• Journal of Astronomy and Space Sciences
• /
• 제34권4호
• /
• pp.315-330
• /
• 2017

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

• 대한환경공학회지
• /
• 제37권5호
• /
• pp.293-302
• /
• 2015

HNMs는 독성이 강한 소독부산물들 중의 하나로 최근에 다양한 연구가 진행되고 있다. 본 연구에서는 headspace-solid phase microextraction (SPME) 전처리장치와 GC-ECD를 이용하여 9종의 HNMs를 동시분석할 수 있는 분석법을 개발하였다. 9종의 HNMs에 대한 검출한계(LOD)는 90~260 ng/L였으며, 정량한계(LOQ)는 270~840 ng/L였다. 수돗물과 해수를 이용하여 시료수의 matrix 영향을 살펴본 결과, 9종의 HNMs에 대해 80%~127%의 양호한 회수율을 나타내어 시료수의 matrix에 영향을 받지 않았다. 또한, 본 연구에서 개발된 headspace SPME GC-ECD 분석법은 용매류를 이용한 별도의 전처리 과정이 필요하지 않아서 친환경적이며 간편하고 빠른 자동화된 방법으로 HNMs 분석에 적합한 것으로 나타났다.

• 대한공간정보학회지
• /
• 제10권3호
• /
• pp.41-51
• /
• 2002

최근 들어, 수치항공영상을 이용하여 지형정보를 추출하고자 하는 많은 연구가 수행되어 왔다. 그러나 수치항공영상에서 기존의 경계선 검출기법을 이용하여 대상물을 자동으로 인식하고 추출하는 것은 매우 어려우므로, 수동이나 반자동의 형태로 이루어졌다. 따라서, 본 연구에서는 먼저 도로 영역을 자동으로 추출하기 위해 밝기값이 분할된 항공영상의 의미론적인 정보의 대역을 중첩한 영상을 이용하여 인식에 장애가 되는 요소를 제거한 다음, 도로정보를 자동으로 인식하고 추출할 수 있는 알고리즘을 개발하여 시스템개발시 적용하고자 한다. 이를 위해 먼저, 횡단보도 대역 영상으로부터 횡단보도영역을 자동으로 인식하기 위한 '템플릿 회전이동 연산자'와 인식된 횡단보도의 장변의 길이를 바탕으로 도로영역을 추적할 수 있는 '윈도우 법선 탐색 추적 알고리즘'을 개발하므로써 항공영상으로부터 직접 도로정보를 자동으로 추출할 수 있는 기법을 제시하고자 한다.

• 대한인간공학회지
• /
• 제35권3호
• /
• pp.125-133
• /
• 2016

Objective: The aim of this study is to figure out the level of Functional Movement Screen (FMS) of 122 automobile manufacturing workers and to set the FMS score for predicting risk of musculoskeletal disorders. Background: Although today's industrial sites have been becoming automated rapidly, the risks of work-related musculoskeletal disorders (WMSDs) have been on the rise. In the case of WMSDs, it is important to control WMSDs at the early stage. Early detection of WMSDs is very important for the successful treatment. However, the medical examination puts a great financial burden on most workers. To reduce their burden, there is one test to check the musculoskeletal functional condition and to predict the risk of injury, which is called FMS. Method: This research tested the FMS score of 122 workers at a motor company, and also conducted a questionnaire survey of individual characteristics and job characteristics. Results: For the 122 subjects, the average score of FMS is $14.63{\pm}2.27$. There is a negative correlation between FMS and their ages and BMI (p <0.05). FMS is higher when exercising regularly (p <0.05). The FMS scores of musculoskeletal disorder patients are lower than those of normal workers (p <0.05). While it is more likely to become a musculoskeletal disorder patient when FMS score is less than 14, it is more likely to become a normal worker when FMS score is more than or equal to 14. Conclusion: According to the result of FMS test, there is a score difference between individuals with musculoskeletal disorders and normal ones. FMS scores can also predict and identify workers with risk of the musculoskeletal disorders. Application: According to this study, FMS can be expected to have a positive effect on the prevention of WMSDs in worksites.

