• Journal of The Korean Association For Science Education
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• v.37 no.5
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• pp.825-833
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• 2017

The Korean Ministry of Education has emphasized human resource development with creative and convergent ability for future science and technology development. Korean STEAM Education aims to enhance students' interest and their understanding of science and technology as well as to develop students' creative problem-solving skills. Through STEAM R&E project, students experience self-directed research in order to solve the problem in the context of everyday life. In this study, we aim to find out whether the creative leader competency of high school students changed after they experienced the STEAM R&E project. The creative leader competency consisted of three domains: cognitive, affective, and societal domain. We measured the creative leader competency using the questionnaire scales. The questionnaire was administered to 612 high school students who participated in the 2016 STEAM R&E project. Pre- and post- test scores were collected, and we analyzed it. We compared the mean difference between pre- and post- test scores as well as the mean differences among science high school, gifted school, science core school, and general high school. From the result, we found that all student' creative leader competency improved after participating in the STEAM R&E project in all three domains. The result also showed that students' test scores of science high school and gifted school showed no significant mean differences, while student's scores of both science core school and general high school improved significantly. From the results, we concluded that STEAM R&E activities could be an effective tool in cultivating creative leader competency, especially for general high school students and science core school students. We also suggested that further researches are needed to find how we could enhance students' creative leader competency.

• The Journal of the Petrological Society of Korea
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• v.20 no.1
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• pp.23-37
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• 2011

The scaling properties on the length distribution of microcrack populations from Tertiary crystalline tuff are investigated. From the distribution charts showing length range with 15 directional angles and five groups(I~V), a systematic variation appears in the mean length with microcrack orientation. The distribution charts are distinguished by the bilaterally symmetrical pattern to nearly N-S direction. The whole domain of the length-cumulative frequency diagram for microcrack populations can be divided into three sections in terms of phases of the distribution of related curves. Especially, the linear middle section of each diagram of five groups represents a power-law distribution. The frequency ratio of linear middle sections of five groups ranges from 46.6% to 67.8%. Meanwhile, the slope of linear middle section of each group shows the order: group V($N60{\sim}90^{\circ}E$, -2.02) > group IV($N20{\sim}60^{\circ}E$, -1.55) > group I($N60{\sim}90^{\circ}W$, -1.48), group II($N10{\sim}60^{\circ}W$, -1.48) > group III($N10^{\circ}W{\sim}N20^{\circ}E$, -1.06). Five sub-populations(five groups) that closely follow the power-law length distribution show a wide range in exponents( -1.06 - -2.02). These differences in exponent among live groups emphasizes the importance of orientation effect. In addition, breaks in slope in the lower parts of the related curves represent the abrupt development of longer lengths, which is reflected in the decrease in the power-law exponent. Especially, such a distribution pattern can be seen from the diagram with $N10{\sim}20^{\circ}E,\;N10{\sim}20^{\circ}W$ and $N60{\sim}70^{\circ}W$ directional angles. These three directional angles correspond with main directions of faults developed around the study area. The distribution chart showing the individual characteristics of the length-cumulative frequency diagrams for 15 directional angles were made. By arraying above diagrams according to the categories of three groups(A, B and C), the differences in length-frequency distributions among these groups can be easily derived. The distribution chart illustrates the importance of analysing microcrack sets separately. From the related chart, the occurrence frequency of shorter microcracks shows the order: group A > group B > group C. These three types of distribution patterns could reveal important information on the processes occurred during microcrack growth.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1089-1121
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• 2017

'Technological convergence' is the recent innovation trend which facilitates to solve social crux as well as to generate new industries. Korean government research institutes (GRIs) have taken a pivotal role for economic growth which capitalized on technology-oriented strategies. Recently, the policy interests on the transition of their role and mission towards multidisciplinary research organization is increasingly shed lights. This study regards the collaborative activities as one of the key success factors in the multidisciplinary research. In this sense, this study sets research purposes as follows: First, we intend to define a concept and to confine a scope of multidisciplinary research from the view point of R&D purposes and problem-solving process. Second, we categorize the collaboration and the relevant performances which reflect the characteristics of the multidisciplinary research. Third, we analyze the characteristics of collaborative activities and the effects of strength on the research performances. To this end, this study conducted a survey of 104 research project directors, which have experienced at least one of two types of multidisciplinary research projects through National R&D project or NST (National Research Council of Science & Technology) convergence research project. Then, we conducted regression analysis by utilizing the survey results in order to verify the relation between the collaborative activities and the performances. As results of analyses, first, the diversification of collaboration partners was a salient factor in the process of knowledge creation. Second, collective works among the researchers in similar area and domain enhanced mission-oriented technology development projects such as patent creation or technology transfer. Third, we verified that the diversity of created knowledge and the degree of relation continuity between researchers increased in the condition of guaranteeing individual researcher's independence and autonomy as well as sharing various technological capabilities. These results provide the future policy directions related to the methods to measure the collaboration and performance analysis for multidisciplinary research.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.85-94
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• 2009

