• Title/Summary/Keyword: SPPS

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A novel framework for correcting satellite-based precipitation products in Mekong river basin with discontinuous observed data

  • Xuan-Hien Le;Giang V. Nguyen;Sungho Jung;Giha Lee
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.173-173
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    • 2023
  • The Mekong River Basin (MRB) is a crucial watershed in Asia, impacting over 60 million people across six developing nations. Accurate satellite-based precipitation products (SPPs) are essential for effective hydrological and watershed management in this region. However, the performance of SPPs has been varied and limited. The APHRODITE product, a unique gauge-based dataset for MRB, is widely used but is only available until 2015. In this study, we present a novel framework for correcting SPPs in the MRB by employing a deep learning approach that combines convolutional neural networks and encoder-decoder architecture to address pixel-by-pixel bias and enhance accuracy. The DLF was applied to four widely used SPPs (TRMM, CMORPH, CHIRPS, and PERSIANN-CDR) in MRB. For the original SPPs, the TRMM product outperformed the other SPPs. Results revealed that the DLF effectively bridged the spatial-temporal gap between the SPPs and the gauge-based dataset (APHRODITE). Among the four corrected products, ADJ-TRMM demonstrated the best performance, followed by ADJ-CDR, ADJ-CHIRPS, and ADJ-CMORPH. The DLF offered a robust and adaptable solution for bias correction in the MRB and beyond, capable of detecting intricate patterns and learning from data to make appropriate adjustments. With the discontinuation of the APHRODITE product, DLF represents a promising solution for generating a more current and reliable dataset for MRB research. This research showcased the potential of deep learning-based methods for improving the accuracy of SPPs, particularly in regions like the MRB, where gauge-based datasets are limited or discontinued.

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Synthesis of Sulfonated Poly(phenylene sulfide) via Soluble Precursor and its Ammonia Gas Adsorption (용해성 전구체를 통한 Sulfonated Poly(phenylene sulfide)의 합성과 암모니아가스 흡착)

  • Son, Won Keun;Kim, Hyun Suk;Park, Soo Gil
    • Applied Chemistry for Engineering
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    • v.10 no.5
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    • pp.666-671
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    • 1999
  • In this work, sulfonated poly(phenylene sulfide) (SPPS) was prepared by demethylation with aqueous NaOH solution after poly[methyl[4-(phenylthio)phenyl]sulfonium trifluoromethanesulfonate](PPST) was sulfonated with fumic sulfonic acid(10% $SO_{3}-H_{2}SO_{4}$). PPST soluble in organic solvents was synthesiszed by self-condensation polymerization of methyl-(phenylthio)phenyl sulfoxide(MPPSO). SPPS showed IR bands of asymmetric O=S=O stretching at $1200cm^{-1}$ and S-O stretching at $621cm^{-1}$ from $-SO_{3}H$ group. From the result, it could be known that sulfonic acid groups were introduced to poly(phenylene sulfide). when PPST was sulfonated for 12hr at $150^{\circ}C$, 1.48 sulfonic acid groups were introduced per repeat unit. The weight average molecular weight(Mw) of PPST and SPPS determined by high temperature GPC were 118323 and 131204, respectively. The SPPS exhibited adsorption capacity of ammonia gas $9.67mmol\;NH_{3}/g$ and it was much higher than that of active carbon or silica gel.

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Ultra Broadband Absorption of SPPs Enhanced Dual Grating Thin Film CIGS Solar Cell Enabled by Particle Swarm Optimization

  • Le, DuyKhanh;Tran, QuyetThang;Lee, Sangjun;Kim, Sangin
    • Journal of the Optical Society of Korea
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    • v.18 no.5
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    • pp.429-435
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    • 2014
  • We examined the effective utilization of Particle Swarm Optimization (PSO) to enhance the light absorption performance in thin CuIn1-xGaxSe2 (CIGS) solar cells with dual (top and bottom) gratings. The PSO tuned structure was demonstrated to be capable of achieving high and ultra broadband absorption spectra due to well-spaced and well-defined absorption peaks, which were SPPs and photonic modes induced by the metal and dielectric gratings. For only TM polarization and both polarizations, the fully optimized net absorptions exhibit 85.6% and 78.1%, which correspond to ~35.4% and ~23.5% improvement compared to optimized flat structures, respectively.

Development of Sweet Potato Shaped Rice Madeira Cakes using Sweet Potato Paste with Different Cultivars (품종별 고구마 페이스트를 이용한 고구마형 쌀구움과자 개발)

  • Yoon, Huina;Jeong, Onbit;No, Junhee;Kim, Wook;Shin, Malshick
    • Korean journal of food and cookery science
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    • v.33 no.1
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    • pp.78-86
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    • 2017
  • Purpose: To increase the availability of Korean sweet potato (SP), the quality characteristics of the sweet potato pastes (SPPs) and rice madeira cake (RMC) using them were investigated. Methods: Ten different SPPs, orange fleshed Sinwhangmi, Juwhangmi, purple fleshed Sinjami and Yeonjami, Cream fleshed Sinyulmi, Sinchunmi, and newly developed Geonwhangmi, Dahomi, Daeyumi, and Pongwonmi were used. Their pastes were prepared by washed, peeled, steamed, crushed, vacuum packed and stored in a freezer until use. Results: The SPPs and RMC with them were significant difference from different cultivars with color value, rheology and texture properties, and preference test. The SPP showed the highest lightness value in Sinchunmi (55.89) and the highest viscosity in Geonhwangmi (55.33 poise). The RMCs with SPPs had lower values in hardness and chewiness than the RMC without SPP. Overall quality of preference test showed the highest values in RMC with Sinyulmi and Sincheonmi. Conclusion: The best quality of sweet potato shaped rice madeira cake was made using Sinyulmi and Sinchunmi pastes. It is suggested that sweet potato paste is possible to use as the biomaterials for application of processed foods.

