• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.32.2-32.2
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• 2010

Red dusty Active Galactic Nuclei (AGNs) are suspected to mid-stage between ULIRG and AGN phase. As well as, they are suspected that they have more than 50% of AGN population. To understand character of red AGN, Black Hole (BH) mass of red AGN is a key property and haven't measured by existing method such as reverberation mapping and single epoch method. So we still don't know their character and properties clearly. To estimate properties of red AGNs escape from effect of dust-obscuration, we have obtained Near InfraRed (NIR) spectra of 31 reverberation mapped AGNs and 49 Palomar-Green(PG) Quasi-Stellar Object (QSO) using the infrared camera (IRC) for AKARI with unique wavelength range 2.5-$5.0{\mu}m$. From this spectra, we measured the FWHM and luminosity of brackett ${\alpha}$, ${\beta}$ at 4.0, 2.6 micron meter for deriving new BH mass estimators based on the properties of Brackett line emission.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.1-14
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• 2012

A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.381-382
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• 2012

InfraRed Survey Facility (IRSF) is our facility for near-infrared (NIR) observation located at South African Astronomical Observatory. The NIR camera SIRIUS on the 1.4m telescope provides three $7.7^{\prime}{\times}7.7^{\prime}$ images in the J ($1.25{\mu}m$), H ($1.63{\mu}m$), and $K_S$ ($2.14{\mu}m$) bands simultaneously with a pixel scale of 0.45". IRSF has three unique capabilities, which are suitable for follow-up observations of AKARI-selected objects. Several synergistic studies with AKARI are in progress from stars to galaxies. We introduce advantages of the above unique capabilities of IRSF for further synergistic studies between AKARI and IRSF.

• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.270-275
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• 2012

In this study, we have developed a mobile-based visible/NIR(Near InfraRed) imaging equipment for the animal testing. This equipment can provide visible, NIR and merged image through the viewer program. Especially, merged image help researcher to understand visual messages at animal in-vivo test. Also, it is available to send real-time images through the smart phone. Researcher can communicate with another researcher who is a long distance away. Also, the equipment was made with portable small size to enable it to commercialize.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.852-862
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• 1996

This study was carried out to develop tools to detect defects of apple using machine vision. For the purpose, 6 kinds of frame for color images, R, G, B, h, S, and I frame, and a frame for near infra-red images (NIR frame) were tested first to select one which is useful to segment defect areas from apple images. After then, several methods to classify kind of defect for the segmented defect areas were developed and tested. Five kinds of apple defect -bruise , decay ,fleck worm hole and scar were investigated . The results are as follows: NIR frame was selected as the best one among the 7 kinds of image frame, and R, G and I frames showed favourable result to segment areas of apple defect. Various features of the segmented defect areas were measured to classify the defect areas. Eight kids of feature of the areas-size, roundness, axes length ratio, mean and variance of pixel values, variance of real part of spectrum, mean and variance of power spectrum resulted from spacial ourier transform were observed for the segmented defect areas in the selected 4 frames. then procedures to classify defects using the features were developed for the 4 frames and tested with 75-113 defects on apples. The test resulted that NIR and I frames showed high accuracies to classify the kind of defect as 77% and 76% , respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.6
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• pp.739-743
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• 2017

Product distribution and consumption in the military is difficult due to unique contracts and supply systems. It is difficult to change suppliers immediately when quality problem is encountered. Due to these special circumstances, the quality of products must be thoroughly controlled. Sesame oil is used to increase the taste and nutrition of food, but it is more expensive than other cooking oils. Oil producers may blend other cooking oils with sesame oil to make higher profits, so it has become important to identify good and bad products. In this study, pure sesame oil and blend oils were compared by analyzing their smell, taste, chemical components, and near infra-red spectra to determine quality differences between them.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.628-635
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• 2017

It is important to prescribe and take medicines that are appropriate for symptoms, since medicines are closely related to human health and life. Moreover, it becomes more important to accurately classify genuine medicines with counterfeit, since the number of counterfeit increases worldwide. However, the number of high-quality experts who have enough experience to properly classify them is limited and there exists a need for the automatic technique to classify medicine tablets. In this paper, we propose a method to classify the tablets by using a handheld spectrometer which provides both Near Infra-Red (NIR) and visible light spectrums. We adopted Support Vector Machine(SVM) as a machine learning algorithm for tablet classification. As a result of the simulation, we could obtain the classification accuracy of 99.9 % on average by using both NIR and visible light spectrums. Also, we proposed a two-step SVM approach to discriminate the counterfeit tablets from the genuine ones. This method could improve both the accuracy and the processing time.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.71.1-71.1
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• 2015

We prensent the near-infrared(NIR) images of the asymmetric circumstellar disk around a T-Tauri star in the ${\rho}$ Ophiuchi star-forming region, and two faint stellar objects around central star. These results were obtainted with the Subaru Telescope with HiCIAO(the High-Contrast Instrument with Adaptive Optics) and IRCS(the InfraRed Camera and Spectrograph). The disk shows center-offset from the star and a strong morphological asymmetry along both the major and minor axis. The physical conditions in the disk is derived from the infrared visibilites results and the complete spectral energy distribution using HOCHUNK3D, Monte-Carlo radiative transfer code. Two companion candidates are separated by 11.6 arcsec(~1450 au at 125 parsec) and 4.34 arcsec(~540 au at 125 parsec). This could be the first case, which imaged both of planetary mass companions and disk around same star. We discuss physical structures of the disk, and probablity that two candidates are real companions.

• Journal of Animal Environmental Science
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• v.13 no.2
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• pp.113-120
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• 2007

This study was conducted to measure Nitrogen(N), Phosphate($P_2O_5$), Potassium ($K_2O$), Organic matter(OM) and Moisture content of liquid pig manure by Near Infrared Spectroscopy(NIRS) and to develop an alternative and analytical instrument which are used for measurement of N, $P_2O_5$, $K_2O$, OM, and Moisture contents for liquid pig manure. The liquid pig manure sample's transmittance spectra were measured with a NIRS in the wavelength range of 400 to 2,500 nm. Multiple linear regression and partial least square regression were used for calibrations. The correlation coefficient(RSQ) and standard error of calibration(SEC) obtained for nitrogen were 0.9190 and 2.1649, respectively. The RSQ for phosphate, potassium, organic matter and moisture contents were 0.9749, 0.5046, 0.9883 and 0.9777, and the SEC were 0.5019, 1.9252, 0.1180 and 0.0789, respectively. These results are indications of the rapid determination of components of liquid pig manure through the NIR analysis. The simple analytical instrument for liquid pig manure consisted of a tungsten halogen lamp for light source, a sample holder, a quartz cell, a SM 301 spectrometer for spectrum analyzer, a power supply, an electronics, a computer and a software. Results showed that the simple analytical instrument that was developed can approximately predict the phosphate, organic matter and moisture content of the liquid pig manure when compared to the analysis taken by NIRS. The low predictability value of potassium however, needs further investigation. Generally, the experiment proved that the simple analytical instrument was reliable, feasible and practical for analyzing liquid pig manure.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.1-9
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• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.