• Title/Summary/Keyword: Reflectance performance

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Characteristic Analysis and Preparation of Multi-layer TiNOx Thin Films for Solar-thermal Absorber (태양열 흡수판용 복층 TiNOx 박막의 제조와 특성 분석)

  • Oh, Dong-Hyun;Han, Sang-Uk;Kim, Hyun-Hoo;Jang, Gun-Eik;Lee, Yong-Jun
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.27 no.12
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    • pp.820-824
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    • 2014
  • TiNOx multi-layer thin films on aluminum substrates were prepared by DC reactive magnetron sputtering method. 4 multi-layers of $TiO_2$/TiNOx(LMVF)/TiNOx(HMVF)/Ti/substrate have been prepared with ratio of Ar and ($N_2+O_2$) gas mixture. $TiO_2$ of top layer is anti-reflection layer on double TiNOx(LMVF)/TiNOx(HMVF) layers and Ti metal of infrared reflection layer. In this study, the crystallinity and surface properties of TiNOx thin films were estimated by X-ray diffraction(XRD) and field emission scanning electron microscopy(FE-SEM), respectively. The grain size of TiNOx thin films shows to increase with increasing sputtering power. The composition of thin films has been investigated using electron probe microanalysis(EPMA). The optical properties with wavelength spectrum were recorded by UV-Vis-NIR spectrophotometry at a range of 200~1,500 nm. The TiNOx multi-layer films show the excellent optical performance beyond 9% of reflectance in those ranges wavelength.

Optical Properties Analysis of SiNx Double Layer Anti Reflection Coating by PECVD

  • Gong, Dae-Yeong;Park, Seung-Man;Yi, Jun-Sin
    • Proceedings of the Korean Vacuum Society Conference
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    • 2010.02a
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    • pp.149-149
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    • 2010
  • The double-layer antireflection (DLAR) coatings have significant advantages over single-layer antireflection (SLAR) coatings. This is because they will be able to cover a broad range of the solar spectrum which would enhance the overall performance of solar cells. Moreover films deposited at high frequency are expected to show excellent and UV-stable passivation in the refractive index that we adopted. In this work, we present a novel DLAR coating using SiNx:H thin films with refractive indices 1.9 and 2.3 as the top and bottom layers. This approach is cost effective when compared to earlier DLAR coatings with two different materials. SiNx:H films were deposited by Plasma enhanced chemical vapor deposition (PECVD) technique using $SiH_4$, $NH_3$ and $N_2$ gases with flow rates 20~80sccm, 200sccm and 85 sccm respectively. The RF power, plasma frequency and substrate temperature for the deposition were 300W, 13.56 MHz and $450^{\circ}C$, respectively. The optimum thickness and refractive indices values for DLAR coatings were estimated theoretically using Macleod simulation software as 82.24 nm for 1.9 and 68.58 nm for 2.3 respectively. Solar cells were fabricated with SLAR and DLAR coatings of SiNx:H films and compared the cell efficacy. SiNx:H> films deposited at a substrate temperature of $450^{\circ}C$ and that at 300 W power showed best effective minority carrier lifetime around $50.8\;{\mu}s$. Average reflectance values of SLAR coatings with refractive indices 1.9, 2.05 and 2.3 were 10.1%, 9.66% and 9.33% respectively. In contrast, optimized DLAR coating showed a reflectance value as low as 8.98% in the wavelength range 300nm - 1100nm.

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The evaluation of Spectral Vegetation Indices for Classification of Nutritional Deficiency in Rice Using Machine Learning Method

  • Jaekyeong Baek;Wan-Gyu Sang;Dongwon Kwon;Sungyul Chanag;Hyeojin Bak;Ho-young Ban;Jung-Il Cho
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2022.10a
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    • pp.88-88
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    • 2022
  • Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

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Development of Artificial Intelligence-Based Remote-Sense Reflectance Prediction Model Using Long-Term GOCI Data (장기 GOCI 자료를 활용한 인공지능 기반 원격 반사도 예측 모델 개발)

  • Donguk Lee;Joo Hyung Ryu;Hyeong-Tae Jou;Geunho Kwak
    • Korean Journal of Remote Sensing
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    • v.39 no.6_2
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    • pp.1577-1589
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    • 2023
  • Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

