• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.474-481
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• 2013

The purpose of this study is that pre-service teacher are able to design creative instruction based on NFTM-TRIZ for building up their continuous creative thinking and promoting their creative instruction activities. NFTM-TRIZ is a educational technology system to form and develop creative thinking from child to adult continuously based on TRIZ theory. TRIZ is the thinking technique of creative problem solving that can be the tool of inventory solutions by finding and get over the key of contradiction that is necessary to obtain ideal final results of suggested problems. The subjects for this study were 90 pre-service teachers who are attending third and fourth graders of Teachers' College in G university and are taking 'Curriculum and Educational Evaluation'. The creativity program for this study was carried out for ten minutes at the end of lectures. The verification for this study results were performed two faces. First, pre-service teachers presented teaching and learning plan for one time used 8 Steps' Teaching and Learning Model based on NFTM-TRIZ. Second, researcher got feedback from them about this creative program.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1447-1451
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• 2008

저수지 군 연계 운영을 위한 각 댐에서의 방류량을 결정하기 위해서는 대개 각 댐의 초기 저수량, 유역 상 하류 댐의 총 저수량, 수요량, 기간별 발전 목표 달성 정도, 그리고 예상되는 미래유입량 등이 추정되어야 한다. 본 연구에서는 댐 군 연계운영을 위한 일별 최적화 모형인 CoMOM(Coordinated Multi-reservoir Operating Model, 4.2)의 상위 단계의 더 큰 단위 기간에 활용될 댐 군 연계 운영 기본 가이드라인을 신경망 기법을 활용하여 도출할 수 있을 지를 실험해 보고자 한다. 이 방법은 기본적으로 CoMOM이 제시하는 일별 운영 계획의 결과가 최선의 정책일것이라는 가정에 근거하고 있다. 즉, 주어진 상황에서 일별 CoMOM이 제시하는 결과를 교사 신호로 하여 신경망 학습을 수행하고, 이 결과를 통해 규칙(Rule)을 생성하는 과정으로 요약할 수 있다. 신경망 분석은 CoMOM이 이수기 모형인 점을 고려하여 이수기만을 대상으로 실험하였으며, 단위 분석기간을 10일로 택하여 미래 10일간의 방류량을 결정하는 것을 목표로 하였다. 신경망 모형의 입력요소로는 각 댐의 초기 유효 저수량, 유역 상 하류 댐의 총 저수량, 10일간의 수요량, 그리고 향후 한달 동안의 예상 유입량을 적용하였고, 출력요소로는 CoMOM에서 제시한 방류량 결과를 사용하였다. 모형의 유효성을 검증하기 위해 한강수계의 이수기를 대상으로 과거의 유입량 자료가 재현된다고 가정하고, 모의운영을 통하여 적합성을 분석하였다. 이를 위해 매일 단위의 실제 댐 군 연계 운영의 상황을 모의할 수 있는 실시간 시뮬레이션을 적용하였으며, 신경망 모형의 운영 기준에 의해 결정된 향후 10일 동안의 총 방류량이 해당기간 동안 동일한 양으로 나누어 방류된다는 가정 하에 모의 운영하였다. 그리고 도출된 운영 결과는 최종적으로 실적과의 평균저수량, 발전량, 여수로 방류량 비교를 통해 평가하였다.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).

• Journal of The Korean Association For Science Education
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• v.32 no.2
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• pp.346-371
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• 2012

The purpose of this study is to investigate the possibilities of science ethics education with history of science (HOS) and to develop its teaching and learning model for secondary school students. A total of 72 cases about science ethics were extracted from 20 or more HOS books, journal articles, and newspaper articles. These cases were categorized into 8 areas, such as forgery, fabrication, violation of bioethics in testing, plagiarism and stealth, unfair allocation of credit, over slander, conjunction with ideologies, and social responsibility problems. The results of this study are as follows. First, research forgery, occurring in the process of the research, was the most frequent in HOS. Second, we developed eight teaching lesson plans for each area. Third, we proposed a teaching and learning model based on the developed lesson plans as well as related teaching and learning models in the fields of science ethics education, ethics education, and history education. Our model has five steps, 'investigating-suggesting casesclarifying problems-finding alternatives-summarizing'.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.145-152
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• 2012

