• Korean Institute of Interior Design Journal
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• v.16 no.5
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• pp.115-125
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• 2007

As a part of "The method of suitable design for resident," the purpose of this study is to figure out the resident aptitude of client through "Saju-Myungrihak" more effectively and precisely. That is, the study is to find out the relations between individual character and environmental interior tendency through experimentingss to make Images based on one's individual inherent aptitude and character. For all these reasons, we proudly launch to develop the interior design program based on emotional inherent-aptitude technology for our clients with applying Nakamitch Mizuo's emotional technology thesis "the Expert System." However, a critical implication made by Nakamitch Mizuo's emotional technology, elucidates the fact that there was a problem for understanding the system of client's inherent aptitude. 1. Appearance of The inherent-aptitude in Saju-Myungrihak 2. MBTI (Myers Briggs Type Indicator) Form K 3. To strengthen and develop the tools of visual images through understanding clients' character and tendency by the result of his or her DNA (ACE, DRD4, DRD2, G-protein) test.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.27-30
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• 1997

The purpose of this study is to estimate human sensibility quantitatively under color and music stimulation and to examine the correlation between visual and auditory emotion. We measured biological signals such as EEG, ECG, skin conductance and the number of respiration in order to compare color with music sensibilities. Our result showed that red, yellow and violet color provoked active and exciting senses dominatively as dance, rock and blues music. While blue, cyan and pink color were involved in tranquil and resting emotions deeply as classic and ballade music. Our quantitative estimations of human sensibilites are useful in the design of manufactured goods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.103-110
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• 2010

TPR(Total Physical Response) is a new representative learning method for children's education. Today's approach to TPR has focused on signals from a user which becomes input data in a human-computer interaction, but the accuracy of sensing from body signals(e. g. motion and voice) isn't so perfect that it seems difficult to apply on an education system. To overcome these limits, we suggest a mediating interface device which can detect the user's motion using correct numerical values such as acceleration and angular speed. In addition, we suggest new design requirements for the mediating device through analyzing children's behavior as human factors by ethnography research and protocol analysis. As a result, we found that; children are unskilled in physical control when they use objects; tend to lean on an object unconsciously with touch. Also their behaviors are restricted, when they use objects. Therefore a mediating device should satisfy new design requirements which are make up for unskilled handling, support familiar and natural physical activity.

• Korea Science and Art Forum
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• v.25
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• pp.463-475
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• 2016

As the transition to the digital age in the late 20th century, the intrinsic value of the craft, the emotional values of human, has been noticed as an alternative to overcome the adverse effects of the modernism of the industrial age. To introduce experimental tries which convergence of contemporary technologies and elements of traditional craft, and to inspire artists and present the new possibility of creation to them who want to take advantage of craft emotion as the elements of creation is the purpose of this study in the current digital technology age. First, the meaning and value of craft in modern times and digital media and hybrid creation environments are theoretically investigated based on previous studies and literature. Second, design cases produced by combining digital technology as a tool and craft elements are classified for substantial understanding of the design. Thirdly, identify the design characteristics presented through case studies and suggest the new possibility of creation. The results of the study are as follows. Reject typical types highlight the functional role and try free express conversion, e.g. form, material, texture, making process etc. Extracts the various elements that can be applied and search combining ways, because the convergence of digital technology and the craft is sufficient to activate the human emotion. Interaction between the craft and the digital medium is made actively. Craft accepts digital form, the craft appeared again as the contents of the digital. the traditional and digital method appropriately fused and utilized depending on the situation in process.

