본 연구는 메꽃과 6종의 식물에 대해 신속하고 비파괴적으로 분류하기 위해 근적외선 (Vis-NIR) 스펙트럼을 이용하였고 데이터의 전처리와 머신러닝 기술을 적용하였다. 전국적으로 분포하는 메꽃과 6종에 대해 야외에서 휴대용 분광기를 이용하여 판별하였다. 식물의 잎의 표면에서 400~1,075 nm의 근적외선 스펙트럼 (1.5 nm)을 수집하였 다. 수집된 스펙트럼 데이터는 3가지의 전처리와 raw데이터를 이용하였고 4종류의 머신러닝 모델을 적용하여 높은 판별 정확도를 확인하였다. 전처리와 머신러닝 모델의 조합을 통해 분석된 판별의 정확도는 43~99%의 범위로 분석되었고, standard normal variate 전처리와 support vector machine 머신러닝 모델의 조합에서 판별 정확도가 98.6% 로 가장 높게 나타났다. 본 연구에서 수집된 스펙트럼은 식물의 성장단계, 다양한 측정 지역 및 잎에서의 측정 위치 등과 같은 요인과 더불어 데이터 분석을 위한 조건으로 최 적의 전처리와 머신러닝 기술을 적용한다면 메꽃과 식물의 야외에서의 정확한 분류가 가능하고 이들 식물의 효과적인 관리와 모니터링에 활용할 수 있을 것으로 판단되었다.

Variability is one of the major characteristics of Active Galactic Nuclei (AGN), and it is used for understanding the energy generation mechanism in the center of AGN and/or related physical phenomena. It it known that there exists a time lag between AGN light curves simultaneously observed at different wavelengths, which can be used as a tool to estimate the size of the area that produce the radiation. In this paper, We present long term near-infrared variability of optically bright type 1 AGN using the Wide-field Infrared Survey Explorer data. From the Milliquas catalogue v6.4, 73 type 1 QSOs/AGN and 140 quasar candidates are selected that are brighter than 18 mag in optical and located within 5 degree around the ecliptic poles. Light curves in the W1 band (3.4 ㎛) and W2 band (4.6 ㎛) during the period of 2010-2019 were constructed for these objects by extracting multi-epoch photometry data from WISE and NEOWISE all sky survey database. Variability was analyzed based on the excess variance and the probability Pvar . Applying both criteria, the numbers of variable objects are 19 (i.e., 26%) for confirmed AGN and 12 (i.e., 9%) for AGN candidates. The characteristic time scale of the variability (τ) and the variability amplitude (σ) were derived by fitting the DRW model to W1 and W2 light curves. No significant correlation is found between the W1/W2 magnitude and the derived variability parameters. Based on the subsample that are identified in the X-ray source catalog, there exists little correlation between the X-ray luminosity and the variability parameters. We also found four AGN with changing W1-W2 color.

Near infrared reflectance spectroscopy (NIRS) is routinely used for the determination of nutrient components of forages. However, little is known about the impact of sample preparation and wavelength on the accuracy of the calibration to predict minerals. This study was conducted to assess the effect of sample preparation and wavelength of near infrared spectrum for the improvement of calibration and prediction accuracy of Calcium (Ca) and Phosphorus (P) in imported hay using NIRS. The samples were scanned in reflectance in a monochromator instrument (680–2,500 nm). Calibration models (n = 126) were developed using partial least squares regression (PLS) based on cross-validation. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2) and the lowest standard error of cross-validation (SECV). The highest R2 and the lowest SECV were obtained using oven-dry grinded sample preparation and 1,100-2,500 nm wavelength. The calibration (R2) and SECV were 0.99 (SECV: 468.6) for Ca and 0.91 (SECV: 224.7) for P in mg/kg DM on a dry weight, respectively. Results of this experiment showed the possibility of NIRS method to predict mineral (Ca and P) concentration of imported hay in Korea for routine analysis method to evaluate the feed value.

The aim of this study was to investigate the feasibility of discrimination 12 different cultivar of sorghum × sudangrass hybrid (Sorghum genus) seed through near infrared spectroscopy (NIRS). The amount of samples for develop to the best discriminant equation was 360. Whole samples were applied different three spectra range (visible, NIR and full range) within 680-2500 nm wavelength and the spectrastar 2500 Near near infrared was used to measure spectra. The calibration equation for discriminant analysis was developed partial least square (PLS) regression and discrimination equation (DE) analysis. The PLS discriminant analysis model for three spectra range developed with mathematic pretreatment 1,8,8,1 successfully discriminated 12 different sorghum genus. External validation indicated that all samples were discriminated correctly. The whole discriminant accuracy shown 82 ~ 100 % in NIR full range spectra. The results demonstrated the usefulness of NIRS combined with chemometrics as a rapid method for discrimination of sorghum × sudangrass hybrid cultivar through seed.

