• Clinical and Experimental Pediatrics
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• v.60 no.7
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• pp.227-231
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• 2017

Purpose: This study aimed to verify the safety of low-dose topiramate on language development in pediatric patients with migraine. Methods: Thirty newly diagnosed pediatric patients with migraine who needed topiramate were enrolled and assessed twice with standard language tests, including the Test of Language Problem Solving Abilities (TOPs), Receptive and Expressive Vocabulary Test, Urimal Test of Articulation and Phonology, and computerized speech laboratory analysis. Data were collected before treatment, and topiramate as monotherapy was sustained for at least 3 months. The mean follow-up period was $4.3{\pm}2.7months$. The mean topiramate dosage was 0.9 mg/kg/day. Results: The patient's mean age was $144.1{\pm}42.3months$ (male-to-female ratio, 9:21). The values of all the language parameters of the TOPs were not changed significantly after the topiramate treatment as follows: Determine cause, from $15.0{\pm}4.4$ to $15.4{\pm}4.8$ (P>0.05); making inference, from $17.6{\pm}5.6$ to $17.5{\pm}6.6$ (P>0.05); predicting, from $11.5{\pm}4.5$ to $12.3{\pm}4.0$ (P>0.05); and total TOPs score, from $44.1{\pm}13.4$ to $45.3{\pm}13.6$ (P>0.05). The total mean length of utterance in words during the test decreased from $44.1{\pm}13.4$ to $45.3{\pm}13.6$ (P<0.05). The Receptive and Expressive Vocabulary Test results decreased from $97.7{\pm}22.1$ to $96.3{\pm}19.9months$, and from $81.8{\pm}23.4$ to $82.3{\pm}25.4months$, respectively (P>0.05). In the articulation and phonology validation in both groups, speech pitch and energy were not significant, and all the vowel test results showed no other significant values. Conclusion: No significant difference was found in the language-speaking ability between the patients; however, the number of vocabularies used decreased. Therefore, topiramate should be used cautiously for children with migraine.

• Journal of Legislation Research
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• no.44
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• pp.63-101
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• 2013

This article firstly explores into the concepts, components, and pictures of institutional realization of competition and diversity respectively on the premise that competition and diversity comprise the primary objectives to be pursued by the broadcasting-related laws which provide the concrete measures of media policy, and argues that while the competition objective has differentiation factors, there are also particularities in the diversity value in the broadcasting-related laws as sector-specific competition laws. Then assuming that special competition rules including structural regulatory measures particularly in the broadcasting market are required in order to realize values of competition and diversity harmoniously, this article suggests the following improvement directions for regulations aimed at protection of competition and diversity in the broadcasting-related laws. The first one is with the improvement method for regulations aimed at protection of competition. Regulation on share of audience as an ex ante regulation of status and regulation on prohibited activities as an ex post regulation of conduct may play important roles in substituting the causative regulation while seeking for diversity value. For this purpose, it is needed to develop a concrete method that incorporates diversity-related factors as consideration factors in the standard for determining illegality of prohibited activities by inference to methods of determining illegality in the competition law. The second one is with the improvement method for regulations aimed at protection of diversity. This could be considered from three viewpoints that are the setting of regulatory objectives, the identification of alternative regulatory measures, and the choice of regulatory measures and levels suitable for regulatory objectives. From these viewpoints, the regulatory framework should be improved mainly with institutional measures in which diversity value is used for tools of assessment and analysis, not just remaining as mere rhetorical devices, and whether or to what extent to maintain regulations seemingly unreasonable in terms of harmonization with economic objectives such as competition should be discreetly reviewed.

• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.267-282
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• 2022

This study aims to analyze the continuity and sequence between the intelligent life curriculum for grades 1-2 and the science curriculum for grades 3-4 with a focus on knowledge and inquiry process skills. The results demonstrate that contents related to science in the intelligent life curriculum consisted of only 10 out of 32 elements. Five elements were related to the science curriculum for grades 3-4 and limited to the 'life sciences' area. Particularly, the intelligent life curriculum did not address topics related to 'matter' and 'motion and energy'. Developmental connection was established in the 'life sciences' area and dramatic changes were noted for the topics related to 'earth and space' area. In terms of inquiry process skills, the levels of observation, measurement, inference, and communication naturally increased, whereas a developmental connection was noted between the intelligent life and science curricula. Classification can be viewed as a developmental link; however, viewing the classification as scientific from the epistemic perspectives was insufficient. In the case of expectation, a gap was observed in both curricula due to the absence of expectation activities in the intelligent life curricula. The study discussed the implications for securing the connection between the intelligent life and science curricula on the basis of these results.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.31-47
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• 2008

As OWL(Web Ontology Language) has been selected as a standard ontology description language by W3C, many ontologies have been building and developing in OWL. The lena developed by HP as an Application Programming Interface(API) provides various APIs to develop inference engines as well as storages, and it is widely used for system development. However, the storage model of Jena2 stores most owl documents not acceptable into a single table and it shows low processing performance for a large ontology data set. Most of all, Jena2 storage model does not consider hierarchical structures of classes and properties. In addition, it shows low query processing performance using the hierarchical structure because of many join operations. To solve these issues, this paper proposes an OWL ontology relational database model. The proposed model semantically classifies and stores information such as classes, properties, and instances. It improves the query processing performance by managing hierarchical information in a separate table. This paper also describes the implementation and evaluation results. This paper also shows the experiment and evaluation result and the comparative analysis on both results. The experiment and evaluation show our proposal provides a prominent performance as against Jena2.

