection of the thermal environment. Finally, it is expected to contribute to the improvement and management of the thermal environment in the city by being used as a basic data for the improvement and management policy of the thermal environment. Moreover, the macro view is expected to contribute to the mitigation of urban temperature reduction and heat island.

It is assumed that air temperature and light intensity may influence thermal image of plants but little effort has been made to these environmental factors. We conducted this study to investigate the effects of these environmental factors on the thermal image of rice and thus to optimize the condition for thermal image acquisition for high-throughput screening of salt-tolerant rice. Rice (Oryza sativa cv. Chucheong-byeo) seedlings at the four-leaf stage were treated with 0, 50, and 100 mM of NaCl for salt stress. Thermal images (T420, FLIR, Sweden) were taken at 1 and 2 days after salt treatment under 4 different air temperatures and 3 light intensities. Thermal images were analyzed using FLIR Tools 3.1 (FLIR systems Inc., USA) and MATLAB 8.1 (The MathWorks Inc., USA). Rice leaf temperature increased significantly with increasing air temperature and light intensity, resulting in greater discrimination between salt-stressed and unstressed rice plants. Our results thus conclude that environmental conditions such as air temperature and light intensity affect rice thermal image and their optimization is essential for better image acquisition and high-throughput screening system based on thermal image analysis