Due to the large sizes of streaming media, previous delivery techniques are not providing optimal performance. For this purpose, video proxy server is employed for reducing the bandwidth consumption, network congestion, and network traffic. This paper proposes a fast distributed network file system using state transition model in the media streaming system for efficient utilization of video proxy server. The proposed method is composed of three steps: step 1. Training process using state transition model, step 2. base and decision probability generation, and step 3. storing and deletion based on probability. In addition, storage space of video proxy server is divided into each segment area in order to store the segments efficiently and to avoid the fragmentation. The simulation results show that the proposed method performs better than other methods in terms of hit rate and number of deletion. Therefore, the proposed method provides the lowest user start-up latency and the highest bandwidth saving significantly.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.441-447
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• 2018

This study aims to develop digital techniques that enable repeated practice of dental radiography using augmented reality technology. A three-dimensional object was fabricated by superimposing a photograph of an adult model and a computed tomography image of a manikin phantom. The system was structured using 106 radiographs such that one of these saved radiographs is opened when the user attempts to take a radiograph on a mobile device. This system enabled users to repeatedly practice at the pre-clinical stage without exposure to radiation. We attempt to contribute to enhancing dental hygienists' competency in dental radiography using these techniques. However, a system that enables the user to actually take a radiograph based on face recognition would be more useful in terms of practice, so additional studies are needed on the topic.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• (The) Research of the performance art and culture
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• no.36
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• pp.327-363
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• 2018

This paper tracks the present state of the Gugak educational programs run by Gugak-specialized museums including Museum of Gugak not only to set a directionality of Museum of Gugak to step forward for their main purposes, but also to find measures to invigorate its Gugak educational programs. There are 826 museums registered in 2016 nationwide, and ten of them are Gugak-specialized museums including Museum of Gugak. An analysis of the educational programs by Museum of Gugak presents high achievements in concentrativeness, participation and satisfaction levels. However, several issues such as difficulty level adjustment, education period arrangement, contents development, setting of a precise aim of education, and overcoming of regional limitations are to be solved in the future. Considering these special circumstances, the study suggests setting a directionality of Gugak education by following four conditions: Firtly, the Gugak education programs by Museum of Gugak should be user-oriented. Secondly, it is necessary to provide customized learning programs to suit users of various ages and generations. Thirdly, a solid education is required to enhance creativity deviating from uniform, unilateral, fragmentary education focused on materials and relics of museums as the users' experiences and learning levels vary. Fourthly, integrated education with relevant study in common use is required as the specialized environments of the museum could cause users psychological resistance and lessen their willingness to approach. Focusing on these four conditions several invigoration measures for the Gugak education programs are discussed: Firstly, a step-by-step approach, not a radical shift, is required in order to turn existing programs into the user-oriented. Secondly, customized learning programs should be planned in consideration of life cycle of the users. Thirdly, it is necessary to establish virtuous circulation reflecting activity-based contents as well as to provide the users experiences through five senses for solid Gugak education, in which various elements such as experiencing, learning, playing, viewing are reflected manifoldly. Fourthly, integrated education can be implemented when the features of Gugak educational programs are internally structured and the external environment matures.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.369-378
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• 2020

This paper examines inquiries generated in three different online educational programs in Korea at the secondary educational level. Data covers 12,023 inquiries recorded during the first semester of 2019 and the compared groups among three programs are divided by four criteria: its type, period, inquirer, and the way of response. Statistical comparisons using Chi-square test suggest that there are significant differences in frequency rates of inquiries among three programs. First, 'Program A' has more inquiries by student themselves, mostly in the middle of the semester about the contents. Second, inquiries are more frequent for 'Program B' by the coordinating teachers about system-related or evaluation-related questions, either at the beginning or the end of the semester. Third, in the case of 'Program C', parents of health-impaired students are the main inquirers who ask admin-related questions at the beginning of the semester. With respect to the way of response to inquiries, more than 95% of inquiries are answered immediately for all three programs. These quantitative findings are also supported qualitatively, by face-to-face interviews with operators of the three programs. Results of this paper can be used for educational practitioners and experts when they design and operate the customized online educational programs with different purposes and different target-students in the future.