• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.128-132
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• 2007

Thermal Imaging systems are reported to be crucial for fire fighting and beginning to be used by fire fighters. The performance of thermal imaging system is determined by both the radiation of infrared from the target and the attenuation of infrared signal in the optical path by the absorption, scattering, diffraction and reflection. In the scene of fire, water drops with various sizes such as vaporized water, wafer mist from sprinkler, and wafer to suppress the fire reside with various gas generated by burning. To measure the transmission of infrared radiation in the scene of fire, fire simulating system and thermal imagers with cooled detector which detects $3{\sim}5{\mu}m$ infrared and uncooled detector fabricated by the MEMS technology which detects $8{\sim}12{\mu}m$ infrared. are made. With thermal imagers and Ire simulating system, the change of thermal image with respect to the change of visibility controlled with the burned fas was measured. It was found that the transmission of infrared was not reduced by the burned gas, which could be explained by the long wavelength of infrared ray compared with visible ray. However, the transmission of infrared ray was largely reduced by the combination of burned gas and water mist supplied by sprinkler. This is contrary to the results of calculated transmission through the pure water mist and shows that the transmission of infrared ray is mostly affected by the compounds of water mist and burned gas. In this case, it was found that the uncooled detector which detects $8{\sim}12{\mu}m$ infrared ray is better than cooled detector which detects $3{\sim}5{\mu}m$ infrared ray for fire fighting.

• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.280-293
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• 2018

The conversion of an invisible thermal radiation pattern of an object into a visible image using infrared (IR) thermal technology is very useful to understand phenomena what we are interested in. Although IR thermal images were originally developed for military and space applications, they are currently employed to determine thermal properties and heat features in various applications, such as the non-destructive evaluation of industrial equipment, power plants, electricity, military or drive-assisted night vision, and medical applications to monitor heat generation or loss. Recently, IR imaging-based monitoring systems have been considered for application in agricultural, including crop care, plant-disease detection, bruise detection of fruits, and the evaluation of fruit maturity. This paper reviews recent progress in the development of IR thermal imaging techniques and suggests possible applications of thermal imaging techniques in agriculture.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.90-98
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• 2022

The output of an infrared (IR) sensor mounted on an EO/IR payload is known to change during a mission period in an orbital environment. As it is required to calibrate the output of the IR sensor periodically to obtain high-quality images, an on-board black body system is mounted on the payload. All systems operating in the space environment require performance tests on ground to verify the target performance in the orbital environment. Therefore, it is also required to test the black body system to verify the performance of the surface temperature uniformity and the estimated representative temperature error within the target temperature range in the operating environment. In this study, calibration of the estimated representative temperature error and verification of the thermal performance of the black body system were conducted by performed a performance test in the thermal vacuum chamber applying deep space radiation cooling effect of an orbital environment.

• Journal of Sensor Science and Technology
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• v.14 no.5
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• pp.337-342
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• 2005

A silicon-based micro-reactor to amplify small amount of deoxyribonucleic acid (DNA) has been fabricated using micro-electro-mechanical systems (MEMS) technology. Polymerase chain reaction (PCR) of DNA requires a precise and rapid temperature control. A Pt sensor is integrated directly in the chamber for real-time temperature measurement and an infrared lamp is used as external heating source for non-contact and rapid heating. In addition to the real-time temperature sensing, PCR needs a rapid thermocycling for effective PCR. For a fast thermal response, the thermal mass of the reactor chamber is minimized by removal of bulk silicon volume around the reactor using double-side KOH etching. The transparent optical property of silicon in the infrared wavelength range provides an efficient absorption of thermal energy into the reacting sample without being absorbed by silicon reactor chamber. It is confirmed that the fabricated micro-reactor could be heated up in less than 30 sec to the denaturation temperature by the external infrared lamp and cooled down in 30 sec to the annealing temperature by passive cooling.

• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.294-299
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• 2018

This article describes the output properties of non-dispersive infrared carbon dioxide($CO_2$) sensors resulting from the changes in ambient temperatures. After the developed sensor module was installed inside the gas chamber, the temperature was set to 267 K, 277 K, 300 K, and 314 K, and the concentrations of $CO_2$ gas were increased from 0 to 5,000 ppm. Then, the output voltage at each concentration was obtained. Through these experimental results, two observations were made. First, both the $CO_2$ sensor and the reference sensor showed an increase in the output voltages as the temperature rose from 0 ppm, Second, the full scale outputs of the $CO_2$ sensor grew as the temperature increased. The output characteristics were analyzed based on two factors: change in the radiant energy of the infrared light source and change in the absorptivity of $CO_2$ gas according to the ambient temperature. Additionally, temperature compensation methods were discussed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.1
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• pp.32-39
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• 2013

Although space-borne satellites are useful in obtaining information all around the world, they cannot observe at a suitable time and place. In order to overcome these limitations, an airborne remote sensing system was developed in this study. It is composed of a SAR sensor and a thermal infrared sensor. Additionally GPS, IMU, and thermometer/hygrometer were attached to the plane for radiometric and geometric calibration. The brightness of SAR image varies depending on surface roughness, and capillary waves on the sea surface, which are easily generated by sea winds, induce the surface roughness. Thus, sea surface wind can be estimated using the relationship between quantified SAR backscattering coefficient and the sea surface wind. On the other hand, thermal infrared sensor is sensitive to measure object's temperature. Sea surface temperature is obtained from the thermal infrared sensor after correcting the atmospheric effects which are located between sea surface and the sensor. Using these two remote sensing sensors mounted on airplane, four test flights were carried out along the west coast of Korea. The obtained SAR and thermal infrared images have shown that these images were useful enough to monitor coastal environment and estimate marine meteorology data.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.237-247
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• 2018

In this paper, we describe a sensor module that monitors fire environment by analyzing fire characteristics. We analyzed the smoke characteristics of indoor fire. Six different environments were defined according to the type of smoke and the flame, and the sensors available for each environment were combined. Based on this analysis, the sensors were selected from the perspective of firefighter. The sensor module consists of an RGB camera, an infrared camera and a radar. It is designed with minimum weight to fit on the robot. the enclosure of sensor is designed to protect against the radiant heat of the fire scene. We propose a single camera mode, thermal stereo mode, data fusion mode, and radar mode that can be used depending on the fire scene. Thermal stereo was effectively refined using an image segmentation algorithm, SLIC (Simple Linear Iterative Clustering). In order to reproduce the fire scene, three fire test environments were built and each sensor was verified.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.550-558
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• 2013

The infrared and visible images are represented by different information due to the different wavelength of the light. The infrared image has thermal information and the visible image has texture information. Desirable results are obtained by fusing infrared and visible information. To enhance a visible image, we extract a weight map from a visible image using saturation, brightness. After that, the weight map is adjusted using thermal information in the infrared image. Finally, an enhanced image is resulted from combining an infrared image and a visible image. Our experiment results show that our proposed algorithm is working well to enhance the smoke in the original image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.