• Journal of Biosystems Engineering
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• v.35 no.6
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• pp.450-457
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• 2010

Although cherry tomato is one of major vegetables consumed in fresh vegetable market, the quality grading method is mostly dependant on size measurement using drum shape sorting machines. Using Visible/Near-infrared spectroscopy, apparatus to be able to acquire transmittance spectrum data was made and used to estimate firmness, sugar content, and acidity of cherry tomatoes grown at hydroponic and soil culture. Partial least square (PLS) models were performed to predict firmness, sugar content, and acidity for the acquired transmittance spectra. To enhance accuracy of the PLS models, several preprocessing methods were carried out, such as normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives, etc. The coefficient of determination ($R^2_p$) and standard error of prediction (SEP) for the prediction of firmness, sugar, and acidity of cherry tomatoes from green to red ripening stages were 0.859 and 1.899 kgf, with a preprocessing of normalization, 0.790 and $0.434^{\circ}Brix$ with a preprocessing of the 1st derivative of Savitzky Golay, and 0.518 and 0.229% with a preprocessing normalization, respectively.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.134.1-134.1
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• 2014

콜로이달 리소그래피는 나노미터 크기의 나노구를 자가조립에 의해 정렬시킴으로써, 파장이하 크기의 주기 구조를 저비용으로 쉽게 구현할 수 있는 패터닝 기법이다. 콜로이달 리소그래피나 소프트 리소그래피와 같이 대면적 패터닝이 가능한 공정을 태양전지를 위한 반사방지 및 광 포획 증대 구조에 적용함으로써, 기존 성능을 크게 향상시켰다. 본 연구에서는, 유한차분 시간영역 수치해석법을 이용하여 반사 방지 및 광 포획 증대 구조에 대한 이론적 검증 및 설계를 진행하였고, 콜로이달 리소그래피 및 반도체 공정을 통해 샘플을 제작하였으며, 제작된 샘플의 성능을 적분구를 겸비한 자외선 가시광 근적외선 영역 분광기를 통해 평가하였다. 반사방지 나노섬을 겸비한 나노 원뿔대 언덕형 굴절률 소자를 구현함으로써, 300나노미터 이하의 구조체를 사용하지 않고도 근자외선 영역을 포함하는 태양광 에너지의 손실을 최소화할 수 있는 광대역 방사방지 구조체를 제시하였다. 나노 원뿔대가 격자상수 이상의 파장에 대한 언덕형 굴절률을 제공하고, 4분의 1파장 나노섬 반사방지막이 격자 상수 이하의 근자외선 태양광을 추가적으로 흡수하여, 근자외선 영역에서의 평균 반사율을 3.8% 수준으로 달성 할 수 있었다. 또한, 낮은 양호계수를 갖는 속삭임 회랑 공진기 어레이를 이용하여, 박막 태양전지에 적합한 유전체 기반 광포획 증대 나노구조를 제시하였다. 나노반구, 나노고깔, 나노구, 함몰형 나노구 어레이 형태를 가지며, 500nm의 주기를 갖는 유전체 표면 텍스쳐드 구조를 초박형 비정질 실리콘 필름(100nm) 위에 제작하여 광대역 광 포획 증대 효과를 실험적으로 평가하였다. 구조들 중 함몰형 나노구 어레이가 결합된 비정질 실리콘 박막이 가장 높은 성능을 보였으며, 구조가 없는 경우 대비 약 67.6%의 가중 흡수율 증가를 나타내었다. 특히, 함몰형 나노구 어레이 구조 중 폴리메틸메타아크릴레이트로 제작된 평판형 함몰층은 나노구 비정질 박막 실리콘 사이의 접착력 및 기계적 강성을 향상시켰을 뿐 아니라, 함몰층 내부로 회절되고 산란된 빛들이 도파모드 효과에 의해 부가적인 광 포획 증대를 가져옴으로써, 가장 높은 광 포획 효과를 얻을 수 있었다. 유전체 기반 나노 구조들은 간단하고 저비용이며, 대면적으로 쉽게 제작할 수 있는 자가 조립 기반 콜로이달 리소그래피 및 소프트 리소그래피 기술을 이용하여 제작되었다.

• Economic and Environmental Geology
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• v.55 no.2
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• pp.197-207
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• 2022

This study investigated spectroscopic exploration of Myeonsan formation, the titanium(Ti) ore hostrock, in Joseon supergroup based on machine learning technique. The mineral composition, Ti concentration, spectral characteristics of Myeonsan and non-Myeonsan formation of Joseon supergroup were analyzed. The Myeonsan formation contains relatively larger quantity of opaque minerals along with quartz and clay minerals. The PXRF analysis revealed that the Ti concentration of Myeosan formation is at least 10 times larger than the other formations with bi-modal distribution. The bi-modal concentration is caused by high Ti concentrated sandy layer and relatively lower Ti concentrated muddy layer. The spectral characteristics of Myeonsan formation is manifested by Fe oxides at near infrared and clay minerals at shortwave infrared bands. The Ti exploration is expected to be more effective on detection of hostrock rather than Ti ore because ilmenite does not have characteristic spectral features. The random-forest machine learning classification detected the Myeonsan fomation at 85% accuracy with overall accuracy of 97%, where spectral features of iron oxides and clay minerals played an important role. It indicates that spectral analysis can detect the Ti host rock effectively, and can contribute for UAV based remote sensing for Ti exploration.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.345-355
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• 2007

