• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.51-58
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• 2007

This paper suggests prototype of dialog system combining VXML(VoiceXML) that is the W3C's standard XML format for specifying interactive voice dialogues between human and computer, and RSS(RDF Site Summary or Really Simple Syndication) that is representative technology of semantic web for syndication and subscription of updated web-contents. Merits of the proposed system are as following: 1) It is a new method that recognize spoken contents using ire and wireless telephone networks and then provide contents to user via STT(Speech-to-Text) and TTS(Text-to-Speech) instead of traditional method using web only. 2) It can apply advantage of RSS that subscription of updated contents is converted to VXML without modifying traditional method to provide RSS service, 3) In terms of users, it can reduce restriction on time-spate in search of contents that is provided by RSS because it uses ire and wireless telephone networks, not internet environment. 4) In terms of information provider, it does not need special component for syndication of the newest contents using speech recognition and synthesis technology. We implemented a news service system using VXML and RSS for performance evaluation of the proposed system. In experiment results, we estimated the response time and the speech recognition rate in subscription and search of actuality contents, and confirmed that the proposed system can provide contents those are provided using RSS Feed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.2
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• pp.65-76
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• 2014

Green Infrastructure(GI) is suggested to recover urban water circulation system as a newly conceptual alternative methodology by Korean landscape field in recent years. In this context, the study considers the essential meaning of GI. The methodology of this study is literature review with 47 published papers which were peer-reviewed in international journals in the recent 5 years. These papers were collected from online database and academic archives. The main analysis targets are definition sentences about GI. The each sentences were interpreted by semantic structure between verbs and objects in the definition sentences. As the results, it figured out 5 aims('Provide', 'Improve', 'Produce', 'Conserve', 'Reduce'), 4 objects('Humanistic', 'Environmental', 'Ecological', 'Hydrological') and 3 spaces('Object space', 'Technically available spaces', 'Object or technically available spaces'). The '5 aims' connected with the elements of '4 objects' based on the '3 spaces'. The elements was connected to the '5 aims' via single form or 2~3 forms of the essential meaning networks of GI. The study provides 83 meaning networks to use landscape architecture planning and urban planning.

• Korean journal of communication and information
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• v.45
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• pp.220-254
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• 2009

Presidential TV debates serve as an important instrument for the general viewers to evaluate the candidates’ character, to examine their policy, and finally to make an important political decisions to cast ballots. Every words candidates utter in the course of entire election campaign exert influence of a certain significance by delivering their ideas and by creating clashes with their respective opponents. This study focuses on the conceptual venue, coined as ‘stasis’ by ancient rhetoricians, in which the clashes take place, and examines the words selection made by each candidates, the manners in which they form stasis, call for evidence, educate the public, and finally create a legitimate form of political argumentation. The study applied computer based content analysis using KrKwic and UCINET software to analyze semantic networks among the candidates. The results showed three major candidates, namely Lee Myung Bak, Jung Dong Young, and Lee Hoi Chang, displayed separate patterns in their use of language, by selecting the words that are often neglected by their opponents. Apparently, the absence of stasis and the lack of speaking mutual language significantly undermined the effects of debates. Central questions regarding issues of North Korea failed to meet basic requirements, and the respondents failed to engage in effective argumentation process.

• Journal of The Korean Association For Science Education
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• v.35 no.6
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• pp.997-1006
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• 2015

Perception of the experiment is one of the most important factors of students' understanding of scientific inquiry and the nature of science. This study examined the perception of middle and high school students of the meaning of 'experiment' in the biological sciences. Semantic network analysis (SNA) was especially used to visualize students' perception structure in this study. One hundred and ninety middle school students and 200 high school students participated in this study. Students responded to two questions on the meaning of 'experiment' in science and biology. This study constructed four semantic networks based on the collected response. As a result, middle school students about the 'experiment' in science are 'we', 'direct', 'principle' of such words was aware of the experiments from the center to the active side. The high school students' 'theory', 'true', 'information' were recognized as an experiment that explores the process of creating a knowledge center including the word. In addition, middle school students relative to 'experiment' of the creature around the 'dissection', 'body', high school students were recognized as 'life', 'observation' observation activities dealing with the living organisms and recognized as a core. The results of this study will be used as important evidence in the future to map out an experiment in biological science curriculum.

• Korean Journal of Cognitive Science
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• v.19 no.2
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• pp.143-161
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• 2008

The goal of this paper is to extract the hyponymy relation between words in the corpus. Adopting the basic algorithm of Hearst (1992), I propose a method of pattern-based extraction of semantic relations from the corpus. To this end, I set up a list of hypernym-hyponym pairs from Sejong Electronic Dictionary. This list is supplemented with the superordinate-subordinate terms of CoroNet. Then, I extracted all the sentences from the corpus that include hypemym-hyponym pairs of the list. From these extracted sentences, I collected all the sentences that contain meaningful constructions that occur systematically in the corpus. As a result, we could obtain 21 generalized patterns. Using the PERL program, we collected sentences of each of the 21 patterns. 57% of the sentences are turned out to have hyponymy relation. The proposed method in this paper is simpler and more advanced than that in Cederberg and Widdows (2003), in that using a word net or an electronic dictionary is generally considered to be efficient for information retrieval. The patterns extracted by this method are helpful when we look fer appropriate documents during information retrieval, and they are used to expand the concept networks like ontologies or thesauruses. However, the word order of Korean is relatively free and it is difficult to capture various expressions of a fired pattern. In the future, we should investigate more semantic relations than hyponymy, so that we can extract various patterns from the corpus.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.161-167
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• 2018

The purpose of this study is to investigate how the advertising campaign is spreading in social networks, and how the advertising model plays an important role in advertisement diffusion. In order to grasp the diffusion patterns of advertising, a text mining and social network analysis were conducted using the beer brand 'Kloud' as a collection keyword. After analyzing the social data for two months since the on-air of 'Good Body' advertisement, which was the first ad that "Sulhyun" appeared in. After the launch of the ad, Kloud has been mainly associated with keywords such as 'yavis & trendy style', 'beer brand', 'beer matching food', 'luxury beer drinking place', 'leisure trend', and 'SNS activity', etc. In addition, "Sul Hyun" also showed that an advertising model contributes to the spread of advertisement on social media in terms of image transition as well as brand's name and unique selling point.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.240-247
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• 2020

There have been many efforts to understand the trends of IT environments that have been rapidly changed. In a view point of management, it needs to prepare the social systems in advance by using Big-data these days. This research is for the implementation of Issue Analysis System for the Big-data based on Artificial Intelligence. This paper aims to confirm the possibility of new technology for Big-data processing through the proposed Issue Analysis System using. We propose a technique for semantic reasoning and pattern analysis based on the AI and show the proposed method is feasible to handle the Big-data. We want to verify that the proposed method can be useful in dealing with Big-data by applying latest security issues into the system. The experiments show the potentials for the proposed method to use it as a base technology for dealing with Big-data for various purposes.