• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.241-248
• /
• 2011

Homographs can have multiple senses. In order to understand the meaning of a sentence, it is necessary to identify which sense isused for each word in the sentence. Previous researches on this problem heavily relied on the word co-occurrence information. However, we noticed that in case of verbs, information about subordinating cases of verbs can be utilized to further improve the performance of word sense disambiguation. Different senses require different sets of subordinating cases. In this paper, we propose the verb sense disambiguation using subordinating case information. The case information acquire postposition features in Standard Korean Dictionary. Our experiment on 12 high-frequency verb homographs shows that adding case information can improve the performance of word sense disambiguation by 1.34%, from 97.3% to 98.7%. The amount of improvement may seem marginal, we think it is meaningful because the error ratio reduced to less than a half, from 2.7% to 1.3%.

• Journal of Digital Convergence
• /
• v.18 no.7
• /
• pp.47-56
• /
• 2020

The purpose of this study is to comprehensively analyze elementary school teachers' perceptions of the use of artificial intelligence in education. Recently, interest in the use of artificial intelligence has increased in the field of education. However, there is a lack of research on the perceptions of elementary school teachers using AI in education. Using descriptive statistics, multiple linear regression analysis, and semantic differential meaning scale, 69 elementary school teachers' perceptions of using AI in education were analyzed. As a results, artificial intelligence technology was perceived as most suitable method for assisting activities in class and for problem-based learning. Factors which influence the use of AI in education were learning contents, learning materials, and AI tools. AI in education had the features of personalized learning, promoting students' participation, and provoking students' interest. Further, instructional strategies or models that enable optimized educational operation should be developed.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.767-776
• /
• 2020

GcGAN is a deep learning model to translate styles between images under geometric consistency constraint. However, GcGAN has a disadvantage that it does not properly maintain detailed content of an image, since it preserves the content of the image through limited geometric transformation such as rotation or flip. Therefore, in this study, we propose a new image-to-image translation method, MSGcGAN(Multi-Scale GcGAN), which improves this disadvantage. MSGcGAN, an extended model of GcGAN, performs style translation between images in a direction to reduce semantic distortion of images and maintain detailed content by learning multi-scale images simultaneously and extracting scale-invariant features. The experimental results showed that MSGcGAN was better than GcGAN in both quantitative and qualitative aspects, and it translated the style more naturally while maintaining the overall content of the image.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.2
• /
• pp.1-9
• /
• 2008

As the size of software becomes larger and the features of it become complex. it is really hard to successfully complete the project without following development guidelines. Software process is the best practices and procedures that organizations of high maturity and capability of software development carry out in common, and it is a set of progressive ideas of management. However, complicated and unfamiliar concepts can interrupt the introduction and improvement of software process of the organizations. Even though many kinds of frameworks such as standard of process and maturity measurement models are introduced, it is still difficult to follow software process without fully understanding their relations. The purpose of this study is to support successful internalization of organizations that introduce and use software process. It also suggests the design of standard ontology, standard relationship domain ontology, and the lifestyle of software process and the relations between them.

• Journal of the Korean Society for Library and Information Science
• /
• v.50 no.2
• /
• pp.309-336
• /
• 2016

This paper introduces a relation extraction system that can be used in identifying and classifying semantic relations between biomedical entities in scientific texts using machine learning methods such as Support Vector Machines (SVM). The suggested system includes many useful functions capable of extracting various linguistic features from sentences having a pair of biomedical entities and applying them into training relation extraction models for maximizing their performance. Three globally representative collections in biomedical domains were used in the experiments which demonstrate its superiority in various biomedical domains. As a result, it is most likely that the intensive experimental study conducted in this paper will provide meaningful foundations for research on bio-text analysis based on machine learning.

• Journal of Game and Entertainment
• /
• v.2 no.2
• /
• pp.61-69
• /
• 2006

Along with advance in visual culture, the importance of motion graphic has been increasingly emphasized day by day, which can maximize information delivery using image illustration and typography, graphic factors of images. In addition, we can easily see increasing cases where what a designer intends is visualized using creative typography in diverse mass media such as TV commercials, movies or web. Thanks to the effects of this trend, various ways of manufacturing works have been proposed in the field of motion typography by applying diverse factors including verbal ones, time, form, motion, colors, and sound for the purpose of expressing formless semantic notions through visual form of typography. However, physiological features such as sex, age, health status, pathological conditions, and body size can have a bigger effect on the process of real communication. Therefore, if property of quasi-verbal sound can be reflected appropriately in motion typography where communication is expressed only by visual form, it can enable people to understand what a designer intends faster and more exactly.

• Korean Journal of Remote Sensing
• /
• v.22 no.2
• /
• pp.123-129
• /
• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Journal of Korean Library and Information Science Society
• /
• v.50 no.3
• /
• pp.97-116
• /
• 2019

This study is to analyze the characteristics of 2019 revised RDA and suggest the consideration in aspects of cataloging using the literature reviews. The following 3 things are suggested with analyzing the revised RDA. First, high quality data such as supplementing cataloging data and constructing vocabulary encoding schemes should be needed to transform bibliographic data to linked data for the semantic web. Second, MARC should be expanded to accept the new conept of LRM and linked data being reflected in revised RDA because MARC is the unique encoding format untile linked data will be transformed from MARC data. Third, the policy statement and the application profile are needed for describing resource consistently because each entity and element has own condition and option, and there are different elements for applying rules in revised RDA. Based on this study, the RDA related researches should be in progress such as exapanding BIBFRAME as well as MARC to accept the new concepts in revised RDA, and, also, reflecting and accepting RDA being able to use revised RDA rules and registries in libraries and nations that have been faced to revise their own cataloging rules.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.419-428
• /
• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.27 no.2
• /
• pp.175-185
• /
• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.