최근 급격한 기후 변화로 인해 도로 교통사고의 발생 빈도가 증가하고 있으며, 특히 겨울철에 자주 발생하는 도로 살얼음(블랙아이 스) 현상이 주요 원인 중 하나로 지목되고 있다. 도로살얼음의 형성 메커니즘은 다양한 요인에 따라 복합적으로 작용하며, 당시의 도 로 기상 조건과 도로의 기하학적 구조에 따라 얼음의 형태 및 강도가 결정된다. 그중에서도 도로 노면 온도는 도로살얼음 형성에 중 요한 요소로, 여러 나라에서 겨울철 교통안전 평가를 위한 주요 지표로 사용되고 있다. 그러나 현재 도로 노면 온도에 대한 명확한 정 의가 부족할 뿐만 아니라, 측정 방법에 따라 계측 편차와 온도 손실 등 여러 한계가 존재해 정확한 온도 측정이 어려운 실정이다. 이 에 본 연구는 지중 깊이에 따른 온도 데이터와 도로 기상 데이터를 결합하여 보다 정밀한 도로 노면 온도 예측 방법을 제시하는 것을 목적으로 한다. 연구를 위해 지중 깊이 2cm, 3cm, 4cm, 5cm, 7cm, 9cm, 15cm, 20cm에 각각 온도 센서를 설치하였으며, 기상 데이터는 해당 지점에서 2m 떨어진 AWS(Automatic Weather System)를 통해 대기 온도, 습도, 강수량, 일사량 등의 정보를 수집하였다. 이를 바 탕으로 지중 온도와 기상 조건의 상관관계를 활용하여 노면 온도를 예측하는 방법론을 도출하였다. 본 연구의 결과는 도로 노면 온도 예측의 정확성을 향상시킬 뿐만 아니라, 새로운 접근 방식을 통해 노면 온도의 정의를 재정립하는 데 기여할 것으로 기대된다.

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.

To automate cooking processes, quantitative descriptions are needed on how quality parameters, such as texture change during heating. Understanding mechanical property changes in foods during thermal treatment due to changes in chemical composition or physical structure is important in the context of engineering models and in precise control of quality in general. Texture degradation of food materials has been studied widely and softening kinetic parameters have been reported in many studies. For a better understanding of kinetic parameters, applied kinetic models were investigated, then rate constants at 100°C and activation energy from previous kinetic studies were compared. The food materials are hardly classified into similar softening kinetics. The range of parameters is wide regardless of food types due to the complexity of food material, different testing methods, sample size, and geometry. Kinetic parameters are essential for optimal process design. For broad and reliable applications, kinetic parameters should be generated by a more consistent manner so that those of foods could be compared or grouped.

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..

Corrosion of rebar causes a severe problem that causes extensive damage to various types of structures. While considerable research has been carried out on measuring the amount of corrosion and methods to slow the progress of corrosion, a qualitative measurement technique that can measure the amount of corrosion of reinforcing steel need to be developed. The purpose of this research is to develop a new tech-nique to measure the corrosion level of rebar using accumulated thermal data. Accumulated thermal data were gathered using infrared cam-era and digitized to distinguish the difference between various corrosion levels of rebar. The test results shows that the higher level of corro-sion displays the higher level of temperature.

인간 감성변화의 기본인 피부온도 및 피복내 온습도 측정을 위한 시스템과 감성공학적 해석을 위한 보다 정밀하고 안정성이 있는 센서의 응답성 및 회로의 선형화에 대한 연구를 수행하고, 의복내 환경평가 및 실내 온습도 측정등의 다양한 감성공학적 해석을 위한 소프트웨어의 개발이 본 연구의 목적이다. 본 연구에는 손쉬운 온습도 변환장치와 풍부한 저장능력 등 다양한 분야에서 활용이 가능한 온습도 측정기와 이에 필요한 센서를 개발하고 측정기의 선형화특성을 평가하였다.

The present study explores time and spatial thermal environment for Daegu, which is a city built on a basin area, according to varying land cover conditions of the earth's surface by analyzing data derived from meteorological observation and satellite images. The study has classified land use by utilizing MODIS satellite images and analyzed land surface temperature. Also, by using data acquired from automatic weather system, the study has evaluated the effects of atmospheric heating caused by city pavements by analyzing the sensible heat flux between the city's land surface and the atmosphere.The results are as follows. 1) Classification of land use in the Daegu area shows 46.64% of urban and built-up area, 1.39% of watersides, 35.19% of forest, 11.43% of crops, and 5.37% grasslands. 2) During the weekdays throughout the year, the land surface temperature was high for Dalseogu, Bukgu, and Seogu regions where industrial complexes could be found. Comparatively, lower temperature could be observed in the woodlands. 3) While the land surface temperature displayed the effects of pushing air upwards during the weekdays in urban areas, the reverse was true for forest regions. During the night, the temperature did not exert any significant influence on air movement.

In recent years, the urban thermal environment has become worse, such as days on which the temperature goes above 30℃, sultry nights and heat stroke increase, due to the changes in terrestrial cover such as concrete and asphalt and increased anthropogenic heat emission accompanied by artificial structure. The land use type is an important determinant to near-surface air temperature. Due to these reasons we need to understand and improve the urban thermal environment. In this study, the fifth-generation Pennsylvania State University- National Center for Atmospheric Research Mesoscale Model(MM5) was applied to the metropolitan of Daegu area in order to investigate the influence of land cover changes and urban modifications increase of Albedo to the surface energy budget on the simulated near-surface air temperature and wind speed. The single urban category in existing 24-category U.S. Geological survey land cover classification used in MM5 was divided into 6 classes to account for heterogeneity of urban land cover. As a result of the numerical simulation intended for the metropolitan of Daegu assumed the increase of Albedo of roofs, buildings, or roads, the increase of Albedo (Cool scenario)can make decrease radiation effect of surface, so that it caused drops in ambient air temperature from 0.2 to 0.3 on the average during the daylight hours and smaller (or near-zero) decrease during the night. The Sensible heat flux and Wind velocity is decreased. Modeling studies suggest that increased surface albedo in urban area can reduce surface and air temperatures near the ground and affect related meteorological parameters such as winds, surface air temperature and sensible heat flux.