• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Proceedings of the KSRS Conference
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• v.1
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• pp.278-281
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• 2006

Korea Meteorological Administration(KMA) has issued the tropical storm(typhoon) warning or advisories when it was developed to tropical storm from tropical depression and a typhoon is expected to influence the Korean peninsula and adjacent seas. Typhoon information includes current typhoon position and intensity. KMA has used the Dvorak Technique to analyze the center of typhoon and it's intensity by using available geostationary satellites' images such as GMS, GOES-9 and MTSAT-1R since 2001. The Dvorak technique is so subjective that the analysis results could be variable according to analysts. To reduce the subjective errors, QuikSCAT seawind data have been used with various analysis data including sea surface temperature from geostationary meteorological satellites, polar orbit satellites, and other observation data. On the other hand, there is an advantage of using the Subjective Dvorak Technique(SDT). SDT can get information about intensity and center of typhoon by using only infrared images of geostationary meteorology satellites. However, there has been a limitation to use the SDT on operational purpose because of lack of observation and information from polar orbit satellites such as SSM/I. Therefore, KMA has established Advanced Objective Dvorak Technique(AODT) system developed by UW/CIMSS(University of Wisconsin-Madison/Cooperative Institude for Meteorological Satellite Studies) to improve current typhoon analysis technique, and the performance has been tested since 2005. We have developed statistical relationships to correct AODT CI numbers according to the SDT CI numbers that have been presumed as truths of typhoons occurred in northwestern pacific ocean by using linear, nonlinear regressions, and neural network principal component analysis. In conclusion, the neural network nonlinear principal component analysis has fitted best to the SDT, and shown Root Mean Square Error(RMSE) 0.42 and coefficient of determination($R^2$) 0.91 by using MTSAT-1R satellite images of 2005. KMA has operated typhoon intensity analysis using SDT and AODT since 2006 and keep trying to correct CI numbers.

• Journal of Plant Biotechnology
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• v.38 no.1
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• pp.9-14
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• 2011

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves of nine commercial Orostachys plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA). The dendrogram based on hierarchical clustering analysis of these PLS-DA data separated the nine Orostachys species into five major groups. The first group consisted of O. iwarenge 'Yimge', 'Jeju', 'Jeongsun' and O. margaritifolius 'Jinju' whereas in the second group, 'Sacheon' was clustered with 'Busan,' both of which belong to O. malacophylla species. However, 'Samchuk', belong to O. malacophylla was not clustered with the other O. malacophylla species. In addition, O. minuta and O. japonica were separated to the other Orostachys plants. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from leaves represented the most probable chemotaxonomical relationship between commercial Orostachys plants. Furthermore these metabolic discrimination systems could be applied for reestablishment of precise taxonomic classification of commercial Orostachys plants.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.4 s.19
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• pp.79-84
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• 2005

When polymeric materials are exposed to fire condition, a lot of heat and toxic gases evolved and cause damage to property and human being. Especially toxic gases are major hazard to life safety. This study FTIR(Fourier Transform Infrared) spectrometer analysis was performaed to etermine the gas analysis and the concentration of gases evolved from PVC, FRP, SMC and Ureathane foam using ASTM E 1678 fire model. And FED toxicity index calculated from FTIR data also presented. By the comparison of animal test adopted in KS F 2271 and FTIR gas analysis method, FTIR gas analysis method can replace current animal toxicity test and produce precise and quantitative combustion gas data.

• Textile Coloration and Finishing
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• v.9 no.3
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• pp.33-41
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• 1997

Polystyrene microcapsules were prepared by microencapsulation technique of solvent-evaporation. These microcapsules were characterized by Infrared spectroscopy, thermal analysis, scanning electron microscopy, and shape analysis. Fragrant water was used as a core material. Thermal and pH stabilities of polystyrene microcapsules were relatively excellent. And functional finishing on cotton fabric with microcapsules was applied.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.315-319
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• 2004

This paper presents the integrity evaluation method of the windings in cast resin transformers using vertical thermal distribution analysis. The method is to compare the temperature distribution in a sound condition specimen with that in the failure condition specimen layer-shorted by accelerated testing. The temperature distribution was acquisited by a infrared thermography system, and the Arrhenius equation was adopted to the accelerated testing. The proposed method may be available to detect the failures in cast resin transformers.

• YAKHAK HOEJI
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• v.24 no.3_4
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• pp.159-165
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• 1980

Tetrathiocyanatocobaltate[II] forms complexes with tertiary amine salts or quarternary ammonium salts which were extractable from aqueous solution by organic solvent. In order to study composition of the complex, the colored crystalline complexes produced were evaluated with elemental analysis, infrared and mass spectra. And also a novel spectrophotometric method for the determination of tertiary amine salt or quarternary ammonium salt with tetrathiocyanatocobaltate[II] was established by organic solvent extraction within coefficient of variation of 1.06-1.35%.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1510-1510
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• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.

• Journal of the Korean Wood Science and Technology
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• v.43 no.6
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• pp.701-713
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• 2015

A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

• The Journal of Korean Obstetrics and Gynecology
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• v.29 no.3
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• pp.1-9
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• 2016

Objectives: Cold Hypersensitivity of Hands and Feet (CHHF) has been diagnosed objectively by Digital Infrared Thermal Imaging (DITI) and has been known to be associated with autonomic nervous system (ANS), which can be assessed by Heart Rate Variability (HRV). This study evaluated the correlation between severity of CHHF and HRV variables.Methods: We studied 155 non-menopausal women with CHHF who visited Kyung Hee University Hospital at Gangdong from 01 October 2013 to 30 April 2016. We measured DITI and HRV of each patient. We used DITI to calculate the severity of CHHF with thermal difference between upper arm (L4, 俠白穴) and palm (P8, 勞宮穴) of both hands and anterior thigh (ST32, 伏兎穴) and dorsum of foot (LI3, 太衝穴) of both feet. The correlation between severity of CHHF and HRV variables were analyzed.Results: In time domain analysis, there was significantly positive correlation between the severity of CHHF and both SDNN and RMSSD. In frequency domain analysis, there was significantly positive correlation between the severity of CHHF and TP, HF and HF Norm while there was significantly negative correlation between the severity of CHHF and LF Norm as well as between the severity of cold hypersensitivity of both hands and LF/HF ratio.Conclusions: The more severe CHHF is, the more increased the function of parasympathetic nerve system (PNS) and relatively decreased the function of sympathetic nerve system (SNS) is. Also, it has known that cold hypersensitivity could be caused by deficiency syndrome and qi deficiency has the same ANS tendency as CHHF. Therefore, in practical fields, this result can be helpful in planning treatment and deciding prognosis in respect of deficiency syndrome.