• Journal of Pharmaceutical Investigation
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• 제37권4호
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• pp.229-235
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• 2007

Multi-layered granules were prepared by a fluidized-bed coater and uniformed granules were obtained with a size range between $950{\sim}1000{\mu}m$ in diameter. The granule system was composed of three layers, i.e. seed layer with sugar sphere bead and a water-swellable polymer, middle layer with a drug, solubilizer and polymer, and the top layer of porous membrane with a polymeric binder. The aim of this work is to find out the dependence of a drug dissolution rate on the amount of a water-soluble binder and a solubilizer in the granule system. The results showed that the higher amount of hydrophilic binder in the porous membrane, gave the bigger pore size and porosity and made faster dissolution rate and also the higher amount of solubilizer in drug layer enhanced the dissolution rate of drug.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2007년도 7th International Meeting on Information Display 제7권1호
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• pp.190-192
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• 2007

A neutral beam system has been developed to produce hyperthermal neutral beams composed of indium, tin, and oxygen atoms. Using these hyper thermal neutral beams with energies in the range of tens of eV, high quality indium-tin oxide (ITO) thin films have been obtained on glass substrates at room temperature. The optical transmittance of the films is higher than 85% at a wavelength of 550 nm and the electrical resistivity is lower than $1{\times}10^{-3}{\Omega}cm$.

• Bulletin of the Korean Chemical Society
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• 제33권2호
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• pp.675-677
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• 2012

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2018년도 정기학술대회 발표논문집
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• pp.249-249
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• 2018

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2019년도 정기학술대회 발표논문집
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• pp.210-210
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• 2019

• 한국진공학회:학술대회논문집
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• 한국진공학회 2016년도 제50회 동계 정기학술대회 초록집
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• pp.175.1-175.1
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• 2016

In recent, tract infections such as atopic dermatitis, allergic rhinitis and a respiratory disease are increasing, giving rise to the atmospheric pollution, inflow of micro-size dust and side effect of humidifier disinfectant. In this context, the environment-friendly technology is required to eliminate airborne pathogens. We propose solution of the previous problems, making use of Radical Mist Generator (RMG). Existing technologies of air purification using a gas discharge produce harmful substances such as ozone, NOx, etc. However, the RMG uses a pure water as a plasma forming material. The RMG sprays the water mist, which contains reactive radicals to sterilize microorganisms. RMG is comprised of a power supply, plasma electrodes and a nozzle. In order to analyze the electrical characteristic and concentrations of reactive radicals, we employ an oscilloscope and a titration method. To test the sterilization effect of RMG, we used E.coli. We confirmed that E.coli was killed over 90%. Eventually, we expect that RMG can be promising tool for a purified system.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2020년도 정기학술대회 발표논문집
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• pp.213-213
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• 2020

• Journal of Pharmaceutical Investigation
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• 제38권1호
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• pp.45-50
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• 2008

5-Fluorouracil (5-FU) is an antimetabolic of the pyrimidine derivatives that is used in chemotherapy for the treatment of several types of cancer. 5-FU have poor oral absorption and short biological half-time and strong side effects. Microneedle introduced to find a solution of problems. Microneedle device with roll was manufactured for transdermal delivery of various drugs. 5-FU was mixed in non-ionic surfactant such as tween 20 and tween 80. Camscope was used to analysis the permeation magnitude of treated skin by microneedle and trypan blue staining. The 5-FU solution with surfactant measured by ZETA-potential analysis system for stability of solution. The skin permeation rate of 5-FU determined by HPLC. We confirmed that cross treated skin was dyed more deeply than parallel treated skin through trypan blue staining. The results indicate that skin permeation rate of 5-FU was increased with the treatment types and treatment times.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.