• Journal of Digital Contents Society
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• v.16 no.4
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• pp.623-634
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• 2015

This research aims to perform an in-depth investigation about meanings and essence of working as clinical research nurses in local general hospitals. In order to interpret and reveal the meanings of role experience, data were collected from objects of 7 participants for 3 months. Data were analyzed by ethnographic research tools of Spradley. Themes conducted from this study were 'new experience about social learning process' and 'joys and sorrows through study participants ', 'lack of specialized learning course in nursing curriculums' and 'roles of general research planner', 'one's own work space' and 'proactive work environment that is relaxing and filled with consideration for others', 'hardship of being temporary employees. Clinical research nurses have experienced expansion of roles through new social learning processes. Conclusively, this study will provide useful basic data to develop new curriculum about clinical research nursing for nursing students and to improve working conditions for clinical research nurses.e purpose of this study is to design and implement a sign language dictionary for the deaf to understand information communication terminologies. When the deafs who have difficulties in communication use the internet, they can get help from this dictionary in accessing various types of information and expressing their intension. In order for the deaf to utilize the internet as efficiently as ordinary people, they must understand information communication terminologies first.

• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.275-291
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• 2003

It was essential to develop the DB-type teaching and teaming material for geography instruction using a method of ICT. The DB-type teaching and learning material was considered as a alternative in solving the problems of web-based geography instruction. Accordingly, in this study, the geography image DB program as developed, and based on this program the CD-ROM called GEO-DB, having the function of electronic dictionary of geography image for geography teaching and teaming was made. The GEO-DB was composed of 3,060 geography images collected by teachers and learners. The GEO-DB was made to be used simply by teachers and learners. Especially, the portfolio function was Included in the GEO-DB, and that was focused to the instructional system design of teacher and the self-directed teaming ability development of learner. Teachers and learners using this GEO-DB assessed that because the GEO-DB had the easiness of use, the speed of reference and the unlimitedness of extension, it could enlarge the possibility of using a method of In, and it could contribute to the development of geography teaming ability and the change of geography teaming attitude.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.17-23
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• 2004

This paper proposes to determine word senses in Korean language processing by corpus-based ontology learning. Our approach is a hybrid method. First, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the least weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9％ improvement over methods which do not use ontology for Korean translation.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.546-557
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• 2016

Tooth carving practice is required for novice learners in dentistry to understand dental morphology and acquire clinically underlying skills. Tooth carving practice is more effective when sequential models can be observed. The purpose of this study is to suggest a tooth carving practice model and to develop a mobile practice supporting tool based on tutorial providing 3D modeling information about steps of tooth carving. As a result, tooth carving practice model consists of the class activity including tooth information lecture and practice and mobile seamless learning connecting learners' practice and regular learning with the mobile tutorial tool. The mobile tutorial tool is implemented with tooth morphology dictionary, tooth carving practice/training tutorial, and 3D tooth modeling. The experts' evaluation on the developed contents shows that the content and function are valid(content validity: 5.0, interface validity: 4.53). Therefore, the mobile tutorial tool is suitable for supporting mobile seamless learning for tooth carving practice. Further researches are expected to be conducted to develop instructional models utilizing ICT and mobile contents in dentistry.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.329-338
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• 2011

Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.153-161
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• 2013

Recently, there has been a tremendous increase in the availability of educational materials for foreign language learning. As part of this trend, there has been an increase in the amount of electronically mediated materials available. However, conventional educational contents developed using computer technology has provided typically one-way information, which is not the most helpful thing for users. Providing the user's convenience requires additional off-line analysis for diagnosing an individual user's learning. To improve the user's comprehension of texts written in a foreign language, we propose an intelligent learning tool based on the analysis of the user's eyeball movements, which is able to diagnose and improve foreign language reading ability by providing necessary supplementary aid just when it is needed. To determine the user's learning state, we correlate their eye movements with findings from research in cognitive psychology and neurophysiology. Based on this, the learning tool can distinguish whether users know or do not know words when they are reading foreign language sentences. If the learning tool judges a word to be unknown, it immediately provides the student with the meaning of the word by extracting it from an on-line dictionary. The proposed model provides a tool which empowers independent learning and makes access to the meanings of unknown words automatic. In this way, it can enhance a user's reading achievement as well as satisfaction with text comprehension in a foreign language.

• Journal of the Korea Computer Industry Society
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• v.2 no.12
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• pp.1581-1590
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• 2001

This paper asserted more developmental model of WSD(word sense disambiguation) than J. Hur(2000)'s WSD model. This model suggested an improved statistical homonym disambiguation Model based on Bayes Theory. This paper using semantic information(co-occurrence data) obtained from definitions of part of speech(POS) tagged UMRD-S(Ulsan university Machine Readable Dictionary(Semantic Tagged)). we extracted semantic features in the context as nouns, predicates and adverbs from the definitions in the korean dictionary. In this research, we make an experiment with the accuracy of WSD system about major nine homonym nouns and new seven homonym predicates supplementary. The inner experimental result showed average accuracy of 98.32% with regard to the most Nine homonym nouns and 99.53% for the Seven homonym predicates. An Addition, we save test on Korean Information Base and ETRI's POS tagged corpus. This external experimental result showed average accuracy of 84.42% with regard to the most Nine nouns over unsupervised learning sentences from Korean Information Base and ETRI Corpus, 70.81 ％ accuracy rate for the Seven predicates from Sejong Project phrase part tagging corpus (3.5 million phrases) too.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.226-231
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• 2017

Word representation, an important area in natural language processing(NLP) used machine learning, is a method that represents a word not by text but by distinguishable symbol. Existing word embedding employed a large number of corpora to ensure that words are positioned nearby within text. However corpus-based word embedding needs several corpora because of the frequency of word occurrence and increased number of words. In this paper word embedding is done using dictionary definitions and semantic relationship information(hypernyms and antonyms). Words are trained using the feature mirror model(FMM), a modified Skip-Gram(Word2Vec). Sense similar words have similar vector. Furthermore, it was possible to distinguish vectors of antonym words.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.321-343
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• 2016

This study aimed to design a semi-automatic web-based pilot system 1) to build a Korean novel geo-name, 2) to update the database using automatic geo-name extraction for a scalable database, and 3) to retrieve/visualize the usage of an old geo-name on the map. In particular, the problem of extracting novel geo-names, which are currently obsolete, is difficult to solve because obtaining a corpus used for training dataset is burden. To build a corpus for training data, an admin tool, HTML crawler and parser in Python, crawled geo-names and usages from a vocabulary dictionary for Korean New Novel enough to train a named entity tagger for extracting even novel geo-names not shown up in a training corpus. By means of a training corpus and an automatic extraction tool, the geo-name database was made scalable. In addition, the system can visualize the geo-name on the map. The work of study also designed, implemented the prototype and empirically verified the validity of the pilot system. Lastly, items to be improved have also been addressed.