목적 : 근적외선 차단렌즈의 색 왜곡을 평가하기 위하여 객관적 및 주관적 측색 결과를 분석하였다. 방법 : Colorchecker Classic의 15번(Red), 16번(Yellow) 및 14번(Green) 색표를 가상신호등으로 설정하여 객관적 측색을 실시하였다. 객관적 평가를 위하여 디지털 카메라에 각 렌즈를 장착한 후 가상신호등을 촬영하였 다. 본래 색상과 렌즈 장착 후 측정된 색을 CIE 1976 L*a*b* 색도좌표로 표시하였고, 좌표간 거리 값인 ΔE* ab를 산출하여 색 왜곡도를 비교 분석하였다. 32명을 대상으로 주관적 평가를 실시하였다. 교통신호등의 관찰된 색을 한국 색채 표준 디지털팔레트에서 선택하도록 하여 본래 색상과 비교하였다. 결과 : 객관적 평가에서, 황색 및 녹색 가상신호등의 색왜곡은 근적외선 차단렌즈 착용 시 가장 작은 것으로 나 타났다. 근적외선 차단렌즈에 의한 적색 가상신호등의 색왜곡은 녹색렌즈보다 크지만 갈색, 회색 및 청색렌즈에 비교하여 더 적은 것으로 나타났다. 주관적 평가에서, 적색 신호등을 주시했을 때 근적외선 차단렌즈에 의한 색왜곡은 갈색렌즈와 비교하여 더 많았으며, 청색 및 녹색렌즈 착용과 유사하였고, 회색렌즈 착용보다 적은 것으로 나타났다. 결론 : 근적외선 차단렌즈의 색왜곡은 다른 렌즈와 비교하여 객관적 평가에서 가장 낮았으나, 주관적 평가에서는 유의한 차이가 없었다. 근적외선 차단렌즈의 주관적 색 재현성을 정량적으로 조사할 수 있는 추가적인 연구가 필요할 것으로 생각된다.

In this study, whole crop rice samples were used to develop near-infrared reflectance (NIR) equations to estimate six forage quality parameters: Moisture, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), Ash and relative feed value (RFV). A population of 564 whole crop rice representing a wide range in chemical parameters was used in this study. Undried finely chopped whole crop rice samples were scanned at 1 nm intervals over the wavelength range 680–2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). NIRS calibrations were developed by means of partial least-squares (PLS) regression. The correlation coefficients of cross-validation (R2 cv) and standard error of cross-validation (SECV) for whole crop rice calibration were 0.98 (SECV 1.81%) for moisture, 0.89 (SECV 0.50%) for CP, 0.86 (SECV 1.79%) for NDF, 0.89 (SECV 0.86%) for ash, and 0.84 (SECV 5.21%) for RFV on a dry matter (%), respectively. The NIRS calibration equations developed in this study will be useful in predicting whole crop rice quality for these six quality parameters.

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R2) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R2 and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

목적 : 근적외선 흡수렌즈의 광학적 특성과 단열특성을 분석하였다. 방법 : 선명도를 분석하기 위해 화상테스트 차트를 카메라로 촬영하였고, 이미지 분석 프로그램을 이용하여 분석하였다. 32명을 대상으로 시력을 측정하였다. 단열효과를 분석하기 위해 돼지피부와 안검 피부에 근적외선을 조사하고 디지털온도계와 열화상카메라를 사용하여 온도를 측정했다. 렌즈 표면에 근적외선을 조사하여 표면온도를 측정하였고, 열에 의한 렌즈손상을 관찰하였다. 근적외선 흡수안경과 착색안경을 착용하고 선명도, 눈부심, 열감, 광량에 대한 주관적 만족도를 평가하였다. 결과 : 근적외선 흡수렌즈와 착색렌즈에 의한 선명도와 시력은 유의한 차이가 없었다. 근적외선 흡수렌즈를 착용 했을 때 돼지피부와 사람 안검의 온도변화는 착색렌즈를 착용했을 때보다 더 낮았다. 근적외선 조사에 의해 근적외선 흡수렌즈가 착색렌즈보다 표면의 온도가 더 높았고, 더 빨리 손상되었다. 설문조사에서 근적외선 흡수렌즈에 의한 선명도(p=0.040)와 눈부심(p=0.000)에 관한 만족도는 모두 청색렌즈보다 더 높았지만 나머지 착색렌즈들과 유의한 차이가 없었다. 근적외선 흡수렌즈에 의한 열감과 광량에 관한 주관적 만족도는 착색렌즈보다 더 높았다. 결론 : 근적외선 흡수렌즈와 착색렌즈에 의한 선명도와 시력은 유의한 차이가 없었으며, 근적외선 흡수렌즈의 단열효과는 착색렌즈보다 더 좋았다. 본 연구는 근적외선 흡수렌즈의 단열효과에 대한 기초자료를 제시하였다고 생각된다.

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R2) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The R2 and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

LuNbO4:0.2Yb3+,xTm3+ powders were prepared using a solid-state reaction process. The effects of the amount of Tm on up-conversion(UC) and down-conversion(DC) luminescence properties are investigated. X-ray diffraction patterns confirm that Yb3+ and Tm3+ ions are successfully incorporated into Lu sites. Under 980 nm excitation, the UC spectra of the powders predominantly exhibit strong near-infrared emission bands that peak at 805 nm, whereas weak 480 nm emission bands are observed as well. The emission bands are assigned to the 1G4→ 3H6 (480 nm) and 3H4→ 3H6 (805 nm) transitions of the Tm3+ ions via an energy transfer from Yb3+ to Tm3+; two- and three-photon UC processes are responsible for the 805 and 480 nm emissions, respectively. The DC emission spectra exhibit blue emission (1D2→ 3F4) of Tm3+ at 458 nm. The amount of Tm affects the emission intensity with the strongest emissions at x = 0.007 and 0.02 for the UC and DC luminescence, respectively. The results demonstrate that LuNbO4:Yb3+,Tm3+ phosphors are suitable for bio-applications.