• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.493-505
• /
• 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 Academia-Industrial cooperation Society
• /
• v.1 no.2
• /
• pp.27-31
• /
• 2000

Thermal imaging system is implemented for the measurement and the analysis of the thermal distribution of the target objects. The main Part of the system is thermal camera in which a focal plane array typed sensor is introduced The sensor detects mid-range infrared spectrum or target objects and then it output generic video signal which should be processed to form a thermal image frame. A digital signal processor(DSP) in the system inputs analog to digital converted data. performs algorithms to improve the thermal images and then outputs the corrected frame data to frame buffers for NTSC encoding and for digital outputs.. To enhance the quality of the thermal images, two point correction method is applied. Figures indicate that the corrected thermal images are much improved.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.05a
• /
• pp.827-828
• /
• 2012

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.10a
• /
• pp.880-881
• /
• 2018

Recently technological advancements have allowed visible, infrared and thermal imaging systems to be readily available for security and access control. Increasing applications of facial recognition for security and access control leads to emerging spoofing methodologies. To overcome these challenges of occlusion, replay attack and disguise, researches have proposed using multiple imaging modalities. Using infrared and thermal modalities alongside visible imaging helps to overcome the shortcomings of visible imaging. In this paper we review and propose hybrid feature extraction methods to combine data from multiple imaging systems simultaneously.

• Korean Journal of Remote Sensing
• /
• v.3 no.2
• /
• pp.81-87
• /
• 1987

The study was to analysis the dispersion of the cooling water of Kori atomic power station using thermal infrared data. The dispersion pattern of the cooling water analysis clearly on the LANDSAT TM band 6. It was changed due to tidal current, that is, the cooling water disperses north-eastern direction during the low tide and southweatern direction during the high tide. The relative temperature distribution was mapped through the density slicing method on the images.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1993.10a
• /
• pp.1354-1363
• /
• 1993

Optical methods were developed to examine their feasibility for quality evaluation of peanut with pod. Surface color and internal quality of peanut were measured without contact. The surface color of peanut was measured by light reflectance at a region of visible wavelengths. Its characteristic was high correlated with a visual grading of peanut. A trial machine for the color grading of peanut was developed using an optical sensor and it was considered to compare with the visual grading. The spectral reflectance at a region of near infrared wavelengths from 1,200 to 2,500nm was measured , and the chemical components of peanut were related to spectral reflectance at special wavelengths. The protein, fat and moisture contents of peanut were estimated by the near infrared methods. An infrared imaging method was developed to evaluate the internal quality of peanut with pod. As thermal characteristic of peanut with pod was deeply related to internal quality , the quality of peanut can be evaluated by temperature changes on the surface of peanut. Measurement of surface color, near infrared reflectance and thermal imaging were shown to be very effective in grading of peanut with pod.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.11
• /
• pp.55-61
• /
• 2009

The wall thinning defect of nuclear power pipe is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid. This type of defect becomes the cause of damage or destruction of in carbon steel pipes. Therefore, it is very important to measure defect which is existed not only on the welding part but also on the whole field of pipe. This study use dual-beam Shearography, which can measure the out-of-plane deformation and the in-plane deformation by using another illuminated laser beam and simple image processing technique. And this study proposes Infrared thermography, which is a two-dimensional non-contact nondestructive evaluation that can detect internal defects from the thermal distribution by the inspection of infrared light radiated from the object surface. In this paper, defect of nuclear power pipe were, measured using dual-beam shearography and infrared thermography, quantitatively evaluated by the analysis of phase map and thermal image pattern.

• Journal of Sensor Science and Technology
• /
• v.30 no.6
• /
• pp.456-460
• /
• 2021

In this study, a Non-Dispersive Infrared (NDIR) Carbon Dioxide (CO₂) sensor with an externally exposed optical cavity is proposed for improving sensitivity. NDIR CO₂ sensors with high performance must use a lamp-type infrared (IR) source with a strong IR intensity. However, a lamp-type IR source generates high thermal energy that induces thermal noise, interfering with the accuracy of the CO₂ concentration measure. To solve this problem, the optical cavity of the NDIR CO₂ sensor is exposed to quickly dissipate heat. As a result, the proposed NDIR CO₂ sensor has a shorter warm-up time and a higher sensitivity compared to the conventional NDIR CO₂ sensor.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.6
• /
• pp.23-29
• /
• 2014

In this study, cooling and heat-transfer tests are performed to compare and evaluate the thermal conductivity in a prepared CNT TIM (thermal interface material). A polymerized CNT heat-transfer resin and commercial thermal grease (Shinetsu G-747) were applied for a comparison test in both cases. Cooling experiments with an aluminum foil specimen were performed in order to measure the temperature distribution using an infrared camera, and in heat radiation experiments, performance testing of the thermal conductivity was conducted using high-power LEDs. Carbon resin with the polymerization of graphite and carbon black, and CNT-polymerized CNT resin with graphite and carbon black were tested and compared with using G-747. It was found that the cooling performance and the heat transfer ability in both the carbon resin and the CNT-polymerized CNT resin were greater than those of G-747 because the temperature by 5. $0^{\circ}C$ in both cases appeared lower than that of the G-747.

• Animal Bioscience
• /
• v.34 no.7
• /
• pp.1235-1242
• /
• 2021

Objective: The purpose of this study was to identify through infrared thermal imaging technology the facial surface temperature (FST) of laying hens in response to the variations in their thermal environment, and to identify the regional differences in FST to determine the most stable and reliable facial regions for monitoring of thermoregulatory status in chickens. Methods: Thirty Hy-Line Brown hens (25-week-old) were sequentially exposed to three different thermal conditions; optimal (OT, 22℃±2℃), low (LT, 10℃±4℃), and high temperature (HT, 30℃±2℃). The mean values of FST in five facial regions including around the eyes, earlobes, wattles, beak and nose, and comb were recorded through infrared thermography. The maximum FST (MFST) was also identified among the five face-selective regions, and its relationship with temperature-humidity index (THI) was established to identify the range of MFST in response to the variations in their thermal environment. Results: Hens exposed to OT condition at 15:00 displayed a higher temperature at wattles and around the eyes compared to other regions (p<0.001). However, under LT condition at 05:00 to 08:00, around the eyes surface temperature showed the highest value (p<0.01). In HT, wattles temperature tended to show the highest temperature over almost time intervals. Main distribution regions of MFST were wattles (63.3%) and around the eyes (16.7%) in OT, around the eyes (50%) in LT, and wattles (62.2%) and comb (18.3%) in HT. The regression equation between MFST and THI was estimated as MFST = 35.37+0.2383×THI (R² = 0.44; p<0.001). Conclusion: The FST and the frequency of MFST in each facial region of laying hens responded sensitively to the variations in the thermal environment. The findings of this experiment provide useful information about the effect of the thermal conditions on the specific facial regions, thus offering an opportunity to stress and welfare assessment in poultry research and industry.