• Journal of Communications and Networks
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• v.11 no.5
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• pp.464-472
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• 2009

In this paper, we propose a packet scheduling algorithm for cellular relay networks by considering relay selection, variation of channel quality, and packet delay. In the networks, mobile users are equipped with not only cellular but also user relaying radio interfaces, where base station exploits adaptive high speed downlink channel. Our proposed algorithm selects a user with good cellular channel condition as a relay station for other users with bad cellular channel condition but can get access to relay link with good quality. This can achieve flexible packet scheduling by adjusting transmission rates of cellular link. Packets are scheduled for transmission depending on scheduling indexes which are calculated based on user's achieved transmission rate, packet utility, and proportional fairness of their throughput. The performance results obtained by using computer simulation show that the proposed scheduling algorithm is able to achieve high network capacity, low packet loss, and good fairness in terms of received throughput of mobile users.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.649-650
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• 2015

We have been performing daily VLBI monitoring of the flux density of Sagittarius (Sgr) $A^{\ast}$ at 22 GHz from February 2013 to August 2014 using a sub-array of the Japanese VLBI Network (JVN). The purpose of this monitoring is to explore the flux density variability at daily time resolution for a period longer than one year with the G2 cloud approaching. The flux density of Sgr $A^{\ast}$ is basically stable during the observational period, though there are some small variations. The average and scattering range are consistent with the previously observed values. We have observed no strong flare of Sgr $A^{\ast}$ although it is near the expected peri-center passing.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.247-253
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• 2007

In this paper, downlink capacity of time duplex division (TDD) based cellular wireless networks utilizing fixed hopping stations is investigated. In the network, a number of fixed subscriber stations act as hopping transmission stations between base stations and far away subscribers, forming a cellular and ad hoc mobile network model. At the radio layer, TDD code division multiple access (CDMA) is selected as the radio interface due to high efficiency of frequency usage. In order to improve the system performance in terms of downlink capacity, we propose different time slot allocation schemes with the usage of fixed hopping stations, which can be selected by either random or distanced dependent schemes. Performance results obtained by computer simulation demonstrate the effectiveness of the proposed network to improve downlink system capacity.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.85.2-85.2
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• 2012

In this paper, we introduce the 3D modeling of the coronal magnetic field in the solar active region by extrapolating from the 2D observational data numerically. First, we introduce a nonlinear force-free field (NLFFF) extrapolation code based on the MHD-like relaxation method implementing the cleaning a numerical error for Div B proposed by Dedner et al. 2002 and the multi-grid method. We are able to reconstruct the ideal force-free field, which was introduced by Low & Lou (1990), in high accuracy and achieve the faster speed in the high-resolution calculation (512^3 grids). Next we applied our NLFFF extrapolation to the solar active region NOAA 10930. First of all, we compare the 3D NLFFF with the flare ribbons of Ca II images observed by the Solar Optical Telescope (SOT) aboard on the Hinode. As a result, it was found that the location of the two foot-points of the magnetic field lines well correspond to the flare ribbon. The result indicates that the NLFFF well capture the 3D structure of magnetic field in the flaring region. We further report the stability of the magnetic field by estimating the twist value of the field line and finally suggest the flare onset mechanism.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.203-207
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• 2005

We will describe the function of interactive 3D digital archive prototype system that uses VR technology. This system allows the user or viewer to automatically control the viewpoint by selecting content based on the user's particular area of interest. However, it is not enough simply to look at the content. The user also needs to understand it. Therefore, we need systems that support the user's understanding of the content. An effective method to aid in understanding is comparison. In this paper, we are constructing a prototype system using the 3D digital archive of the ruins of a church on Gemiler Island in the Turkish Republic.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.8 no.3
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• pp.508-512
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• 1998

The NTC thermistors were sintered by using microwave hybrid heating method at $1100^{\circ}C$~$1300^{\circ}C$ and those electrical properties were investigated. The obtained $B_{25^{\circ}C/85^{\circ}C}$ values from temperature dependence of electrical resisitivity were around 3100~3200 K which were almost the same values as conventionally sintered ones. Compared with conventional sintering process, this process could complete whole sintering process within 20 minutes. This the processing time and energy consumption could be reduced through this rapid heating by using microwave hybrid heating.t there were showed only two peaks, glycolide melting peak and lower molecular weight melting peak without lauryl alcohol. Conversion increased slowly with the reaction time up to 50 minutes, and then gave a sudden increase above that. The reaction time to disappear in glycolide melting peak during polymerization was shortened with the increase of lauryl alcohol content. Zero-shear viscosity of polyglycolic acid decreased with the increase of free acid content in glycolide.ssional energy and bending hysteresis increased. \circled3 Surface characteristics such as friction coefficient and thickness variation of highly shrinkage fabrics became relatively roughened state. \circled4 Since stiffened and roughened characteristics of highly shrinkage fabrics, drapabilities of them were significantly lowered. Additionally thermal insulation property of high shrinkage fabric was higher than that of low shrinkage fabric due to bulky and thickened feature. From the results, it is considered that the silk fabrics with high filling shrinkage have the good bulkiness and heat keeping properties and thus they have the suitable characteristics for high quality men's and women's formal garments.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.718-729
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• 2006

There has been increasing interest in automatic term recognition (ATR), which recognizes technical terms for given domain specific texts. ATR is composed of 'term extraction', which extracts candidates of technical terms and 'term selection' which decides whether terms in a term list derived from 'term extraction' are technical terms or not. 'term selection' is a process to rank a term list depending on features of technical term and to find the boundary between technical term and general term. The previous works just use statistical features of terms for 'term selection'. However, there are limitations on effectively selecting technical terms among a term list using the statistical feature. The objective of this paper is to find effective features for 'term selection' by considering various aspects of technical terms. In order to solve the ranking problem, we derive various features of technical terms and combine the features using machine-learning algorithms. For solving the boundary finding problem, we define it as a binary classification problem which classifies a term in a term list into technical term and general term. Experiments show that our method records 78-86% precision and 87%-90% recall in boundary finding, and 89%-92% 11-point precision in ranking. Moreover, our method shows higher performance than the previous work's about 26% in maximum.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.