• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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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.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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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.

• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.21-25
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• 2015

The Internet of Thing (IoT) will be a very important core technology to implement Intelligent Smart Home Network and it will take charge of an important role connected to Smart Phone, Cloud Computing in the Ubiquitous environment. In this paper, Internal Autonomous Regulation by human autonomic nervous system was studied and its core mechanism was applied to the design of IoT based Autonomous Regulation System for Intelligent Smart Home Network. We proposed an autonomous regulating mechanism in which the factors of Temperature, Humidity and Illumination are automatically adjusted as they communicate with the connected things.

• Journal of Microbiology and Biotechnology
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• v.24 no.3
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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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• v.13 no.4
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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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• v.5 no.3
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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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• v.24 no.6
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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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.55-64
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• 2021

In this study, the operation method and accuracy of Terrestrial Laser Scanner(TLS) are reviewed and discussed by experimental measurements, and guidelines of TLS surveying operation are proposed. Ground control points and TLS station points were measured by TS and/or GPS, in TLS observation experiments, and wood targets were used which designed by this study team. RMSE accuracy of TLS scan shows that TLS surveying operation can be used in the topographic mapping of 1/250 scale and level of 1/100 BIM, the drone data also used in TLS data completeness. Additionally, as the results of the field experiment, the guidelines for TLS surveying operartions were proposed.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.33-37
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• 2019

Nowadays, the needs for linking smart devices are growing fast because of wide spread of smart devices such as smart TV, smart phones, smart pad and so on. This paper presents a game framework for linking smart TV and smart phones and proves its applicability by developing an example contents. The problem of connection between smart devices is basically a problem of connection between heterogeneous devices. The problem is that data transmission and reception between heterogeneous devices must be considered. Therefore, the core data is implemented by adopting the concept of cloud computing and storing it in a server, and in a smart TV, playing the game by using this data. The proposed framework could be applied to a lot of applications such as computer games.