• Journal of Information Display
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• v.13 no.4
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• pp.151-157
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• 2012

A simple method of fabricating out-coupling structures was demonstrated via solution-processing to enhance light extraction from organic light-emitting diodes (OLEDs). Scattering layers were easily obtained by spin-coating an $SiO_2$ sol solution that contained $TiO_2$ particles. By introducing the scattering layer and the solution-processible corrugated structure as internal and external extraction layers, the OLEDs showed increased external quantum efficiency without a change in the electroluminescence spectrum compared to conventional devices. Using these solution-processible out-coupling structures, nearly all-solution-processed OLEDs with enhanced light extraction could be fabricated. The light extraction enhancement is attributed to the suppression by the out-coupling structures of the light-trapping that arose at the interface of the glass substrate and the air.

• Journal of Microbiology and Biotechnology
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• v.11 no.5
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• pp.887-889
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• 2001

The gene encoding yeast pro-carboxypeptidase Y (pro-CPY) has been cloned and expressed in Escherichia coli. Most of the expressed pro-CPY was accumulated as cytoplasmic insoluble aggregates. In our previous study, active CPY was obtained by renaturation of entirely denatured pro-CPY followed by in vitro proteolytic processing with proteinase K along with the activation process. The same refolding process was performed to produce an active CPY from pro-CPY inclusion bodies with renaturation buffers containing proteinase K at different concentrations. The refolding efficiency decreased from $25\%\;to\;2\%$ in the renaturation buffers containing proteinase K at concentrations of $60{\mu}g/ml\;and\;0.6{\mu}g/mi$, respectively. In an attempt to increase the refolding efficiency with a lesser amount of proteinase K, a novel fed-batch refolding process was developed. In a fed-batch refolding, 99 ml of the renaturation buffer containing pro-CPY was gradually added into 1 ml of the renaturation buffer containing $60{\mu}g/ml$ of proteinase K to give a final proteinase K concentration of $0.6{\mu}g/ml$. The fed-batch refolding process resulted in a refolding efficiency of $18\%$, which corresponded to a 9-fold increase over that ($2\%$) in the batch process.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.383-390
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• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.18 no.8
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• pp.78-89
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• 2000

Today's computers are built up from a minimal set of standard pattern recognition operations. Logic gates, such as NAND, are common examples. Biomolecular materials offer an alternative approach, both in terms of variety and context sensitivity. Enzymes, the basic switching elements in biological cells, are notable for their ability to discriminate specific molecules in a complex background and to do so in a manner that is sensitive to particular milieu features and indifferent to others, The enzyme, in effect, is a powerful context sensitivity pattern processor that in a rough way can be analogized to a neuron whose input-output behavior is controlled by enzymatic dynamics.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.292-294
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• 2023

본 논문에서는 국내 사망 원인 1위 질환인 암 중 가장 큰 비중을 차지하는 폐암의 암 오진율 감소 및 정밀 진단을 위해 폐암세포를 검출 및 계수 할 수 있는 시스템을 구현하였다. 사용자가 관심 영역을 지정하면 H&E 염색 방식을 사용한 폐암세포 전처리 과정을 거쳐 검출 및 계수 할 수 있다. 본 시스템을 통해 병리학자가 단 시간에 폐암세포 검출 및 계수하여 객관적 진단 도구로 활용할 수 있으며, 디지털 기술과 융합하여 정밀 의료에 크게 기여할 수 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.447-449
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• 2024

본 논문에서 다양한 환경적 요인에서 촬영한 이미지 데이터를 활용하여 신호등 위치의 정확한 탐지 및 신호등의 색상 인식을 통해 교통 신호를 판별하는데 사용되는 컴퓨터 비전 기반의 신호등 인식 시스템 알고리즘을 제안하였다. 이를 통해 기존에 신호를 인식하던 LiDAR 및 RADAR 센서를 대신해 카메라를 사용함으로써 자율주행 차의 제작비용 감소를 기대할 수 있다. 또한 다양한 환경의 이미지 데이터를 통해 실험을 진행하였고 이러한 실험결과를 분석하고 적용함으로써 악천후에서의 효과적인 신호등 인식 시스템을 구축하는데 기여하고자 한다.

• Journal of Ceramic Processing Research
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• v.21 no.4
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• pp.479-487
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• 2020

Ceramic ZnS nanocomposites were prepared by mechanical processing and one-step heat sintering with powder mixtures of fly ash, waste glass, and ZnS (template-free hydrothermal method manufacturing). Chemical durability and morphological characteristics of heat-treated samples at 800 ℃ with/without acid treatment were evaluated. The photocatalytic activities were estimated with methyl orange (MO), methylene blue (MB), acetaldehyde (ATA), and 2,4-dichlorophenoxyacetic acid (2,4-D) as photodegradation targets. Crystallization behaviors of the prepared ceramic ZnS nanocomposites were investigated using X-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectrometry (EDS). In addition, compressive and bending strength as mechanical properties were evaluated. Ceramic ZnS nanocomposites were found to showed improvement in optimal photocatalytic reaction and physical properties regardless of acid treatment when the amount of ZnS nanoparticles was increased from 7.0 to 25.0 wt%. Degrees of photocatalytic decomposition of MO, ATA, 2,4-D, and MB by acid treated ceramic ZnS nanocomposites containing 25 wt% ZnS were about 0.185, 0.121, 0.216, 0.236, respectively, after UV irradiation for 180 min.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.505-516
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• 2023

Plastic's ease of processing drives its growing production, resulting in a surge of plastic waste. Addressing this issue, catalytic upcycling emerges as a promising remedy. Various metals (Ru, Pt, etc.) and supports (TiO₂, CeO₂, etc.) have been employed for the chemical recycling of polyolefin plastics. Strategies to enhance liquid fuel selectivity and minimize methane include manipulating particle size, introducing heterogeneous metals, and tuning support characteristics. Simultaneously, endeavors to optimize catalysts by reducing precious metal usage were pursued. This study explores enhancing economic viability in hydrogenolysis and hydrocracking reactions, underscoring the potential of catalystdriven upcycling to tackle plastic waste.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.375-382
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• 2008

A protein's subcellular localization is considered an essential part of the description of its associated biomolecular phenomena. As the volume of biomolecular reports has increased, there has been a great deal of research on text mining to detect protein subcellular localization information in documents. It has been argued that linguistic information, especially syntactic information, is useful for identifying the subcellular localizations of proteins of interest. However, previous systems for detecting protein subcellular localization information used only shallow syntactic parsers, and showed poor performance. Thus, there remains a need to use a full syntactic parser and to apply deep linguistic knowledge to the analysis of text for protein subcellular localization information. In addition, we have attempted to use semantic information from the WordNet thesaurus. To improve performance in detecting protein subcellular localization information, this paper proposes a three-step method based on a full syntactic dependency parser and WordNet thesaurus. In the first step, we constructed syntactic dependency paths from each protein to its location candidate, and then converted the syntactic dependency paths into dependency trees. In the second step, we retrieved root information of the syntactic dependency trees. In the final step, we extracted syn-semantic patterns of protein subtrees and location subtrees. From the root and subtree nodes, we extracted syntactic category and syntactic direction as syntactic information, and synset offset of the WordNet thesaurus as semantic information. According to the root information and syn-semantic patterns of subtrees from the training data, we extracted (protein, localization) pairs from the test sentences. Even with no biomolecular knowledge, our method showed reasonable performance in experimental results using Medline abstract data. Our proposed method gave an F-measure of 74.53% for training data and 58.90% for test data, significantly outperforming previous methods, by 12-25%.