Robust Radiometric and Geometric Correction Methods for Drone-Based Hyperspectral Imaging in Agricultural Applications

  • Hyoung-Sub Shin;Seung-Hwan Go;Jong-Hwa Park
    • Korean Journal of Remote Sensing
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    • v.40 no.3
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    • pp.257-268
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    • 2024
  • Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

Effect of Surface Morphology in ZnO:Al/Ag Back Reflectors for Flexible Silicon Thin Film Solar Cells on Light Scattering Properties (플렉서블 실리콘 박막 태양전지용 ZnO:Al/Ag 후면반사막의 표면형상에 따른 광산란 특성 변화)

  • Beak, Sang-Hun;Lee, Jeong-Chul;Park, Sang-Hyun;Song, Jin-Soo;Yoon, Kyung-Hoon;Wang, Jin-Suk;Lee, Hi-Deok;Cho, Jun-Sik
    • Korean Journal of Materials Research
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    • v.20 no.10
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    • pp.501-507
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    • 2010
  • Changes in surface morphology and roughness of dc sputtered ZnO:Al/Ag back reflectors by varying the deposition temperature and their influence on the performance of flexible silicon thin film solar cells were systematically investigated. By increasing the deposition temperature from $25^{\circ}C$ to $500^{\circ}C$, the grain size of Ag thin films increased from 100 nm to 1000 nm and the grain size distribution became irregular, which resulted in an increment of surface roughness from 6.6 nm to 46.6 nm. Even after the 100 nm thick ZnO:Al film deposition, the surface morphology and roughness of the ZnO:Al/Ag double structured back reflectors were the same as those of the Ag layers, meaning that the ZnO:Al films were deposited conformally on the Ag films without unnecessary changes in the surfacefeatures. The diffused reflectance of the back reflectors improved significantly with the increasing grain size and surface roughness of the Ag films, and in particular, an enhanced diffused reflectance in the long wavelength over 800 nm was observed in the Ag back reflectors deposited at $500^{\circ}C$, which had an irregular grain size distribution of 200-1000 nm and large surface roughness. The improved light scattering properties on the rough ZnO:Al/Ag back reflector surfaces led to an increase of light trapping in the solar cells, and this resulted in a noticeable improvement in the $J_{sc}$ values from 9.94 mA/$cm^2$ for the flat Ag back reflector at $25^{\circ}C$ to 13.36 mA/$cm^2$ for the rough one at $500^{\circ}C$. A conversion efficiency of 7.60% ($V_{oc}$ = 0.93, $J_{sc}$ = 13.36 mA/$cm^2$, FF = 61%) was achieved in the flexible silicon thin film solar cells at this moment.

Establishment of a Nondestructive Analysis Method for Lignan Content in Sesame using Near Infrared Reflectance Spectroscopy (근적외선분광(NIRS)을 이용한 참깨의 lignan 함량 비파괴 분석 방법 확립)

  • Lee, Jeongeun;Kim, Sung-Up;Lee, Myoung-Hee;Kim, Jung-In;Oh, Eun-Young;Kim, Sang-Woo;Kim, MinYoung;Park, Jae-Eun;Cho, Kwang-Soo;Oh, Ki-Won
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.67 no.1
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    • pp.61-66
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    • 2022
  • Sesamin and sesamolin are major lignan components with a wide range of potential biological activities of sesame seeds. Near infrared reflectance spectroscopy (NIRS) is a rapid and non-destructive analysis method widely used for the quantitative determination of major components in many agricultural products. This study was conducted to develop a screening method to determine the lignan contents for sesame breeding. Sesamin and sesamolin contents of 482 sesame samples ranged from 0.03-14.40 mg/g and 0.10-3.79 mg/g with an average of 4.93 mg/g and 1.74 mg/g, respectively. Each sample was scanned using NIRS and calculated for the calibration and validation equations. The optimal performance calibration model was obtained from the original spectra using partial least squares (PLS). The coefficient of determination in calibration (R2) and standard error of calibration (SEC) were 0.963 and 0.861 for sesamin and 0.875 and 0.292 for sesamolin, respectively. Cross-validation results of the NIRS equation showed an R2 of 0.889 in the prediction for sesamin and 0.781 for sesamolin and a standard error of cross-validation (SECV) of 1.163 for sesamin and 0.417 for sesamolin. The results showed that the NIRS equation for sesamin and sesamolin could be effective in selecting high lignan sesame lines in early generations of sesame breeding.