This study examines the operational effectiveness of a Convection Current Aeration System (CCAS) in reservoir. CCAS was run from June, 2008 when the thermocline begun forming in the reservoir. This paper reviews the influence of stratification, dissolved oxygen dynamics and temperature in the lake's natural state from June to October 2008. The survey was done on a week basis. Upwelling flow effects a radius of $7{\sim}10m$ at a surface directly and was irrelevant to the strength of thermocline. On the other hand, it was affected the number of working days, and strength of thermocline at vertical profiles of the reservoir. Longer CCAS run, the deeper was the vertical direct flow area. However it didn't break the thermocline during summer season of 2008. The operating efficiency of the CCAS in the reservoir depends on hydraulics and meteological conditions. Computational Fluid Dynamics (CFD) is a very useful tool for evaluating the operating efficiency of fluid dynamics. The geometry for CFD simulation consists of a cylindrical vessel 25 m radius and 40 m height. The CCAS is located in center of domain. The non-uniform tetrahedral meshes had a bulk of the geometry. The meshes ranged from the coarse to the very fine. This is attributed to the cold water flowing into the downcomer and rising, creating a horizontal flow to the top of the CCAS. The result of CFD demonstrate a closer agreement with surveyed data for temperature and flow velocity. Theoretical dispersion volume were calculated at 8m depth, 120 m diameter working for 30 days and 10 m depth, 130 m diameter working for 50 days.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of The Korean Ophthalmological Society
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• v.59 no.12
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• pp.1152-1159
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• 2018

Purpose: We prospectively investigated clinical changes and long-term outcomes after administration of the drugs recommended by the Age-Related Eye Disease Study-2 to patients with intermediate age-related macular degeneration (AMD). Methods: This prospective multicenter study enrolled 79 eyes of 55 patients taking lutein and zeaxanthin. The primary endpoint was contrast sensitivity; this was checked every 12 months for a total of 36 months after treatment commenced. The secondary endpoints were visual acuity, central macular thickness, and drusen volume; the latter two parameters were assessed using spectral domain optical coherence tomography. Results: The mean patient age was $72.46{\pm}7.16years$. Contrast sensitivity gradually improved at both three and six cycles per degree. The corrected visual acuity was $0.13{\pm}0.14logMAR$ and did not change significantly over the 36 months. Neither the central macular thickness nor drusen volume changed significantly. Conclusions: Contrast sensitivity markedly improved after treatment, improving vision and patient satisfaction. Visual acuity, central retinal thickness, and drusen volume did not deteriorate. Therefore, progression of AMD and visual function deterioration were halted.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.42 no.2
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• pp.103-109
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• 2016

Efficacy of cosmetics has been mainly evaluated by qualitative and quantitative methods based on visual sense, tactile sense and skin structure until now. In this study, we suggested a novel evaluation method for skin status based on sound; measuring and analyzing the rubbing noise generated by applying cosmetics. First, the rubbing noise was measured at a close range by a high-sensitivity microphone in anechoic environment, and the noises were analyzed by 1/3 octave band analysis in frequency-domain. Three conditions, 1) before washing, 2) after washing and 3) after application of cosmetics, were compared. As a result, sound pressure level (SPL) of rubbing noise after washing was larger than that of before washing, and the SPL of rubbing noise after cosmetic application was the smallest. Furthermore, the energy of rubbing noise after application was higher than that of the before and after washing conditions in a low frequency band (lower than 2 kHz region). Conversely, the energy of rubbing noise after application was much lower than the others in a high-frequency band (upper than 2 kHz region). This change of energy distribution was described as a balloon-skin model. High SPL in the low frequency region after the cosmetic applications was due to the increase of "flexibility index", while SPL in the high frequency region significantly decreased because of the attenuation which is related to "softness index". Therefore, we developed two indices based on the spectrum-energy difference for evaluating skin conditions. This proposed method and indices were verified via skin flexibility and roughness measurement using cutometer and primos respectively. These results suggest that acoustic measurement of skin friction noise may be a new skin status evaluation method.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.3
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• pp.134-144
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• 2003

A rhizobacterium Pseudomonas cholororaphis O6 produced several secondary metabolites, such as phenazines, protease, and HCN that may be involved in inhibition of the growth of phytopathogenic fungi. In field study, P. chlororaphis O6 treatment on wheat seed suppressed root rot disease caused by Fusarium culmorum. The major organic acids of cucumber root exudates were fumaric acid, malic acid, benzoic acid, and succinic acid. Glucose and fructose were major monosaccharides in cucumber root exudates. The total amount of organic acids was ten times higher than that of the sugars. P. chlororaphis O6 grew well on cucumber root exudates. The dctA gene of P. chlororaphis O6 consisted of a 1,335 bp open reading frame with a deduced amino acid sequence of 444 residues, corresponding to a molecular size of about 47 kD and pI 8.2. The deduced dctA sequence has ten putative transmembrane domains, as expected of a membrane-embedded protein. Our results indicated that organic acids in cucumber root exudates may play an important role in providing nutrient source for root colonization of biological control bacteria, and the dctA gene of P. chlororaphis O6 may be an important bacterial trait that is involved in utilization of root exudates.