• The Journal of the Convergence on Culture Technology
• /
• v.1 no.1
• /
• pp.15-26
• /
• 2015

The purpose of this study was to analyze the sexual related predicting factors for Female University students in Korea. The cross-sectional descriptive study design was used. We selected 320 students from 6 Universities located in Seoul, Chungchung-do and Gangwon-do by convenience random sampling and received IRB from Y Univ. 299 students were included in the final analysis using logistic regression. Among 299 students, 60.2% of students reported to have sexual experience. The result of analyzing the related factors to sexual experience revealed that the students who were having friends who had sexual experience, smokers and those who were high grade, had significantly more sexual experience. According to the results of this study, there should be an intensive and female tailed sexual related program development for the University students, especially for smokers and including smoking cessation program. And the school health services of University combined general staff working should be strengthened to protect the University students from the critical situation caused by unwanted sexual experience.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.737-744
• /
• 2023

Sarcopenia is an increasingly common disease among the elder that has recently received attention. Although the causes of sarcopenia are diverse, aging, dietary habits, lack of exercise are the one of the major factors. As the causes of sarcopenia are diverse, it is important to develop strategies for prevention and treatment. However, predicting sarcopnia accuartely is difficult due to the variety of factors involved. Here, machine learning can significantly improve the accuracy and convenience of predicting sarcopenia. However, since lifestyle habits and biological data are vast, using data without preprocessing may be inappropriate in terms of time complexity and accuracy. This paper reviews recent literature on sarcopnia and its causes, focusing on preprocessing the data to be used in sarcopnia prediction machine learning accrodingly.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.3
• /
• pp.123-129
• /
• 2009

Ubiquitous home is emerging as the future digital home environments that provide various ubiquitous home services like u-Life, u-Health, etc. It is composed of some home appliances and sensors which are connected through wired/wireless network. Ubiquitous home services become aware of user's context with the information gathered from sensors and make home appliances adapt to the current home situation for maximizing user convenience. In these context-aware home environments, it is the one of significant research topics to predict user behaviors in order to proactively control the home environment. In this paper, we propose Multi-Level prediction algorithm for context-aware services in ubiquitous home environment. The algorithm has two phases, prediction and execution. In the first prediction phase, the next location of user is predicted using tree algorithm with information on users, time, location, devices. In the second execution phase, our table matching method decides home appliances to run according to the prediction, device's location, and user requirement. Since usually home appliances operate together rather than separately, our approach introduces the concept of mode service, so that it is possible to control multiple devices as well as a single one. We also devised some scenarios for the conceptual verification and validated our algorithm through simulations.

• The Journal of Bigdata
• /
• v.5 no.2
• /
• pp.77-84
• /
• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.133-140
• /
• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.869-873
• /
• 2006

유역에서의 강우-유출해석모형과 예측모형의 정확성을 위해서 기상요소와 지형인자간의 상관성에 대해서 많은 연구가 진행되었으나 수자원의 효율적인 해석과 관리를 위한 물과 관련된 기관들은 단지 물 관련 정보를 DB로 구축한 수준에 머물러 있다. 급속한 정보화 시대로 인해 편의성을 추구하기 위한 서비스 요구가 증가되어 사용자들은 질적으로 우수한 정보뿐만 아니라 여러 가지 매체를 통해서 시간과 장소에 제한 없이 사용자들과 관련된 각종 정보를 원하고 있다. 그래서 수문학적으로 중요한 위치에 있는 지점과 상습적으로 홍수피해를 입고 있는 지역의 홍수예경보를 위해서 물 관련 정보를 신속히 활용, 해석 및 예측하는 시스템의 개발이 필요하다. 따라서 물리적인 공간상에서 3차원 GIS DB, GPS 또는 무선인터넷 기술 등의 전자기술(IT:컴퓨터, 통신, 방송)을 도입하여 인터넷이나 무선통신을 통해 물 관련 정보를 획득하고 홍수예경보 시스템의 효율적인 관리를 위한 기법을 개발하고자 한다. 이를 위해서 본 연구는 위치기반서비스(LBS : Location Based Service)의 기반기술과 응용사례 및 활용 가능성을 검토하여 홍수예경보 정보시스템을 제시하였다.

• Fire Science and Engineering
• /
• v.19 no.2 s.58
• /
• pp.20-28
• /
• 2005

The existing algorithms or programs of egress time estimations rule out the walking velocity of each single person. But this algorithm can not be applied to estimation of evacuation in a hospital, because most patients are handicapped or walking on various kinds of tools. This study measured the moving velocities of patients according to different types of physical handicap. Also evacuation drills in several hospitals were carried out to establish an algorithm for prediction of total egress time of wards. Besides these measurements awareness of staffs about safety was investigated with the questionnaire. The results of this study is divided into two serial papers.

• Information and Communications Magazine
• /
• v.26 no.1
• /
• pp.31-37
• /
• 2009

우리나라는 세계 최고 수준의 IT 인프라를 기반으로 네트워크 및 서비스 융합, RFID 등 u-IT 서비스 확산 등을 통해 유비쿼터스 사회로 빠르게 진입하고 있다. 향후 디지털 융합이 가속화됨에 따라 시간과 공간의 제약 없이 원하는 정보의 획득 활용이 증가하고, u-Health, u-learning 등 IT가 타산업과 융합되면서 높은 부가가치를 창출할 것으로 전망 된다. 그러나 정보화의 급속한 진전에 따른 사회 전반의 편의성과 효율성이 향상하였으나, 해킹 바이러스, 개인정보 유출사고, 스팸 등 역기능으로 인한 피해도 확산되고 있다. 최근에는 네트워크 방어체계를 무력화시키는 지능화된 해킹, 대량의 고객정보 유출, 사회공학 기법을 활용한 피싱 등 이용자의 자산과 프라이버시를 침해하는 사이버범죄 증가 등으로 이용자자산과 권리 보호관점에서의 정보보호의 중요성이 부각되고 있다. 향후 시간과 장소에 상관없이 지식정보를 활용하여 편리하고 쾌적한 생활을 누리게 하는 지식정보사회는 예측 불가능한 위험이 곳곳에 산재한 정보위험사회로의 진입을 의미 할 수도 있다. 그러므로 미래사회에서 예상되는 위협을 예측하여 효과적으로 사전에 예방할 수 있는 체계를 마련하는 것은 안전하고 신뢰할 수 있는 지식정보사회를 향유하기 위한 전제조건으로 작용한다. 이에 본고에서는 미래 지식정보사회에 대비한 정보보호 전략으로 안전한 u-사회 청사진 설계 및 환경조성 선도와 국제화, 사이버위협 예방 및 대응체계의 입체적 조화와 융합, 정보보호 기술 제품 산업간 선순환 촉진과 성장 등 3대 전략을 설정하고 실행방안을 제시한다.

• Journal of Korean Society of Transportation
• /
• v.27 no.2
• /
• pp.207-214
• /
• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.2
• /
• pp.202-209
• /
• 2008

Application of wind turbine generator system (WTGS) needs researches for performance prediction, pitch control, and optimal operation method. Recently a new type WTGS is developed and under testing. The notable feature of this WTGS is that it consists of two rotor systems positioned horizontally at upwind and downwind locations, and a generator installed vertically inside the tower. In this paper, a nonlinear simulation software developed for the performance prediction of the Dual Rotor WTGS and testing of various control algorithm is introduced. This software is hybrid in the sense that FORTRAN is extensively used for the purpose of computation and Matlab/Simulink provides a user friendly GUI-like environment.