• Journal of the Korean earth science society
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• v.34 no.6
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• pp.550-560
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• 2013

In this study, the variations of geoid measured by GRACE satellite are investigated in the 20 volcanic areas erupted since 2005, and it is recognized that a detailed geological study is necessary in using geoid data for a research of the magmatic activities under the volcano. Therefore, the relationship between the regional geoid variation obtained by GRACE satellite and the change of magma activity, is studied in Japan's Shinmoedake volcano in the Kirishima volcanic complex whose eruption in 2011 was studied in detail geologically. Throughout this study the increase of geoid from 2002 in the Shinmoedake volcanic area is confirmed to be caused by the increase of gravity under the volcano, which is well matched with geological interpretation of the continuous intrusion of basaltic magma into magma chamber during several years before the 2011 eruption. The result indicates that information of the geoid variation measured by GRACE satellite is useful for monitoring the possibility of volcanic eruption although there is a need to more study to be able to confirm the possibility.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.53-62
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• 2021

After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1150-1158
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• 2013

Artificial cochlear implant system is known as the most efficient and widespread device to patients who have cochlear disorder. However, current commercialized artificial cochleas have inconveniences because of large volume size and high power consumption, requiring further research on improvements in terms of the size, power, and performance. In this paper, we will introduce our fully implantable artificial cochlear implant system, where small-size sensors and actuators are wirelessly connected, focusing on communication system design and its performance simulation.

• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.2
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• pp.70-82
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• 2003

본 연구의 목적은 산림경관의 스트레스 완화 효과를 생리측정(피부 돌출치와 심장박동시간)과 심리측정 (ZIPER 설문지)을 통하여 조사하는 것이다. 연구가설은 두 가지로 첫 번째 가설에서는 산림경관을 본 사람들의 스트레스 측정치가 인공경관을 본 사람들의 측정치보다 낮을 것이라고 예측되었다. 두 번째 가설에서는 산림경관을 본 사람들의 스트레스 해소가 인공경관을 본 사람들보다 보다 빠르고 완전하게 발생할 것으로 예측되었다. 실험에는 대학 학부학생 70명이 참여하였으며, 실험에 이용된 산림 및 인공경관은 예비설문을 거쳐 선정되었다. 실험결과를 보면 첫 번째 가설은 피부 돌출치와 긍정적인 심리요인 그리고 부정적인 심리요인에서 채택되었다. 두 번째 가설 중 스트레스 해소속도에 대한 가설은 피부 돌출치와 심장박동시간에서 모두 채택되었으나, 스트레스 해소의 완전성에 대한 가설은 피부 돌출치에서만 채택되었다. 비록 심장박동시간과 집중/호기심 요인에서 연구가설이 채택되지 못했지만 전반적으로 산림경관과 인공경관을 비교할 때 산림경관의 스트레스 완화효과가 더 크고 빠르고 완전함을 알 수 있다. 본 연구의 의의는 산림경관의 효과를 두 가지 측정(생리적, 심리적)을 이용하여 조사하였다는 점과 국외연구결과와 유사한 결과를 얻음으로써 이러한 효과가 지역에 관계없이 공통적임을 밝혔다는 점이다. 본 연구결과는 산림이 인간의 건강과 복지에 긍정적이라는 점을 입증함으로써 산림의 조성과 보존에 중요한 근거를 제공할 수 있다. 보다 발전적인 후속연구를 위해서는 다양한 종류의 인공경관과 일반시민들의 참여, 그리고 인지능력의 병행이 필요하다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.87-95
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• 2018

Recently, with the technological development of artificial intelligence, related market is expanding rapidly. In the artificial intelligence technology field, which is still in the early stage but still expanding, it is important to reduce uncertainty about research direction and investment field. Therefore, this study examined technology trends using text mining and topic modeling among big data analysis methods and suggested trends of core technology and future growth potential. We hope that the results of this study will provide researchers with an understanding of artificial intelligence technology trends and new implications for future research directions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.63-69
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• 2010

Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. This paper performs an empirical comparison of Boosted neural networks and traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Journal of IKEEE
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• v.17 no.4
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• pp.434-439
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• 2013

Artificial landmarks have been widely used for reducing the uncertainty in localization of a mobile robot. In addition, research for efficient placement of artificial landmarks has been considered as one of the fundamental issues since the cost of localization is increased with the number of used landmarks. Therefore, this paper proposes a method in which landmarks are efficiently placed by considering the uncertainty characteristics of the motion model and the sensor model. Because two models have different uncertainty distributions, the final uncertainty can be considerably reduced through their efficient combination. The usefulness of the proposed method is demonstrated by simulation results.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.637-643
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• 2016

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.71-78
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• 2016

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

• Journal of Korea Game Society
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• v.17 no.4
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• pp.101-108
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• 2017

This study explores how Artificial Intelligence (AI), which is tremendously developed these days, applies to the game and advances. It analyzes the reality of AI and provides reasonable suggestion in Poker, one of the most popular games. Specifically, this study focuses on Texas Hold'em, the most favored kind in the world among various kinds of Poker games and deals with two AIs, Libratus and DeepStack that have applied to the game. Several news media report the growth of AI, but this study will multi-dimensionally discusses how and why AI works in Poker, the real problems of AI, and suggestions for advancement.