• 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.

• Progress in Superconductivity and Cryogenics
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• v.18 no.2
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• pp.1-4
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• 2016

To observe the superconducting current and structural properties of high critical temperature ($T_c$) superconductors (HTS), we suggest the following imaging methods: Room temperature imaging (RTI) through thermal heating, low-temperature bolometric microscopy (LTBM) and Raman scattering imaging. RTI and LTBM images visualize thermal-electric voltages as different thermal gradients at room temperature (RT) and superconducting current dissipation at near-$T_c$, respectively. Using RTI, we can obtain structural information about the surface uniformity and positions of impurities. LTBM images show the flux flow in two dimensions as a function of the local critical currents. Raman imaging is transformed from Raman survey spectra in particular areas, and the Raman vibration modes can be combined. Raman imaging can quantify the vibration modes of the areas. Therefore, we demonstrate the spatial transport properties of superconducting materials by combining the results. In addition, this enables visualization of the effect of current flow on the distribution of impurities in a uniform superconducting crystalline material. These imaging methods facilitate direct examination of the local properties of superconducting materials and wires.

• 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 the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.65.1-65.1
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• 2013

The 2011 August 09 Flare is one of the largest X-ray flares of Sunspot Cycle 24 to attract a lot of attention for its various activities detected in coronal images. In this study we concern ourselves mostly on information of high energy electrons produced during this flare provided by hard X ray data from the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) and radio data from the Korean Solar Radio Burst Locator (KSRBL) and Ondrejov. EUV images obtained by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamic Observatory are used to provide the context of magnetic reconnection. In our results, (1) HXR spectra have a rich spectral morphology. Initially it could be fit by one thermal component (T~30MK) and one single power law nonthermal spectrum, but later a better fit could be made by introducing an additional thermal component (T~55 MK). (2) Time delays between the KSRBL burst and the RHESSI hard X-ray emission were found which are more obvious at low frequencies and insignificant at high frequencies. (3) The HXR source lies in the core of the quadrupolar active region. In our interpretation based on AIA 94 A images, the outer part of the active region erupted to be blown out, leaving the intense hard X-ray emission concentrated in the core. We relate the appearance of the second thermal component to the evolution of the AIA 171 and 94 A images. The time delays of microwave peaks to HXR peaks are interpreted as indicating presence of trapped electrons in larger closed magnetic loops. With these result we conclude that the hard X ray and microwaves are due to impulsive acceleration in the low and high heights and a sigmoidal reconnection scenario.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.657-664
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• 2013

By using shear mixing and ultrasonication, we fabricated specimens of well-dispersed multi-walled carbon nanotube composites. To confirm the proper dispersion of the filler, we used scanning electron microscopy images for quantitative evaluation and a tensile test for qualitative assessment. Furthermore, the coefficients of thermal expansion of several specimens having different filler contents were calculated from the measured thermal strains and temperatures of the specimens. Based on the microscopy images of the well-dispersed fillers and the small deviations in the measurements of the tensile strength and stiffness, we confirmed the proper dispersion of nanotubes in the epoxy. As the filler contents were increased, the values of tensile strength increased from 58.33 to 68.81 MPa, and those of stiffness increased from 2.93 to 3.27 GPa. At the same time, the coefficients of thermal expansion decreased. This implies better thermal stability of the specimen.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.106-117
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• 2016

Recently, solar power plants have spread widely as part of the transition to greater environmental protection and renewable energy. Therefore, regular solar plant inspection is necessary to efficiently manage solar-light modules. This study implemented a test that can detect solar-light module faults using an UAV based thermal infrared camera and GIS spatial analysis. First, images were taken using fixed UAV and an RGB camera, then orthomosaic images were created using Pix4D SW. We constructed solar-light module layers from the orthomosaic images and inputted the module layer code. Rubber covers were installed in the solar-light module to detect solar-light module faults. The mean temperature of each solar-light module can be calculated using the Zonalmean function based on temperature information from the UAV thermal camera and solar-light module layer. Finally, locations of solar-light modules of more than $37^{\circ}C$ and those with rubber covers can be extracted automatically using GIS spatial analysis and analyzed specifically using the solar-light module's identifying code.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.157-163
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• 2013

In this paper, we propose a human detection technique using thermal imaging camera. The proposed method is useful at night or rainy weather where a visible light imaging cameras is not able to detect human activities. Under the observation that a human is usually brighter than the background in the thermal images, we estimate the preliminary human regions using the statistical confidence measures in the gray-level, brightness histogram. Afterwards, we applied Gaussian filtering and blob labeling techniques to remove the unwanted noise, and gather the scattered of the pixel distributions and the center of gravities of the blobs. In the final step, we exploit the aspect ratio and the area on the unified object region as well as a number of the principal components extracted from the object region images to determine if the detected object is a human. The experimental results show that the proposed method is effective in environments where visible light cameras are not applicable.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.81-88
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• 2015

Various satellite sensors having ranges of the visible, infrared, and thermal wavelengths have been launched due to the improvement of hardware technologies of satellite sensors development. According to the development of satellite sensors with various wavelength ranges, the fusion and integration of multisensor images are proceeded. Image matching process is an essential step for the application of multisensor images. Some algorithms, such as SIFT and SURF, have been proposed to co-register satellite images. However, when the existing algorithms are applied to extract matching points between optical and thermal images, high accuracy of co-registration might not be guaranteed because these images have difference spectral and spatial characteristics. In this paper, location of control points in a reference image is extracted by SURF, and then, location of their corresponding pairs is estimated from the correlation of the local similarity. In the case of local similarity, phase correlation method, which is based on fourier transformation, is applied. In the experiments by simulated, Landsat-8, and ASTER datasets, the proposed algorithm could extract reliable matching points compared to the existing SURF-based method.