• Archives of Pharmacal Research
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• v.24 no.3
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• pp.207-210
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• 2001

A fully automated high performance liquid chromatography with column-switching was developed for the simultaneous determination of KR60436, a new reversible proton pump inhibitor, and its active metabolite O-Demethyl-KR60436 from rat plasma samples. Plasma sample (50$\mu$l) was directly introduced onto a Capcell Pak MF Ph-1 column ($10{\times}4$ mm I.D.) where primary separation was occurred to remove proteins and concentrate target Substances Using acetonitrile-Potassium Phosphate (PH 7, 0.1 M) (2 : 8, v/v). The drug molecules eluted from MF Ph-1 column were focused in an intermediate column ($10{\times}2$ I.D.) by the valve switching step. The substances enriched in intermediate column were eluted and separated on a Vydac 218MR53 column ($250{\times}3.2$ I.D.) using acetonitrilepotassium phosphate (pH 7, 0.02 M) (47:53, v/v) at a flow rate of 0.5 ml/min when the valve status was switched back to A position. The method showed excellent sensitivity (detection limit of 2 ng/ml) with small volume of samples ($50{\mu}$l), good precision and accuracy, and speed (total analysis time 24 min) without any loss in chromatographic efficiency. The response was linear ($r^2{\geq}0.797$) over the concentration range of 5-500 ng/ml.

• Journal of The Korean Astronomical Society
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• v.51 no.5
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• pp.129-142
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• 2018

The DEEP-South (the Deep Ecliptic Patrol of the Southern Sky) photometric census of small Solar System bodies produces massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques for faster access to a portion of the DEEP-South year-one data. Our new pipeline is designed to perform automated point source detection, robust high-precision photometry and calibration of non-crowded fields which have overlap with previously surveyed areas. In this paper, we show some examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discover 21 new periodic variables with period ranging between 0.1 and 31 days, including four eclipsing binary systems (detached, over-contact, and ellipsoidal variables), one white dwarf/M dwarf pair candidate, and rotating variable stars. We also recover astrometry (< ${\pm}1-2$ arcsec level accuracy) and photometry of two targeted near-earth asteroids, 2006 DZ169 and 1996 SK, along with the small- (~0.12 mag) and relatively large-amplitude (~0.5 mag) variations of their dominant rotational signals in R-band.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.295-301
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• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• Korean Journal of Ophthalmology
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• v.32 no.6
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• pp.459-469
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• 2018

Purpose: To evaluate changes in the peripapillary retinal nerve fiber layer (RNFL) thicknesses using spectral-domain optical coherence tomography (SD-OCT) in hydroxychloroquine (HCQ) users. Methods: The medical records of HCQ users were retrospectively reviewed. In these HCQ users, an automated perimetry, fundus autofluorescence photography, and SD-OCT with peripapillary RNFL thickness measurements were performed. The peripapillary RNFL thicknesses were compared between the HCQ users and the control groups. The relationships between the RNFL thicknesses and the duration or cumulative dosage of HCQ use were analyzed. Results: This study included 77 HCQ users and 20 normal controls. The mean duration of HCQ usage was $63.6{\pm}38.4$ months, and the cumulative dose of HCQ was $528.1{\pm}3.44g$. Six patients developed HCQ retinopathy. Global and six sectoral RNFL thicknesses of the HCQ users did not significantly decrease compared to those of the normal controls. No significant correlation was found between the RNFL thickness and the duration of use or cumulative dose. The eyes of those with HCQ retinopathy had temporal peripapillary RNFL thicknesses significantly greater than that of normal controls. Conclusions: The peripapillary RNFL thicknesses did not change in the HCQ users and did not correlate with the duration of HCQ use or cumulative doses of HCQ. RNFL thickness is not a useful biomarker for the early detection of HCQ retinal toxicity.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.587-604
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• 2021

Because the library cannot be run independently and used by many applications, it is important to detect vulnerabilities in the library. Fuzzing, which is a dynamic analysis, is used to discover vulnerabilities for the library. Although this fuzzing technique shows excellent results in terms of code coverage and unique crash counts, it is difficult to apply its effects to library fuzzing. In particular, a fuzzing executable and a seed corpus are needed that execute the library code by calling a specific function sequence and passing the input of the fuzzer to reproduce the various states of the library. Generating the fuzzing environment such as fuzzing executable and a seed corpus is challenging because it requires both understanding about the library and fuzzing knowledge. We propose a novel method to improve the ease of library fuzzing and enhance code coverage and crash detection performance by using a test framework. The systems's performance in this paper was applied to nine open-source libraries and was verified through comparison with previous studies.

• Journal of Korean Society of Disaster and Security
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• v.14 no.1
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• pp.41-50
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• 2021

As a method of predicting the displacement of river levee in advance, Differential Interferometry (D-InSAR) kind of InSAR techniques was used to identify weak points in the area of the levee collapes near Gumgok Bridge (Somjin River) in Namwon City, which occurred in the summer of 2020. As a result of analyzing the displacement using five images each in the spring and summer of 2020, the Variation Index (V) of area where the collapse occurred was larger than that of the other areas, so the prognostic sysmptoms was detected. If the levee monitoring system is realized by analyzing the correlations with displacement results and hydrometeorological factors, it will overcome the existing limitations of system and advance ultra-precise, automated river levee maintenance technology and improve national disaster management.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.310-316
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• 2020

Microarray technology represents a critical new advance in molecular cytogenetics. The development of this approach has provided fundamental insights into the molecular pathogenesis in clinical cytogenetics and has provided a clue to many unidentified or unexplained diseases. The approach allows a comprehensive investigation of thousands and millions of genomic loci simultaneously and enables the efficient detection of copy number alterations. The application of this technology has shown tremendous fluidity and complexity of the human genome, and has provided accurate diagnosis and appropriate clinical management in a timely and efficient manner for identifying genomic alterations. The clinical impact of the genomic alterations identified by microarrays is evolving into a diagnostic tool to identify high-risk patients better and predict patient outcomes from their genomic profiles. The transformation of conventional cytogenetics into an automated discipline will improve diagnostic yield significantly, leading to accurate diagnosis and genetic counseling. This article reviews cytogenetic technologies used to identify human chromosome alterations and highlights the potential utility of present and future genome microarray technology in the diagnosis.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.1
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• pp.7-14
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• 2021

In jewelry industry, the proportion of labor costs is high. Also, the production time and quality of products are highly varied depending on the workers' capabilities. Therefore, there is a demand from the jewelry industry for automation. The ear pin insertion automation system is the robot automatically inserts the ear pins into the silicone mold, and this automated system require accurate and fast hole detection method. In this paper, we propose optimal binarization method and a template matching method that can be applied in the ear pin insertion automation system. Through the performance test, it was shown that the applied method has an accuracy of 98.5% and 0.5 seconds faster processing speed than the Otsu binarization method. So, this automation system can contribute to cost reduction, work time reduction, and productivity improvement.