• Bulletin of the Korean Chemical Society
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• v.35 no.6
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• pp.1832-1836
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• 2014

A deep understanding of the metallic silver catalysts formation process on oxide support and the formation mechanism is of great scientific and practical meaning for exploring better catalyst preparing procedures. Herein the thermal decomposition process of supported silver catalyst with silver oxalate as the silver precursor in the presence of ethylenediamine and ethanolamine is carefully investigated by employing a variety of characterization techniques including thermal analysis, in situ diffuse reflectance infrared Fourier transform spectroscopy, scanning electron microscopy, and X-ray diffraction. The formation mechanism of supported silver particles was revealed. Results showed that formation of metallic silver begins at about $100^{\circ}C$ and activation process is essentially complete below $145^{\circ}C$. Formation of silver was accompanied by decomposition of oxalate group and removal of organic amines. Catalytic performance tests using the epoxidation of ethylene as a probe reaction showed that rapid activation (for 5 minutes) at a relatively low temperature ($170^{\circ}C$) afforded materials with optimum catalytic performance, since higher activation temperatures and/or longer activation times resulted in sintering of the silver particles.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.433-440
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• 2015

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

• Bulletin of the Korean Chemical Society
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• v.35 no.2
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• pp.441-447
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• 2014

$AgI/AgCl/H_2WO_4$ double heterojunctions photocatalyst was prepared via deposition-precipitation followed by ion exchange method. The structure, crystallinity, morphology, chemical content and other physical-chemical properties of the samples are characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive x-ray spectra (EDX), UV-vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL). The photocatalytic activity of the $AgI/AgCl/H_2WO_4$ was evaluated by degrading methyl orange (MO) under visible light irradiation (${\lambda}$ > 400 nm). The double heterojunctions photocatalyst displayed more efficient photocatalytic activity than pure AgI, AgCl, $H_2WO_4$ and AgCl/$H_2WO_4$. Based on the reactive species and energy band structure, the enhanced photocatalytic activity mechanism of $AgI/AgCl/H_2WO_4$ was discussed in detail. The improved photocatalytic performance of $AgI/AgCl/H_2WO_4$ double heterojunctions could be ascribed to the enhanced interfacial charge transfer and the inhibited recombination of electron-hole pairs, which was in close relation with the $AgI/AgCl/H_2WO_4$ heterojunctions formed between AgI, AgCl and $H_2WO_4$.

• KIEAE Journal
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• v.12 no.6
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• pp.107-112
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• 2012

The purpose of this study is to determine the optimum slat angle of the venetian blind which was applied at an outer skin of a curtain-wall system. The evaluation of the blind slat angle was performed in terms of the comfortable visual environment and decreased energy consumption. The office building prototype was considered for the analysis and simulation variables include application of blind, blind slat angle and dimming control of lighting. The annual energy consumption and incidence rate of discomfort glare were analyzed using EnergyPlus which is developed by the U. S. Department of Energy for the detailed building energy simulation. As a result, it turns out that when the blind (reflectance: 0.5) was installed, the annual energy consumption was greater than that of the base model. However, when the dimming control was applied, the maximum energy saving of 16.3% could be achieved at a slat angle of $0^{\circ}$. In addition, in case of the base model, the incidence rate of discomfort glare was 84%, while the case of the blind with the slat angle of $0^{\circ}$ showed that the incidence rate of discomfort glare was 42.4%. Consequently, the results showed that the slat angle of $55^{\circ}$ with dimming control was the optimum strategy for the comfortable visual environment and decreased energy consumption.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.4
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• pp.515-523
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• 2012

The objective of this study was to investigate the variability in broiler performance, apparent metabolisable energy (AME) and ileal digestible energy (IDE) between five different maize samples fed with and without xylanase at 16,000 U/kg. Various in vitro characterisations were conducted to determine if any could predict performance or AME. Samples of the maize were harvested in five diverse regions and fed individually in a mash diet as follows (g/kg): test maize 608.3; soya bean meal (SBM) 324.1; poultry fat 25.2; salt 4.6; met 2.6; lys 1.6; thr 0.5; limestone 9.7, dical 18.4; vit/min 5.0; CP 210 and ME (kcal/kg) 3,085. The diets were fed to 720 broilers with 6 replicates, each containing 12 birds per treatment, from 0 to 18 d of age. Maize samples were analysed for starch, protein, crude fibre, fat, protein solubility index (PSI) and vitreousness using near infra red reflectance spectroscopy (NIR). They were also assayed using an in vitro starch digestibility method. The results showed that there was no effect of harvest region on the feed intake (FI), body weight gain (BWG) or feed conversion ratio (FCR) of the broilers over the 18 d period (p = 0.959, 0.926, 0.819 respectively). There was an improvement in all parameters with the addition of xylanase (FI p = 0.011; BWG and FCR p<0.001). There was a significant positive effect of xylanase on IDE, AME, IDE Intake (IDEI) and AME intake (AMEI) (p<0.0001 in all cases). Although there was no significant effect of maize source, there was a strong trend towards variability in IDE (p = 0.066) and AME (p = 0.058). There were no significant correlations (p<0.05) between any proximate or physiochemical values and any performance or AME values. This may suggest that none of those selected were suitable predictors for performance or AME. The broilers performed well according to the breed guidelines, with slightly increased FI, increased BWG and similar FCR prior to the addition of xylanase. When FCR and BWG were analysed with FI as a covariate, xylanase addition remained significant suggesting that the improvement in BWG and FCR was driven by an increase in digestibility and nutrient availability.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.609-625
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• 2022

Validation of satellite data products is critical for subsequent analysis that is based on the data. Particularly, performance of ocean color products in turbid and shallow near-land ocean areas has been questioned for long time for its difficulty that stems from the complex optical environment with varying distribution of water constituents. Furthermore, validation with ship-based or station-based measurements has also exhibited clear limitation in its spatial scale that is not compatible with that of satellite data. This study firstly performed validation of major GOCI-II products such as remote sensing reflectance, chlorophyll-a concentration, suspended particulate matter, and colored dissolved organic matter, using the in-situ measurements collected from ship-based field campaign. Secondly, this study also presents preliminary analysis on the use of drone images for product validation. Multispectral images were acquired from a MicaSense RedEdge camera onboard a UAV to compensate for the significant scale difference between the ship-based measurements and the satellite data. Variation of water radiance in terms of camera altitude was analyzed for future application of drone images for validation. Validation conducted with a limited number of samples showed that GOCI-II remote sensing reflectance at 555 nm is overestimated more than 30%, and chlorophyll-a and colored dissolved organic matter products exhibited little correlation with in-situ measurements. Suspended particulate matter showed moderate correlation with in-situ measurements (R²~0.6), with approximately 20% uncertainty.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1032-1032
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• 2001

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.