• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.271-276
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• 2006

In this paper a polarimetric airborne SAR measurement has been used to study the radar polarimetric characteristics in the intertidal area on the south coastof the Korea. The L-band NASA/JPL airborne SAR (AIRSAR) data, which were acquired on the intertidal zone during PACRIM-II Korea campaign on September 30, 2000, were used for this research. The most intertidal zones of Yeoja Bay are composed of muddy soils with high silt and clay percentage. Models of microwave scattering from rough surfaces, i.e., semi-empirical model, and Extended Bragg model, were applied to investigate the surface characteristics of intertidal zones.

• Journal of Pharmaceutical Investigation
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• v.32 no.2
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• pp.135-140
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• 2002

Cefadroxil is a semi-synthetic cephalosporin active against many Gram-positive and Gram-negative bacteria. The drug has been used for the treatment of the urinary and respiratory tract infections when caused by susceptible strains of the designated microorganism. The purpose of the present study was to evaluate the bioequivalence of two cefadroxil capsules, Duricef (Bo Ryung Pharmaceutical Co. Ltd.) and Hanacef (Korean Pharmaceutical Co. Ltd.), according to the guidelines of Korea Food and Drug Administration (KFDA). The cefadroxil release from the two cefadroxil capsules in vitro was tested using KP VII Apparatus II method with various different kinds of dissolution media (pH 1.2, 4.0, 6.8 buffer solution and water). Twenty four normal male volunteers, $21.58{\pm}2.43$ years in age and $70.74{\pm}10.29$ kg in body weight, were divided into two groups and a randomized $2{\times}2$ cross-over study was employed. After one capsule containing 500 mg as cefadroxil was orally administered, blood was taken at predetermined time intervals and the concentrations of cefadroxil in serum were determined using HPLC with UV detector. The dissolution profiles of two cefadroxil capsules were very similar at all dissolution media. The pharmacokinetic parameters such as $AUC_t,\;C_{max}\;and\;T_{max}$ were calculated and ANOVA test was utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t\;and\;C_{max}$ and untransformed $T_{max}$. The results showed that the differences in $AUC_t,\;C_{max}\;and\;T_{max}$ between two capsules based on the Duricef were 0.05%, -5.29% and 4.53%. There were no sequence effects between two capsules in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log(0.8) to log(1.25) $(e.g.,\;log(0.95){\sim}log(1.05)\;and\;log(0.87){\sim}log(1.02)$ for $AUC_t\;and\;C_{max}$, respectively). The 90% confidence interval using untransformed data was within ${pm}20%$ $(e.g.,\;-6.75{\sim}15.74\;for\;T_{max})$. All parameters met the criteria of KFDA guideline for bioequivalence, indicating that Hanacef capsule is bioequivalent to Duricef capsule.

• 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.