• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.315-322
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• 2010

The phase-screen and the split-step Fourier migrations, which are implemented in both the frequency-wavenumber and frequency-space domains by using one-way scalar wave equation, allow imaging in laterally heterogeneous media with less computing time and efficiency. The generalized-screen migration employs the series expansion of the exponential, unlike the phase-screen and the split-step Fourier migrations which assume the vertical propagation in frequency-wavenumber domain. In addition, since the generalized-screen migration generalizes the series expansion of the vertical slowness, it can utilize higher-order terms of that series expansion. As a result, the generalized-screen migration has higher accuracy in computing the propagation with wide angles than the phase-screen and split-step Fourier migrations for media with large and rapid lateral velocity variations. In this study, we developed a 2D prestack generalized-screen migration module for imaging a complex subsurface efficiently, which includes various dips and large lateral variations. We compared the generalized-screen propagator with the phase-screen propagator for a constant perturbation model and the SEG/EAGE salt dome model. The generalized-screen propagator was more accurate than the phase-screen propagator in computing the propagation with wide angles. Furthermore, the more the higher-order terms were added for the generalized-screen propagator, the more the accuracy was increased. Finally, we compared the results of the generalizedscreen migration with those of the phase-screen migration for a model which included various dips and large lateral velocity variations and the synthetic data of the SEG/EAGE salt dome model. In the generalized-screen migration section, reflectors were positioned more accurately than in the phase-screen migration section.

• Proceedings of the Korean Magnestics Society Conference
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• 2000.09a
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• pp.473-479
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• 2000

The nanocrystalline Fe$_{84}$Zr$_{7}$B$_{6}$Cu$_{1}$ $Al_{2}$ alloy was annealed at 450 $^{\circ}C$ and 550 $^{\circ}C$ for l hour to achieve the ultra-soft magnetic properties such as large magnetoimpedence ratio(MIR), the incremental permeability ratio(PR), nearly zero coercivity, zero magnetostriction, etc. The PR and MIR of the sample were measured from 100 kHz to 10 MHz at a cryogenic chamber where the temperature can be varie from 10 K to 300 K. The increment of MIR value is proportional to increasing temperature. The maximum PR values measured at high frequency above 1 MHz remain almost same despite of the temperature variation from 10 K to 300 K except the sharpness in PR curves. However, the maximum PR values measured below 1 MHz show drastic increment at above 150K due to thermal activation of magnetic domains.s.s.

• Korean Journal of Rural Living Science
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• v.6 no.2
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• pp.121-136
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• 1995

The purposes of this research are to find out the standards which mothers consider seriously when choosing a day care center and mother's perception for the quality criteria of day care center, and to estimate the differences of these according to region. The sample consisted of 568 mothers with children using day care center located at the city of Taegu and Dalsung Gun. Collected data were analyzed by frequency, Percentage, chi-square, t-test, chronbach's $\alpha$. The results are summerized as follows : 1. In mother's perception for the quality criteria of day care center, the mothers living in the large city perceived more importantly in staff qualifications, administration - evaluation, staff- parent/staff- child interaction, curriculum, staffing, physical environment nutrition, health and safty than mothers in rural community. 2. In the importance classified by domains of the quality criteria of day care center, there were statistically significant differences according to the variable of region in staff qualifications, staff-parent/staff-child interaction, curriculum, staffing, physical environment 3. Given the present situations (income, transportation, location of workplace, etc.), when mothers choose a day care center, generally, staff-parent/staff-child interaction was the most important factor. And according to region, the distance to workplace or home was the most important factor in the case of mothers living in the large city, on the other hand, staff-parent/staff-chad interaction was the most important factor in the case of mothers living in rural community when mothers choose an ideal day care center, generally staff-parent/staff-chad interaction was the most important selection standard, and there were not remarkable differences according to region.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.7 s.361
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• pp.17-22
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• 2007

OFDM (Orthogonal Frequency Division Multiplexing) is a special case of multicarrier transmission, where a single data stream is transmitted over a number of lower-rate subcarrier. One of the main reasons to use OFDM is to increase robustness against frequency-selective fading or narrowband interference. Unfortunately, an OFDM signal consists of a number of independently modulated subcarriers, which can give a large PAPR (Peak-to-Average Power Ratio) when added up coherently. In this paper, we investigate the performance of a simple PAPR reduction scheme, which requires no change of a receiver structure or no additional information transmission. The approach we employed is clipping in the time and frequency domains. The time-domain clipping is carried out with a predetermined clipping level while the frequency-domain clipping is done within EVM (Error Vector Magnitude). This approach is suboptimal with lower computational complexity compared to the optimal method. This evaluation is carried out on the OFDM system with an nonlinear amplifier. The simulation results demonstrated that the PAPR reduction algorithm is one of ways to reduce the effects of the nonlinear distortion of an HPA (High Power Amplifier).

