• Journal of Korean Academy of Nursing
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• v.28 no.1
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• pp.210-220
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• 1998

The various and serious types of disaster occur everyday and everywhere on the earth. There is no doubt that it is very timely to discuss about the effectiveness and preparedness of disaster. The purpose of this study is to develop a curriculum on the disaster management through reviewing disaster concepts and the disaster management system. For the empirical relevance of the study, researchers participated in a couple or more disaster training program, reviewed references, and consulted to the experts working on action parts in the area. As a result, the 'Integrated Disaster Management System Model (IDMSM)' was designed, in which four dimensions were explained. Then the 'Disaster Curriculum Model (DCM)' was explored with its theoretical framework based on the system model. The developed curriculum is composed of four levels ; the introductory course, the fundamental course, the advanced course, and the expert course. From this DCM, basically the course-outlines of two subjects in the introductory course, 18 subjects in the fundamental course (5 of direct services. 13 of indirect services) were developed. Also each course-outline was explored by its course objective, learning objectives, contents, and its length. Finally to make the most of the results, suggestions are proposed. The governmental considerations on the policy should support the systematic and integrated educational program to practice, appointing 「Disaster School」 or 「Disaster Training Center」 of relevance and accountabilities. The further study should explore the higher levels of the DCM through interdisciplinary efforts, and develop the text aterials. ilities. The further study should explore the higher levels of the DCM through interdisciplinary efforts, and develop the text materials.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.467-474
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• 2018

To organize and document the contents of meetings and discussions is very important in various tasks. However, in the past, people had to manually organize the contents themselves. In this paper, we describe the development of a system that generates the meeting minutes automatically using the TextRank algorithm. The proposed system records all the utterances of the speaker in real time and calculates the similarity based on the appearance frequency of the sentences. Then, to create the meeting minutes, it extracts important words or phrases through a non-supervised learning algorithm for finding the relation between the sentences in the document data. Especially, we improved the performance by introducing the keyword weighting technique for the TextRank algorithm which reconfigured the PageRank algorithm to fit words and sentences.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.457-464
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• 2008

In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.29-39
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• 2014

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

• Journal of Science Education
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• v.40 no.1
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• pp.90-102
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• 2016

Flipped learning is generally designed to allow students to learn on their own in advance with the help of scaffolding material such as videos and text, and in the classroom, it is operated with the help of a teacher while the class is being learner-centered. For flipped learning, each of the teachers has to design the class, collect information, and prepare for scaffolding material, so they get to face a lot of difficulties spending much time to reorganize the curriculum and produce a video and so on. Accordingly, this researcher has developed flipped learning textbook models applicable to science class by analyzing Korean and overseas textbooks, conducting an in-depth interview to six science teachers practicing flipped learning, and also developing and applying the science textbook sample model. The elementary, middle, and high school science textbook models developed include not only the textbook-based model with no videos presented in advance but also the lecture-type model, experiment-based model, and inquiry and research-based model to realize flipped learning. This study is expected to present crucial implications to develop textbooks and science class as a class to perform learner-centered inquiry activity.

• Child Health Nursing Research
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• v.24 no.4
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• pp.443-453
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• 2018

Purpose: This study was describes the development and implementation a sex education program with a blended learning method for university students. Methods: Sixty-eight university students were recruited either to the experimental group (n=35) or the control group (n=33). This program was developed based on the analysis, design, development, implementation, and evaluation model. The analysis phase consisted of a literature review, focus group interview, expert consultations, and target group survey. In addition, learning objectives and structure were designed, and a printed text-book, presentation slides, cross-word puzzle, and debate topics were developed. In the implementation phase, the program was conducted 3 times over the course of 3 weeks. The evaluation phase involved verification of the effects of the program on sex-related knowledge, sexual autonomy, and justification of violence, as well as an assessment of satisfaction with the program. Results: The experimental group had significantly higher scores on sex-related knowledge (t=5.47, p<.001), sexual autonomy (t=2.40, p=.019), and justification of violence (t=2.52, p=.015) than the control group. Conclusion: The results indicate that this sex education program with blended learning was effective in meeting the needs of university students and can be widely used in this context.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.22-30
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• 2019

For effective human-robot interaction, robots need to understand the current situation context well, but also the robots need to transfer its understanding to the human participant in efficient way. The most convenient way to deliver robot's understanding to the human participant is that the robot expresses its understanding using voice and natural language. Recently, the artificial intelligence for video understanding and natural language process has been developed very rapidly especially based on deep learning. Thus, this paper proposes robot vision to audio description method using deep learning. The applied deep learning model is a pipeline of two deep learning models for generating natural language sentence from robot vision and generating voice from the generated natural language sentence. Also, we conduct the real robot experiment to show the effectiveness of our method in human-robot interaction.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.39-47
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• 2017

The Internet have increased the number of digital web documents related to the history and traditions of Korean Culture. However, users who search for creators or materials related to traditional cultures are not able to get the information they want and the results are not enough. Document classification is required to access this effective information. In the past, document classification has been difficult to manually and manually classify documents, but it has recently been difficult to spend a lot of time and money. Therefore, this paper develops an automatic text classification model of traditional cultural contents based on the data of the Korean information culture field composed of systematic classifications of traditional cultural contents. This study applied TF-IDF model, Bag-of-Words model, and TF-IDF/Bag-of-Words combined model to extract word frequencies for 'Korea Traditional Culture' data. And we developed the automatic text classification model of traditional cultural contents using Support Vector Machine classification algorithm.