• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.104.1-104.1
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• 2011

We present a catalog of infrared supernova remnants (SNRs) in the Large Magellanic Cloud (LMC). We have searched the Spitzer archival data for infrared counterparts to all 45 known SNRs in the LMC, and identified 21 which is 47% of the known SNRs. Seven of them are newly detected: SNR 0450-70.9, SNR in N4, N103B, DEM L241, DEM L249, DEM L316A, and DEM L316B. All newly discovered SNRs show emission at several IRAC 3.4, 4.5, 5.8, and 8.0 micron bands and/or MIPS 24 and 70 micron bands. Most SNRs show shell structures. We derive infrared fluxes of these newly detected SNRs. The catalog contains general information of each SNR such as location, age, and SN type together with AKARI and/or Spitzer fluxes. For the entire SNR sample, we examine their infrared colors and the possible correlation of the infrared fluxes with the fluxes at other wavelengths. For the newly detected SNRs except the SNR in N4, we also performed follow-up imaging observations of [Fe II] 1.644 micron line using IRIS2 mounted on the Anglo Australian Telescope. Three out of six SNRs show [Fe II] emission corresponding to their infrared shells. [Fe II] knots are also detected in N103B which show good spatial correlation to infrared emission seen at Spitzer images as well as knotty $H{\alpha}$ emission. We investigate the characteristics and origin of the infrared emission in individual SNRs, and discuss the environmental and evolutionary effects.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.363-365
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• 2012

We report a search for fluctuations of the sky brightness toward the North Ecliptic Pole with AKARI, at 2.4, 3.2, and $4.1{\mu}m$. The stacked images with a diameter of 10 arcminutes of the AKARI-Monitor Field show a spatial structure on the scale of a few hundred arcseconds. A power spectrum analysis shows that there is a significant excess fluctuation at angular scales larger than 100 arcseconds that cannot be explained by zodiacal light, diffuse Galactic light, shot noise of faint galaxies, or clustering of low-redshift galaxies. These findings indicate that the detected fluctuation could be attributed to the first stars of the universe, i.e., Population III stars.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1060-1066
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material efficiently. In this paper a detection of the welding defect of thin SUS 304 plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (20kHz) ultrasonic transducer was used to infuse the welded thin SUS 304 plates with a short pulse of sound for 280ms. The ultrasonic source has a maximum power of 2kW. The surface temperature of the area under inspection is imaged by a thermal infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the defect tip and heated up highly, are observed. From the sequence of the thermosonic images, the location of defective or inhomogeneous regions in the welded thin SUS 304 plates can be detected easily.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.77-84
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• 2024

Aging and damaged underground utilities cause cavity and ground subsidence under roads, which can cause economic losses and risk user safety. This study used infrared cameras to assess the thermal characteristics of such cavities and evaluate their reliability using a CNN algorithm. PVC pipes were embedded at various depths in a test site measuring 400 cm × 50 cm × 40 cm. Concrete blocks were used to simulate road surfaces, and measurements were taken from 4 PM to noon the following day. The initial temperatures measured by the infrared camera were 43.7℃, 43.8℃, and 41.9℃, reflecting atmospheric temperature changes during the measurement period. The RP algorithm generates images in four resolutions, i.e., 10,000 × 10,000, 2,000 × 2,000, 1,000 × 1,000, and 100 × 100 pixels. The accuracy of the CNN model using RP images as input was 99%, 97%, 98%, and 96%, respectively. These results represent a considerable improvement over the 73% accuracy obtained using time-series images, with an improvement greater than 20% when using the RP algorithm-based inputs.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1213-1218
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• 2018

Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1181-1187
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• 2009