• Clinical and Experimental Pediatrics
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• v.52 no.9
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• pp.1005-1014
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• 2009

Purpose:Epilepsy of child may cause high level of psychosocial difficulties for parents including stigmatization and stress and therefore worsen their quality of life (QOL). The purpose of this study was to evaluate the mediating effect of perceived stigma against epilepsy on QOL among parents with epileptic child. Methods:Two hundred and sixty parents of epileptic child recruited from five separated university hospital child neurology clinics specializing in epilepsy completed a demographic questionnaire, a medical questionnaire, a perceived stigma questionnaire and the Ro's Quality of Life Inventory, a popular tool for evaluating QOL of adults in Korea which is composed of 6 domains and 47 questions. Data was analyzed with SPSS 14.0 program using frequency analysis and descriptive analysis and with AMOS 7.0 program using Structural Equation Model (SEM) analysis. Results:The level of parents' QOL was relatively fair. SEM analysis on the quality of life level of parents showed that all variables (especially having religion, the monthly income, employment state, age of child, the leisure time, the perceived stigma level against epilepsy, and seizure frequency) directly affect the quality of life level of parents and that disease-related variables also affect the quality of life level of parents indirectly through the mediating factor (the perceived stigma), where the indirect effect is large with the existence of combined disabilities. The total effect on the quality of life level of parents is large with the existence of combined disabilities, the perceived stigma level, the leisure time, having religion, and the monthly income. Conclusion:The results of this study indicated that many factors including parents' perceived stigma may affect the quality of life of parents with epileptic children directly, and that some disease-related factors may affect indirectly through a mediating factor, the perceived stigma. Multidisciplinary collaboration would be imperative for their welfare.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.67-80
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• 2008

Various document categorization methods have been studied to provide a user with an effective way of browsing a large scale of documents. They do compares set of documents into groups of semantically similar documents automatically. However, the automatic categorization method suffers from low accuracy. This thesis proposes a semi-automatic document categorization method based on the domains of documents. Each documents is belongs to its initial domain. All the documents in each domain are recursively clustered in a level-wise manner, so that the category tree of the documents can be founded. To find the clusters of documents, the stop-word of each document is removed on the document frequency of a word in the domain. For each cluster, its cluster keywords are extracted based on the common keywords among the documents, and are used as the category of the domain. Recursively, each cluster is regarded as a specified domain and the same procedure is repeated until it is terminated by a user. In each level of clustering, a user can adjust any incorrectly clustered documents to improve the accuracy of the document categorization.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.728-739
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• 2020

Using webometric big data, this study examines the diversity of online information sources about the novel coronavirus causing the COVID-19 pandemic. Specifically, it focuses on some 28 countries where confirmed coronavirus cases occurred in February 2020. In the results, the online visibility of Australia, Canada, and Italy was the highest, based on their producing the most relevant information. There was a statistically significant correlation between the hit counts per country and the frequency of visiting the domains that act as information channels. Interestingly, Japan, China, and Singapore, which had a large number of confirmed cases at that time, were providing web data related to the novel coronavirus. Online sources were classified using an N-tuple helix model. The results showed that government agencies were the largest supplier of coronavirus information in cyberspace. Furthermore, the two-mode network technique revealed that media companies, university hospitals, and public healthcare centers had taken a positive attitude towards online circulation of coronavirus research and epidemic prevention information. However, semantic network analysis showed that health, school, home, and public had high centrality values. This means that people were concerned not only about personal prevention rules caused by the coronavirus outbreak, but also about response plans caused by life inconveniences and operational obstacles.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.1 s.53
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• pp.11-19
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• 2007

This paper proposes an optimal design method of distribution and capacities of linear viscous dampers for vibration control of two adjacent buildings. The previous researches have dealt with suboptimal design problem under the assumption that linear viscous dampers are distributed uniformly or proportionally to the sensitivity of the modal damping ratio according to floors, whereas this study deals with global optimization problem in which the damping capacities of each floor are independently selected as design parameters. For this purpose, genetic algorithm to effectively search multiple design variables in large searching domains is adopted and objective function leading to the global optimal solutions is established through the comparison of several optimal design values obtained from different objective functions with control performance and damping capacity. The effectiveness of the proposed method is investigated by comparing the control performance and total damping capacity designed by the proposed method with those of the previous method. In addition, the time history analyses are performed by using three historical earthquakes with different frequency contents, and the simulation results demonstrate that the proposed method is an effective seismic design method for the vibration control of the adjacent structures.