• KDI Journal of Economic Policy
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• v.26 no.2
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• pp.281-314
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• 2004

This paper theoretically formulated and empirically explored the relationship between exchange rate pass-through (ERPT) for (average) market price and an individual country's price, using steel products data in the US market, with special reference to two major steel exporting countries, Korea and Japan. It was found that the direction of market ERPT can be different from that of individual ERPT that each exporter experiences, due to strategic interactions among producers and different parameters. Vector error correction (VEC) models and impulse response analysis were used with the statistical inference based on the bootstrap-after- bootstrap of Kilian (1998) for short-run, and the fully modified estimation of Phillips and Hansen (1990) was used for long-run. Empirical results indicate that market ERPT in the US market due to changes in Korea-US exchange rates is different from those due to changes in Japan-US exchange rates. The framework developed in this study indicates that this phenomenon is attributed to either (i) the two countries have individual ERPTs of different magnitudes and directions for the products in the US market, or (ii) the pricing strategies of the other exporters' (to the US steel market) respond differently depending on whether the price of the product from Korea changes or that from Japan does. As each exporter's ERPT can be significantly different, and market response to each country's ERPT can be also different, this study concludes that it is crucial for an exporter to understand how competitors in the market respond to changes in its price, as well as to understand how its price changes when the relevant exchange rate fluctuates.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of Navigation and Port Research
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• v.34 no.5
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• pp.349-354
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• 2010

Cheonan, Republic of Korea Navy patrol ship sank had happened by an unknown incident in the vicinity of Baekryeongdo southwest 1.6km(1 mile) sea at 21:45 on March 26, 2010. In terms of coastal researcher's point of view, it is meaningful to provide the sea condition of basic data necessary for search and rescue, more detailed predictions and inference data through the numerical simulations. Thus, in this study, we investigated the weather, wave, tide, tidal current, bottom soil conditions, and suspended sediment are investigated at the coast of Baekryeong-Daechung islands. And based on these data, the characteristics of sea conditions were analyzed. The tidal period at the time of incident corresponds between neap tide to mean tide. Until April 3-4 after March 26, the date of incident, the strongest velocity was progressed towards the spring tide. Thus, it was considered to be difficult to search and rescue operations. Also, because the ebb tide was in progress during 21:00 to 22:00, mass transport seems to be prevailed to the southeast. In particular, as the sudden turbulence due to the irregular topography existed was anticipated, we had carried out particle tracking experiment. From this experiment, depending on the situation of flow, the initial movement of the particles were directed to the southeast but it turned out moving towards the offshore based on the long term prediction. Through this result, it is considered that the scope of the search operation should be expanded towards the open sea.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.5
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• pp.191-202
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• 2022

Markov envelope as a theoretical solution of the parabolic wave equation with Markov approximation for the von Kármán type random medium is studied and approximated with the convolution of two probability density functions (pdf) of normal and gamma distributions considering the previous studies on the applications of Radiative Transfer Theory (RTT) and the analysis results of earthquake records. Through the approximation with gamma pdf, the constant shape parameter of 2 was determined regardless of the source distance r_o. This finding means that the scattering process has the property of an inhomogeneous single-scattering Poisson process, unlike the previous studies, which resulted in a homogeneous multiple-scattering Poisson process. Approximated Markov envelope can be treated as the normalized mean square (MS) envelope for ground acceleration because of the flat source Fourier spectrum. Based on such characteristics, the path duration is estimated from the approximated MS envelope and compared to the empirical formula derived by Boore and Thompson. The results clearly show that the path duration increases proportionately to r_o^1/2-r_o², and the peak value of the RMS envelope is attenuated by exp (-0.0033r_o), excluding the geometrical attenuation. The attenuation slope for r_o≤100 km is quite similar to that of effective attenuation for shallow crustal earthquakes, and it may be difficult to distinguish the contribution of intrinsic attenuation from effective attenuation. Slowly varying dispersive delay, also called the medium effect, represented by regular pdf, governs the path duration for the source distance shorter than 100 km. Moreover, the diffraction term, also called the distance effect because of scattering, fully controls the path duration beyond the source distance of 300 km and has a steep gradient compared to the medium effect. Source distance 100-300 km is a transition range of the path duration governing effect from random medium to distance. This means that the scattering may not be the prime cause of peak attenuation and envelope broadening for the source distance of less than 200 km. Furthermore, it is also shown that normal distribution is appropriate for the probability distribution of phase difference, as asserted in the previous studies.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.59-66
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• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.