In this paper, we proposed a method to analyze kind of clouds using a fuzzy reasoning method. In the proposed method, we used the clues that G channel value is dominant from RGB color values in land areas and B channel value is dominant in the sea areas discovered by the analyses of both visible images and infrared images. By these information, R and B channel values are applied to land areas and R and G channel values are applied to the sea areas. Noise areas(areas except cloud areas) are removed from a visible image and an infrared image by a threshold value, and then land areas and the sea areas are discriminated from the noise removed image. Cloud areas are extracted from discriminated areas using R, G, B channel values and a fuzzy reasoning method, and finally kind of clouds is decided by combining same cloud areas included in both the visible image and the infrared image. In comparison with a conventional quantization method, we verified that the performance of cloud analysis by the proposed method is more efficient through experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.192-196
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• 2005

Key part of main equipment in a gas turbine may be likely to be damaged due to operation under high temperature, high pressure, high-speed rotation, etc. Accordingly, the cost for maintenance increases and the damaged parts may cause generation to stop. The number of parts for maintenance also increases, but diagnostics technology fur the maintenance actually does not catch up with the demand. Blades are made of precipitation hardening Ni superalloy IN738 and the like for keeping hot strength. The surface of a blade is thermal-sprayed, using powder with main compositions such as Ni, Cr, Al, etc. in order to inhibit hot oxidation. Conventional regular maintenance of the coating layer of a blade is made by FPI (Fluorescent Penetrant Inspection) and MTP (Magnetic Particle Testing). Such methods, however, are complicated and take long time and also require much cost. In this study, defect diagnostics were tested for the coating layer of an industrial gas turbine blade, using an infraredthermography camera. Since the infrared thermography method can check a temperature distribution on a wide range of area by means of non-contact, it can advantageously save expenses and time as compared to conventional test methods. For the infrared thermography method, however, thermo-load must be applied onto a tested specimen and it is difficult to quantify the measured data. To solve the problems, this essay includes description about producing a specimen of a gas turbine blade (bucket), applying thermo-load onto the produced specimen, photographing thermography images by an infrared thermography camera, analyzing the thermography images, and pre-testing for analyzing defects on the coating layer of the gas turbine blade.

• Journal of Acupuncture Research
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• v.21 no.2
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• pp.223-233
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• 2004

Objective: As a manual accupucture method, the twirling-needle treatment has been known more effective in relieving pain than the conventional simple accupuncture treatment. Finding a proper treatment condition is difficult because of the lack of a quantative measurement of the alleviation of pain made by acupuncture. In this research, the authors propose the use of infrared thermal images in a formalin test to quantatively verify the effect of twirling. Methods: After injecting 10%~20% formalin into the tail of rats, the infrared thermal images(ITI) have been obtained to estimate the thermal distribution caused by inflammation. The authors propose a processing method to measure the thermal distribution from the thermal images obtained from the infrared camera as a pain model of the formalin test. Results: The pain model obtained from the infrared thermal image has two phases. The first phase, which is a transient period, is the initial 20 minutes when the pain is developed after the formalin injection. The second phase, which is a steady state, is where the development of pain lasts for 60 minutes or more after the first stage. This characteristic of the proposed model based on ITI is consistent with that of the pain model reported by other researchers whose works are based on the time-course of flinching and licking/biting, following a different concentration of formalin. It is noticed that the response of the thermal distribution obtained from ITI shows very high correlation to the behavioral response in the formalin test performed by Kazuhiro Okuda and four others5). In addition, the authors propose an ITI method to determine the pain-reducing effect of the acupuncture. The thermal distribution obtained from the experiment shows that there is significant pain reducing effect made by the twirling-needle method.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.524-531
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• 2009

In this paper, we propose an automatic image registration method for multi-sensor image fusion such as visible and infrared images. The registration is achieved by finding corresponding feature points in both input images. In general, the global statistical correlation is not guaranteed between multi-sensor images, which bring out difficulties on the image registration for multi-sensor images. To cope with this problem, mutual information is adopted to measure correspondence of features and to select faithful points. An update algorithm for projective transform is also proposed. Experimental results show that the proposed method provides robust and accurate registration results.