• 천문학회보
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• 제41권1호
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• pp.73.2-73.2
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• 2016

We present the results of KaVA SgrA* observation together with Takahagi(32m), Yamaguchi(32m) and Nobeyama(45m) telescopes at 22 and 43GHz, respectively. In early 2014, G2 cloud was expected to encounter with SgrA* and to make a significant flux variation, but it has not been measured yet. So it's worth to check our amplitude calibration method to confirm if we have a missing flux caused by uncertainty in measuring it. We have tested both a standard method using system noise temperature(Tsys) with antenna gain information, and a template method in order to calibrate antenna gain using nearby maser source. As a result, we found that the latter method is useful for antennas which have inaccurate gain information or poor Tsys measurements, and is especially effective for sources at low elevation like SgrA*. In addition, the comparison shows that the amplitude calibration by standard method can be improved up to 10% with a correction factor using a template method. This result implies we can get more accurate flux from a standard method when any maser source not exists around target.

• 대한화학회지
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• 제57권3호
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• pp.377-381
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• 2013

Thevetia peruviana (Yellow Oleander) seed oil was extracted with n-hexane in a soxhlet extractor. The ethanolysis and methanolysis of the oil were carried out with 50% of potassium hydroxide in ethanol and methanol respectively by weight of oil, as catalyst. The biodiesel was tested for biodegradability using E. coli. The percentage yield of the FAEE and FAME were 84.8% and 91.6% respectively. The biodegradability values of 81.4% and 86.2% were obtained for FAEE and FAME respectively after a period of 28 days. Other fuel quality parameters determined are the cetane index of 47.19 (FAEE) and 58.97 (FAME), flash point of $198^{\circ}C$ (FAEE) and $175^{\circ}C$ (FAME), kinematic viscosity at $40^{\circ}C$ of 5.21 $mm^2s^{-1}$ (FAEE) and 5.10 $mm^2s^{-1}$(FAME), pour point of $4^{\circ}C$ (FAEE) and $-2^{\circ}C$ (FAME) and a cloud point of $6^{\circ}C$ (FAEE) and $3^{\circ}C$ (FAME). Thus, Thevetia peruviana oil has a high potential for use in production of environmentally friendly biodiesel.

• 천문학회보
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• 제36권1호
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• pp.38.2-38.2
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• 2011

In general, solar filaments are divided into two parts; one spine and several barbs. Barbs are seen as if they protrudes from the spine. Until now there are many controversies about the structures of a barb and spine. Recently, New Solar Telescope was installed at Big Bear Solar Observatory. Its clear aperture is about 1.6m and it is the largest telescope among ground-based solar telescopes. Fast Imaging Solar Spectrograph (FISS) developed by SNU and KASI was also installed in a vertical optical table in Coude room of the 1.6m NST. It is simultaneously able to record two lines; $H{\alpha}$ and Ca II 8542A lines. On 2010 July 29, we observed a portion of a solar filament located in northern hemisphere with FISS and it had a well-developed barb. And we also observed a potion of a spine. In order to analyze the data, we used the cloud model and obtained physical quantities of the solar filament. Temperature of the solar lament ranged between 4500K and 12000K and non-thermal velocity ranged between 3km/s and 6.5km/s. By comparing physical quantities of a barb and spine, we try to understand these structures of the solar filament.

• 천문학회보
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• 제36권1호
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• pp.87.2-87.2
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• 2011

We report our 110 ks Chandra observations of the supernova remnant (SNR) 0104-72.3 in the Small Magellanic Cloud (SMC). The X-ray morphology shows two prominent lobes along the northwest-southeast direction and a soft faint arc in the east. Previous low resolution X-ray images attributed the unresolved emission from the southeastern lobe to a Be/X-ray star. Our high resolution Chandra data clearly shows that this emission is diffuse, shock-heated plasma, with negligible X-ray emission from the Be star. The eastern arc is positionally coincident with a filament seen in optical and infrared observations. Its X-ray spectrum is well fit by plasma of normal SMC abundances, suggesting that it is from shocked ambient gas. The X-ray spectra of the lobes show overabundant Fe, which is interpreted as emission from the reverse-shocked Fe-rich ejecta. The overall spectral characteristics of the lobes and the arc are similar to those of Type Ia SNRs, and we propose that SNR 0104-72.3 is the first case for a robust candidate Type Ia SNR in the SMC. On the other hand, the remnant appears to be interacting with dense clouds toward the east and to be associated with a nearby star-forming region. These features are unusual for a standard Type Ia SNR. Our results suggest an intriguing possibility that the progenitor of SNR 0104-72.3 might have been a white dwarf of a relatively young population.

• 사물인터넷융복합논문지
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• 제1권1호
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• pp.21-25
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• 2015

사물인터넷(IoT)는 지능형 스마트 홈 네트워크를 구축하는데 매우 중요한 핵심기술이며 유비쿼터스 환경에서 스마트폰, 클라우드 컴퓨팅 등과 연계되어 중요한 역할을 담당할 것으로 전망된다. 본 논문에서는 인간의 자율 신경계에 의한 내부 조절 시스템을 연구하여 그의 핵심 메커니즘을 지능형 스마트 홈 네트워크를 위한 IoT 기반 자동조절 시스템을 설계하는데 응용하였다. 구성된 기기들이 연결되어 상호작용함으로써 온도, 습도, 조도가 자율적으로 조절되는 메커니즘을 제안하고 이를 적용하여 조절과정을 시뮬레이션 하였다.

• Journal of Microbiology and Biotechnology
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• 제24권3호
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• pp.334-336
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• 2014

A new species named Aspergillus cumulatus sp. nov. is described in Aspergillus section Aspergillus (Eurotium state). The type strain (KACC $47316^T$) of this species was isolated from rice straw used in meju fermentations in Korea, and other strains were isolated from the air in a meju fermentation room. The species is characterized by growth at a wide range of water activities and the formation of aerial hyphae on malt extract 60% sucrose agar (ME60S) that resemble a cumulus cloud. Furthermore, A. cumulatus produces yellow ascomata containing small lenticular ascospores (5.1-5.7 ${\mu}m$) with a wide furrow, low equatorial crests, and tuberculate convex surface. The species is phylogenetically distinct from the other reported Aspergillus section Aspergillus species based on multilocus sequence typing using rDNA-ITS, ${\beta}$-tubulin, calmodulin, and RNA polymerase II genes.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• Journal of Information Processing Systems
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• 제5권3호
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• 한국지리정보학회지
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• 제24권4호
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• pp.55-64
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• 2021

이 연구는 지상레이저스캐너(TLS)측량에 대한 관측실험과 분석을 통해 TLS 위치정확도와 운용방안을 분석하고 가이드라인을 제시한다. TLS 관측실험에서는 TS/GPS 기준점측량 기반으로 지상기준점과 TLS점을 설치하고 목제 목표판을 설계·제작하여 사용하였다. 실험결과에서는 TLS 스캔 데이터의 RMSE는 축척 1/250의 지형도 제작과 레벨 1/100의 BIM에 활용이 가능하며, 드론영상데이터와 상호보완이 가능하다. 그리고 실험과정과 결과의 분석을 통해 TLS측량에 대한 가이드라인을 제시하였다.