• 한국전자거래학회지
• /
• 제22권2호
• /
• pp.1-18
• /
• 2017

오늘날 감정 표현이 서툰 영유아가 처한 극도의 스트레스 상태를 자동적으로 파악하는 것은 영유아의 안전을 위협하며 지속적으로 발생하는 위험 상황의 실시간적인 인지를 위해 반드시 필요한 기술이다. 따라서 본 논문에서는 생체신호를 활용하여 영유아의 스트레스 상태를 분류하기 위한 기계학습 기반의 모델과 생체신호 수집용 스마트 밴드 및 모니터링용 모바일 어플리케이션을 제안한다. 구체적으로 본 연구에서는 영유아의 감정을 나타내는 주요한 요인이 되는 음성 및 심박 데이터의 조합을 활용하여 기존에 널리 알려진 데이터 마이닝 기법을 통해 영유아의 스트레스 상태 패턴을 학습하고 예측한다. 본 연구를 통해 생체신호를 활용하여 영유아의 스트레스 상태 식별을 자동화할 수 있는 가능성을 확인하였으며 나아가서 궁극적으로 영유아의 위험 상황 예방에 활용될 수 있을 것으로 기대된다.

• 천문학회보
• /
• 제41권1호
• /
• pp.54.3-55
• /
• 2016

DEEP-South Scheduling and Data reduction System (DS SDS) consists of two separate software subsystems: Headquarters (HQ) at Korea Astronomy and Space Science Institute (KASI), and SDS Data Reduction (DR) at Korea Institute of Science and Technology Information (KISTI). HQ runs the DS Scheduling System (DSS), DS database (DB), and Control and Monitoring (C&M) designed to monitor and manage overall SDS actions. DR hosts the Moving Object Detection Program (MODP), Asteroid Spin Analysis Package (ASAP) and Data Reduction Control & Monitor (DRCM). MODP and ASAP conduct data analysis while DRCM checks if they are working properly. The functions of SDS is three-fold: (1) DSS plans schedules for three KMTNet stations, (2) DR performs data analysis, and (3) C&M checks whether DSS and DR function properly. DSS prepares a list of targets, aids users in deciding observation priority, calculates exposure time, schedules nightly runs, and archives data using Database Management System (DBMS). MODP is designed to discover moving objects on CCD images, while ASAP performs photometry and reconstructs their lightcurves. Based on ASAP lightcurve analysis and/or MODP astrometry, DSS schedules follow-up runs to be conducted with a part of, or three KMTNet telescopes.

• 정보보호학회논문지
• /
• 제30권1호
• /
• pp.59-74
• /
• 2020

웹 애플리케이션이 동작하는 웹 공간은 공개된 HTML로 인하여 공격자와 방어자의 사이버 정보전쟁터이다. 사이버 공격 공간에서 웹 애플리케이션과 소프트웨어 취약성을 이용한 공격이 전 세계적으로 약 84%이다. 웹 방화벽 등의 보안제품으로 웹 취약성 공격을 탐지하기가 매우 어렵고, 웹 애플리케이션과 소프트웨어의 보안 검증과 보증에 많은 인건비가 필요하다. 따라서 자동화된 소프트웨어에 의한 웹 스페이스에서의 신속한 취약성 탐지와 대응이 핵심적이고 효율적인 사이버 공격 방어 전략이다. 본 논문에서는 웹 애플리케이션과 소프트웨어에 대한 보안 위협을 집중적으로 분석하여 보안위험 관리 모델을 수립하고, 이를 기반으로 효과적인 웹 및 애플리케이션 취약성 진단 방안을 제시한다. 실제 상용 서비스에 적용한 결과를 분석하여 기존의 다른 방식들보다 더 효과적임을 증명한다.

• Archives of Pharmacal Research
• /
• 제24권3호
• /
• pp.207-210
• /
• 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.

• 천문학회지
• /
• 제51권5호
• /
• pp.129-142
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제22권10호
• /
• pp.1168-1177
• /
• 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.