A Graphene-electrode-based Infrared Fresnel Lens with Multifocal Function (다초점 기능을 갖는 그래핀 전극 기반 적외선 프레넬 렌즈)

  • Nam, Guk Hyun;Lee, Jong-Kwon
    • Korean Journal of Optics and Photonics
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    • v.33 no.1
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    • pp.28-34
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    • 2022
  • We study through computational simulation the focal performance of an infrared (IR) Fresnel lens, composed of a multilayer-graphene zone plate formed under a graphene electrode. Here the Fermi level EF of the patterned multilayer graphene is adjusted by the overlying graphene electrode. The Fresnel lens effect, with respect to the reflectance contrast between the graphene electrode and the 8-layer graphene zone plate placed on a glass substrate, has been analyzed over a broad wavelength range from 4 to 30 ㎛. As the optimal wavelength of 8 ㎛ (considering the reflectance and the reflectance-contrast ratio) is incident upon the Fresnel lens with a focal length of 240 ㎛, the focal intensity is enhanced by a factor of 4.3 as the EF of multilayer graphene increases from 0.4 eV to 1.6 eV, and is improved by a factor of 5.8 as the number of graphene layers increases from two to eight. As a result, an all-graphene-based IR Fresnel zone-plate lens, exhibiting multifocal function (240 ㎛ and 360 ㎛) according to the selected EF, is proposed as an ultrathin lens platform.

THE USE OF NEAR INFRARED REFLECTANCE SPECTROSCOPY(NIRS) TO PREDICT CHEMICAL COMPOSITION ON MAIZE SILAGE

  • D.Cozzolino;Fassio, A.;Mieres, J.;Y.Acosta
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1610-1610
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    • 2001
  • Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

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Estimation of Chlorophyll-a Concentrations in the Nakdong River Using High-Resolution Satellite Image (고해상도 위성영상을 이용한 낙동강 유역의 클로로필-a 농도 추정)

  • Choe, Eun-Young;Lee, Jae-Woon;Lee, Jae-Kwan
    • Korean Journal of Remote Sensing
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    • v.27 no.5
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    • pp.613-623
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    • 2011
  • This study assessed the feasibility to apply Two-band and Three-band reflectance models for chlorophyll-a estimation in turbid productive waters whose scale is smaller and narrower than ocean using a high spatial resolution image. Those band ratio models were successfully applied to analyzing chlorophyll-a concentrations of ocean or coastal water using Moderate Imaging Spectroradiometer(MODIS), Sea-viewing Wide Field-fo-view Sensor(SeaWiFS), Medium Resolution Imaging Spectrometer(MERIS), etc. Two-band and Three-band models based on band ratio such as Red and NIR band were generally used for the Chl-a in turbid waters. Two-band modes using Red and NIR bands of RapidEye image showed no significant results with $R^2$ 0.38. To enhance a band ratio between absorption and reflection peak, We used red-edge band(710 nm) of RapidEye image for Twoband and Three-band models. Red-RE Two-band and Red-RE-NIR Three-band reflectance model (with cubic equation) for the RapidEye image provided significance performances with $R^2$ 0.66 and 0.73, respectively. Their performance showed the 'Approximate Prediction' with RPD, 1.39 and 1.29 and RMSE, 24.8, 22.4, respectively. Another three-band model with quadratic equation showed similar performances to Red-RE two-band model. The findings in this study demonstrated that Two-band and Three-band reflectance models using a red-edge band can approximately estimate chlorophyll-a concentrations in a turbid river water using high-resolution satellite image. In the distribution map of estimated Chl-a concentrations, three-band model with cubic equation showed lower values than twoband model. In the further works, quantification and correction of spectral interferences caused by suspended sediments and colored dissolved organic matters will improve the accuracy of chlorophyll-a estimation in turbid waters.