Synthesis and Exchange Properties of Sulfonated Poly(phenylene sulfide) with Alkali Metal Ions in Organic Solvents

  • Son, Won Geun;Kim, Sang Heon;Park, Su Gil
    • Bulletin of the Korean Chemical Society
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    • v.22 no.1
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    • pp.53-58
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    • 2001
  • Sulfonated poly(phenylene sulfide) (SPPS) polymers were prepared by sulfonation of poly[methyl[4-(phenylthio) phenyl]sulfonium trifluoromethanesulfonate] (PPST) with fumic sulfonic acid (10% $SO_3-H_2SO_4$) and demethylation with aqueous NaOH solution. The equilibrium constants of ion exchange reactions between alkali metal cations ($Li^+,\;Na^+,\;and\;K^+$) and SPPS ion exchanger in organic solvents such as tetrahydrofuran (THF) and dioxane were measured. The equilibrium constants of ion exchange reactions increased as the polarity of the solvent increased, and the reaction temperature decreased. The equilibrium constants of the ion exchange reaction ($K_{eq}$) also increased in the order of $Li^+,\;Na^+,\;and\;K^+$. To elucidate the spontaneity of the exchange reaction in organic solvents, the enthalpy, entropy, and Gibbs free energy were calculated. The enthalpy of reaction ranged from -0.88 to -1.33 kcal/mol, entropy ranged from 1.42 to 4.41 cal/Kmol, and Gibbs free energy ranged from -1.03 to -2.55 kcal/mol. Therefore, the exchange reactions were spontaneous because the Gibbs free energies were negative. The SPPS ion exchanger and alkali metal ion bounding each other produced good ion exchange capability in organic solvents.

Application of Convolutional Neural Networks (CNN) for Bias Correction of Satellite Precipitation Products (SPPs) in the Amazon River Basin

  • Alena Gonzalez Bevacqua;Xuan-Hien Le;Giha Lee
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.159-159
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    • 2023
  • The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

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Application of machine learning for merging multiple satellite precipitation products

  • Van, Giang Nguyen;Jung, Sungho;Lee, Giha
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.134-134
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    • 2021
  • Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

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Analysis of bias correction performance of satellite-derived precipitation products by deep learning model

  • Le, Xuan-Hien;Nguyen, Giang V.;Jung, Sungho;Lee, Giha
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.148-148
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    • 2022
  • Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

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Biological Activity of Multifunctional Oligopeptide Derivatives

  • Kim, Bo Mi
    • Journal of Integrative Natural Science
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    • v.9 no.2
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    • pp.86-93
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    • 2016
  • The peptide sequences, GHK(Gly-His-Lys) and KTTKS(Lys-Thr-Thr-Lys-Ser), using a collagen stimulator recently were manipulated at N-terminal as a multifunctional peptide derivative with PEG(polyethyleneglycol) linker connected to gallic acid which presents anti-inflammatory activity. The multifunctional peptide derivatives were obtained in a normal peptide preparation method through SPPS(solid phase peptide synthesis) using Fmoc chemistry and a carboxyl group insertion reaction of PEG-3,4,5-triacetoxy benzoate by using potassium tert-butoxide and ethyl bromoacetate, which was separated by Sephadex DEAE. It gave a good compromise to a cosmetic application for cell cytotoxicity, anti-wrinkle, and anti-inflammation.

10 Gbps Optical Signal Transmission via Long-Range Surface Plasmon Polariton Waveguide

  • Ju, Jung-Jin;Kim, Min-Su;Park, Sun-Tak;Kim, Jin-Tae;Park, Seung-Koo;Lee, Myung-Hyun
    • ETRI Journal
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    • v.29 no.6
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    • pp.808-810
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    • 2007
  • We demonstrate 10 Gbps optical signal transmission via long-range surface plasmon polaritons (LR-SPPs) in a very thin metal strip-guided geometry. The LR-SPP waveguide was fabricated as a 14 nm thick, 2.5 ${\mu}m$ wide, and 4 cm long gold strip embedded in a polymer and pigtailed with single-mode fibers. The total insertion loss of 16 dB was achieved at a wavelength of 1.55 ${\mu}m$ as a carrier wave. In a 10 Gbps optical signal transmission experiment, the LR-SPP waveguide exhibits an excellent eye opening and a 2.2 dB power penalty at $10^{-12}$ bit error rate. We confirm, for the first time, that LR-SPPs can efficiently transfer data signals as well as the carrier light.

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