• Journal of Ginseng Research
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• v.48 no.2
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• pp.140-148
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• 2024

Synthetic biology approaches offer potential for large-scale and sustainable production of natural products with bioactive potency, including ginsenosides, providing a means to produce novel compounds with enhanced therapeutic properties. Ginseng, known for its non-toxic and potent qualities in traditional medicine, has been used for various medical needs. Ginseng has shown promise for its antioxidant and neuroprotective properties, and it has been used as a potential agent to boost immunity against various infections when used together with other drugs and vaccines. Given the increasing demand for ginsenosides and the challenges associated with traditional extraction methods, synthetic biology holds promise in the development of therapeutics. In this review, we discuss recent developments in microorganism producer engineering and ginsenoside production in microorganisms using synthetic biology approaches.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.279-294
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• 2023

It is well known to generate synthetic data sets by the sequential regression multiple imputation (SRMI) method. The R-package synthpop are widely used for generating synthetic data by the SRMI approaches. In this paper, I suggest generating synthetic data based on the probabilistic principal component analysis (PPCA) method. Two simple data sets are used for a simulation study to compare the SRMI and PPCA approaches. Simulation results demonstrate that pairwise coefficients in synthetic data sets by PPCA can be closer to original ones than by SRMI. Furthermore, for the various data types that PPCA applications are well established, such as time series data, the PPCA approach can be extended to generate synthetic data sets.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1589-1605
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• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• Natural Product Sciences
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• v.16 no.2
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• pp.59-67
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• 2010

Flavonoids are well-known anti-inflammatory agents that exert their effects via a variety of mechanisms including antioxidative action, inhibition of eicosanoid metabolizing enzymes and regulation of theexpression of proinflammatory molecules. In this review, synthetic approaches to obtain more useful flavonoid derivatives are summarized. Human clinical trials of flavonoid therapy are discussed. Through continual investigation to identify more potent and comparable flavonoids, new anti-inflammatory flavonoid therapy will be successfully launched, especially for the treatment of chronic inflammatory disorders.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.141-166
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• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• Proceedings of the PSK Conference
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• 2002.04a
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• pp.49-49
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• 2002

• Proceedings of the Korean Society of Potoscience Conference
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• 2005.06a
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• pp.60-60
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• 2005

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• Journal of the Korean Applied Science and Technology
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• v.26 no.1
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• pp.60-68
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• 2009

In this work, the synthetic approaches for a series of aminopyrimiclinylmethanone derivatives 1a-i, which versatile biological activities such as antibacterial and anticancer activities are expected from a structural point of view, were described. Nicotinic acid was converted to (2-methylsulfonylpyrimidin-4-yl) (pyridin-3-yl)methanone, a key intermediate, which was reacted with nucleophiles to yield the desired aminopyrimidinylmethanone derivatives 1a-i bearing various substituents.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.