• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.70-78
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• 2020

Nitrox diving was introduced by the NOAA (National Oceanic and Atmospheric Administration) to increase the oxygen content and lower the nitrogen content in respiratory gases. The commercial diving sector specializing in underwater operations has recently introduced regulations on the use of Nitrox. Because the respiratory gas for Nitrox diving has a lower nitrogen content than the normal air, the amount of nitrogen dissolved in the body is small, which not only significantly reduces the decompression time compared to air diving, but also reduces the chance of exposure to decompression sickness. In this study, we applied the VPM (Varying Permeability Model) algorithm to virtual diving with air and Nitrox as a respiratory gas, respectively, to study the optimal Nitrox diving for the safety at the underwater works. The results showed that Nitrox diving had a longer NDL (No-Decompression Limit), a much shorter depression time. In other words, Nitrox diving in underwater works is safer from decompression sickness than commonly used air diving.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.64-79
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• 2017

This study was to evaluate the suitability of scientific questions as qualification examination for high school graduation by content validity and item response with three examinees and two preliminary examinees. As a result, scientific questions were concentrated on two units of six units of total, and application appeared to be lacking problem area by 8% compared to understanding and application. Examinees and preliminary examinees chose correct answers most by hap or guess, sometimes by experience or common sense, and the least by scientific concept. In addition, they could chose correct answers by hap or guess because there were words that implied the correct answer in questions and answers, or because they could compared and/or analyzed questions and answers. With these results, two proposals were suggested as follows; (1) scientific questions of qualification examination for high school graduation should measure basic scientific literacy. (2) specific criteria for science literacy in qualification examination for high school graduation should be set.

• Journal of Digital Contents Society
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• v.8 no.1
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• pp.41-50
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• 2007

People access web contain via PC and many other devices. In other words, not only they access information by a PC connected internet, but also they get information through a mobile phone, a PDA even D-TV. In this article, to resolve the problem, we suppose new web caching mechanism called 'SOP(Shared Object Page)'based on applying of meta data of web page information and storing adapted objects.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.387-408
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• 2010

This study examined the effects of emotional content on the encoding process of emotional stimuli and the effects of emotional context on those of neutral stimuli. It was examined whether the superior memory of emotional stimuli is due to attentional resource allocation. This study were performed an emotional picture and a neutral word were presented in succession at every trials. The results of recognition judgement showed superior memory of emotional pictures than neutral pictures, but showed poorer memory of neutral words in emotional context than those in neutral context. LPC(Late Positive Complex) of ERP results showed the similar pattern: higher amplitude by emotional pictures than neutral pictures, and lower amplitude by neutral words in emotional context than those in neutral context. This result is considered to support attention allocation hypothesis.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.41-49
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• 2012

The purpose of this paper is to extract the vowel formants of the ten young male speakers from the Buckeye Corpus of Conversational Speech [1] and to analyze them in comparison to earlier works in terms of various phonetic factors that are expected to affect the realization of the formant distribution. The first two formant frequency values were automatically extracted with a Praat script along with such factors as the place of articulation, the content versus function word information, syllabic stress information, the location in a word, location in utterance, speech rate of three consecutive words, and the word frequency in the corpus. The results indicated that the formant patterns from the corpus were very different from those of earlier works although the overall pattern was similar and that the factors were strongly responsible for the realization of the two formants. The purpose of this paper is to extract the vowel formants of the ten young male speakers from the Buckeye Corpus of Conversational Speech [1] and to analyze them in comparison to earlier works in terms of various phonetic factors that are expected to affect the realization of the formant distribution. The first two formant frequency values were automatically extracted with a Praat script along with such factors as the place of articulation, the content versus function word information, the syllabic stress information, the location in a word, the location in an utterance, the speech rate of the three consecutive words, and the word frequency in the corpus. The result indicated that the formant patterns from the corpus were very different from those of earlier works although the overall pattern was similar and that the factors were strongly responsible for the realization of the two formants.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.690-696
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• 2016

Search engines that rely on morphological matching of user query and web document content do not support individual interests. This research proposes a personalized web search scheme that returns the results that reflect the users' query intent and personal preferences. The performance of the personalized search depends on using an effective user profiling strategy to accurately capture the users' personal interests. In this study, the user profiles are the databases of topic words and customized weights based on the recent user queries and the frequency of topic words in click history. To determine the precise meaning of ambiguous queries and topic words, this strategy uses WordNet to calculate the semantic relatedness to words in the user profile. The experiments were conducted by installing a query expansion and re-ranking modules on the general web search systems. The results showed that this method has 92% precision and 82% recall in the top 10 search results, proving the enhanced performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.43-48
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• 2019

While reading text-only documents and finding unknown words, readers will become the focus disturbed and not be able to understand the content of the documents. Because children have little experience, it is difficult to understand correctly if the description in context is unfamiliar or ambiguous. In this paper, in order to help understand the text and increase the interest of the readers, we analyze the texts of documents and select the contents that are considered important, and implement a system that displays the most relevant images automatically from the web and links the texts and the images together. The implementation of the system divides the article into paragraphs, analyzes the text, selects important sentences for each paragraph and the important words that best represent the meaning of the important sentences, searches for images related to the words on the web, and then links the images to each of the previous paragraphs. Experiments have shown how to select important sentences and how to select important words in the sentences. As a result of the experiment, we could get 60% performance by evaluating the accuracy of the relation between three selected images and corresponding important sentences.

• Journal of Korea Trade
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• v.24 no.8
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• pp.171-188
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• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.317-322
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• 2010

This paper proposes how to improve performance of the Korean document sentiment-classification system using semantic properties of the sentiment words. A sentiment word means a word with sentiment, and sentiment features are defined by a set of the sentiment words which are important lexical resource for the sentiment classification. Sentiment feature represents different sentiment intensity in general field and in specific domain. In general field, we can estimate the sentiment intensity using a snippet from a search engine, while in specific domain, training data can be used for this estimation. When the sentiment intensity of the sentiment features are estimated, it is called semantic orientation and is used to estimate the sentiment intensity of the sentences in the text documents. After estimating sentiment intensity of the sentences, we apply that to the weights of sentiment features. In this paper, we evaluate our system in three different cases such as general, domain-specific, and general/domain-specific semantic orientation using support vector machine. Our experimental results show the improved performance in all cases, and, especially in general/domain-specific semantic orientation, our proposed method performs 3.1% better than a baseline system indexed by only content words.