• Tuberculosis and Respiratory Diseases
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• v.60 no.2
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• pp.171-179
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• 2006

Background : Despite the emerging danger of MDR-TB to human beings, there have only been a limited number of drugs developed to treat MDR-TB since 1970. This study investigated the cross-resistance rate between rifampicin (RFP) and rifabutin (RBU) in order to determine the efficacy of rifabutin in treating MDR-TB. In addition, the results of rifabutin were correlated with the rpoB mutations, which are believed to be markers for MDR-TB and RFP resistance. Methods : The MICs of RBU were tested against 126 clinical isolates of MDR-TB submitted to the clinical laboratory of National Masan TB Hospital in 2004. Five different concentrations ($10-160{\mu}g/ml$) were used for the MICs. The detection of the rpoB mutations was performed using a RFP resistance detection kit with a line probe assay(LiPA), which contains the oligonucleotide probes for 5 wide type and 3 specific mutations (513CCA, 516GTC, and 531TTG) The rpoB mutation was determined by direct sequencing. Results : The rate of cross-resistance between RFP and RBU was 70.5%(74/105) at $20{\mu}g/ml$ RBU(ed note: How much RFP?) Most mutations (86.3%) occurred in the 524~534 codons. The His526Gln, His526Leu, Leu533Pro, Gln513Glu, and Leu511Pro mutations(Ed note: Is this correct?) were associated with the susceptibilty to RBU. Conclusion : Based on the cross-resistance rate between RFP and RBU, RBU may be used effectively in some MDR-TB patients. Therefore, a conventional drug susceptibility test for RBU and a determination of the critical concentration are needed. However, rpoB gene mutation test may be have limited clinical applications in detecting RBU resistance.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Life Science
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• v.25 no.5
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• pp.548-556
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• 2015

Three groups of black seabream (Acanthopagrus schlegeli) were fed with treatment diets containing certain concentrations of conjugated linoleic acid (CLA) and carotenoids. The control group feed contained 0% CLA and 0% carotenoids, the CP10 group feed contained 1% CLA and 0.1% carotenoids, and the CP25 group feed contained 2.5% CLA and 0.1% carotenoids. The CP10 and CP25 groups demonstrated the enhanced growth and increased feed conversion efficiency of black seabream. The specific growth rates (SGRs) were 0.74, 0.81, and 0.97, while the feed conversion ratios (FCRs) were 2.65, 2.46, and 2.04 for the control, CP10, and CP25 groups, respectively. The total contents of high unsaturated fatty acid (HUFA) for the control, CP10, and CP25 groups were 41.0%, 41.7%, and 43.5%, respectively. CLA was deposited to the extent of 2.8% and 5.6% in the muscle, and 4.0% and 8.3% in the viscera of the CP10 and CP25 groups, respectively. Meanwhile, treatment with the viscera lipid extract (VLE) from CP25 fish evidently lowered 3T3-L1 adipocytes viability. The lipid extract from the muscle and viscera of black seabream contained ample amounts of beneficial substances, such as CLA, carotenoids, EPA, and DHA. CLA, which enriched black seabream muscle, could be categorized as a functional food and serve as a well-being food. Meanwhile, the fish oil from its viscera could serve as a high function supplement.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.3-14
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• 2015

An ad endorser's gaze direction is a salient nonverbal cue that consumers use in responding to advertisements. The gaze direction influences consumers' social perceptions (e.g., attractiveness, credibility) of the endorser and advertising effectiveness (e.g., advertising attitudes, brand attitudes). Especially, the cerebral emotional asymmetry hypothesis suggests that an ad endorser's left-averted gaze can produce more positive social perceptions and advertising effectiveness than the right-averted gaze for right-handed consumers. This study examined the effects of three gaze directions (direct, left-averted and right-averted gaze directions) of unknown female ad endorser on Korean males' advertising responses (attractiveness-, credibility- and ad-effectiveness-related responses), using online experimental method. The results indicated that the ad endorser's direct gaze was more likely to increase both positive (correspondence bias) and negative (suspicion, deceptiveness) social perceptions of her than the right-averted gaze. The direct gaze also created more positive advertising effectiveness (advertising attitudes) than the right-averted gaze. However, the study failed to find consistent differences in responses between left-averted gaze and either direct or right-averted gaze.