PLS methods are suited for regressing q-variate Y variables on p-variate X variables even in the presence of multicollinearity problem among X variables. Consequently, they are useful for analyzing datasets with smaller number of observations compared to the number of variables, such as NIR(near-infrared) spectroscopy data in chemometrics. In this study, we propose two visualizing methods of p-variate X variables and q-variate Y variable that can be used in connection with PLS analysis.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.837-847
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• 2023

In this study, we explored a method for assessing the extent of damage caused by chemical substances at an accident site through the use of a vegetation index. Data collection involved the deployment of two different drone types, and the damaged area was determined using photogrammetry technology from the 3D point cloud data. To create a vegetation index image, we utilized spectral band data from a multi-spectral sensor to generate an orthoimage. Subsequently, we conducted statistical analyses of the accident site with respect to the damaged area using a predefined threshold value. The Kappa values for the vegetation index, based on the near-infrared band and the green band, were found to be 0.79 and 0.76, respectively. These results suggest that the vegetation index-based approach for analyzing damage areas can be effectively applied in investigations of chemical accidents.

• Journal of Animal Environmental Science
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• v.18 no.sup
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• pp.81-90
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• 2012

This study was evaluated high end research grade Near infrared spectrophotometer (NIRS) to low end popular field grade multiple Near infrared spectrophotometer (NIRS) for rapid analysis at forage quality at sight with 241 samples of Italian ryegrass silage during 3 years collected whole country for evaluate accuracy and precision between instruments. Firstly collected and build database high end research grade NIRS using with Unity Scientific Model 2500X (650 nm~2,500 nm) then trim and fit to low end popular field grade NIRS with Unity Scientific Model 1400 (1,400 nm~2,400 nm) then build and create calibration, transfer calibration with special transfer algorithm. The result between instruments was 0.000%~0.343% differences, rapidly analysis for chemical constituents, NDF, ADF, and crude protein, crude ash and fermentation parameter such as moisture, pH and lactic acid, finally forage quality parameter, TDN, DMI, RFV within 5 minutes at sight and the result equivalent with laboratory data. Nevertheless during 3 years collected samples for build calibration was organic samples that make differentiate by local or yearly bases etc. This strongly suggest population evaluation technique needed and constantly update calibration and maintenance calibration to proper handling database accumulation and spread out by knowledgable control laboratory analysis and reflect calibration update such as powerful control center needed for long lasting usage of forage analysis with NIRS at sight. Especially the agriculture products such as forage will continuously changes that made easily find out the changes and update routinely, if not near future NIRS was worthless due to those changes. Many research related NIRS was shortly study not long term study that made not well using NIRS, so the system needed check simple and instantly using with local language supported signal methods Global Distance (GD) and Neighbour Distance (ND) algorithm. Finally the multiple popular field grades instruments should be the same results not only between research grade instruments but also between multiple popular field grade instruments that needed easily transfer calibration and maintenance between instruments via internet networking techniques.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• Analytical Science and Technology
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• v.24 no.2
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• pp.85-93
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• 2011

To protect the steel structure in a high story buildings from fire, the sprayed fire-resistive materials are applied during the construction. Current standard methods to check the quality of sprayed fire-resistive materials are real fire test in lab, which take a long time (several weeks) and expensive. In this study, a simple analytical method to check the quality of sprayed fire-resistive materials is developed using Near Infrared Spectroscopy (NIR). Total 9 kinds of sprayed fire-resisted materials and 3 kinds of normal sprayed material sets were used for the analysis. Each set of materials was 50 to 100 samples. Samples are grinded and make a fine powder. The spectral data acquisition was carried out using FT-NIR spectrometer with a integrating sphere. NIR methods successfully identify the sprayed fire resistive materials by a principle component analysis (PCA) after a vector normalization (SNV) pretreatment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.325-333
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• 2006

Recently, in the case of observing the global environment, satellite remote sensing technology has been important. It's because satellite remote sensing is valuable for assessing relatively large areas. But now, small scale remote sensing techniques are needed which can be applicable to the detail investigation of plant tree areas which afforest land after the large scale construction of roads, dams and airports. In this study, we tried to develop and propose a lower altitude sensing technique which can be used in ground remote sensing by using a CCD camera. As a result of this investigation the following can be concluded: We recognized the transference characteristics of filters which were used in comparative tests about the four ground remote sensing devices. We also found that the near-IR camera could be used for an imaging spectral radiometer in the extraction of the vegetation index. Furthermore, we found that the vegetation index has varied hour by hour during the day of the experiment. Finally, we brought about an increase phase of the NDVI in a forest fire, which caused considerable damage, by developing new ground remote sensing technology.