• Journal of the Korea Concrete Institute
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• v.24 no.4
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• pp.481-490
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• 2012

Evaluation of chloride penetration is very important, because induced chloride ion causes corrosion in embedded steel. Diffusion coefficient obtained from rapid chloride penetration test is currently used, however this method cannot provide a correct prediction of chloride content since it shows only ion migration velocity in electrical field. Apparent diffusion coefficient of chloride ion based on simple Fick's Law can provide a total chloride penetration magnitude to engineers. This study proposes an analysis technique to predict chloride penetration using apparent diffusion coefficient of chloride ion from neural network (NN) algorithm and time-dependent diffusion phenomena. For this work, thirty mix proportions with the related diffusion coefficients are studied. The components of mix proportions such as w/b ratio, unit content of cement, slag, fly ash, silica fume, and fine/coarse aggregate are selected as neurons, then learning for apparent diffusion coefficient is trained. Considering time-dependent diffusion coefficient based on Fick's Law, the technique for chloride penetration analysis is proposed. The applicability of the technique is verified through test results from short, long term submerged test, and field investigations. The proposed technique can be improved through NN learning-training based on the acquisition of various mix proportions and the related diffusion coefficients of chloride ion.

• Journal of The Korean Association For Science Education
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• v.20 no.1
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• pp.20-28
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• 2000

We examined how science teachers in academic high schools perceived the 6th science curriculum and how they practiced under the curriculum. A nationwide survey was administered to obtain the responses from 402 teachers of 135 high schools. Most thought that the main themes of curriculum revision were well-embedded in the 'objectives', and that the 'content and content structure' were proper. However, they thought that the 'objectives' were not stated explicitly enough to develop teaching materials and to improve actual teaching and evaluation, and that some statements in the sections of 'method' and 'evaluation' were not proper if considered actual teachers' ability to teach inquiry and educational facilities. Many teachers also felt that the information about the curriculum was not sufficiently included at in-service teacher training programs, and that students' knowledge, attitude, and problem solving ability were not enhanced. Only few teachers were found to apply the STS approaches, reconstruct lessons, vary the structure of learning group, and develop evaluation tools with their colleagues. The lack of the practices was explained by entrance-examination-centered instruction and assessment, poor educational facilities, and lack of innovative teaching materials.

• Journal of Distribution Science
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• v.13 no.2
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• pp.55-62
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• 2015

Purpose - This study aims to investigate the success factors pertaining to K-POPs from an analysis of the internal business environment. Much research has investigated Korean Moves or how to popularize them. The research mainly focused on aspects of Korean Moves. However, few studies have attempted to examine Korean Moves or K-POPs from a managerial viewpoint. The current research tries to investigate the success factors of K-POP from strategic perspectives, specifically utilizing internal resource based view perspectives. It differentiates itself by looking at the competitiveness of K-POP from the internal resources. Research design, data, and methodology - In the entertainment industry, where creativity is heavily stressed, competitiveness is often regarded within the organization as a form of intangible asset, knowledge, or technology that is often related with the organization's personnel. Some research has tried to reveal the competitiveness of K-POP using Porter's competitiveness of nations framework. Others utilize the adapted model of Porter's structure. However, these models only look at the outside environment, and not inside a firm's resource, knowledge, or capabilities. This research utilizes the VRIO model to examine the internal resources and capabilities of K-POP producers. The model measures whether a firm's internal resources and capabilities are valuable, rare, difficult to imitate by competitors, or organizable. The research covered businesses whose yearly revenue exceeds $10 Million in music planning and recording in South Korea. There were only thirteen such companies (one percent of the total population). Of these, companies for whom 20 percent or more of the sales revenue comes from the abroad are targeted. Only seven are selected and these participated in the research. In order to find a firm's internal resources, we conducted qualitative research methodology. Their business names and persons who participated in this research are not revealed due to case sensitive issues. Instead, we use unrelated initials for their names and their statements. Results - From the in-depth interview with top-tier K-POP producers and managers, the current research tried to identify resources and capabilities that helped to strengthen their competitiveness. These resources and capabilities are sought from the scope of the VRIO model, which looks at the internal resources and capabilities from the scope of value, rarity, imitability, and organization. Interviews with the top tier producers and managers reveal the internal success factors of K-POPs. We conclude that these resources and capabilities are from internally accumulated producing know-how, unique managing (training) system, and outstanding all-round entertainment capabilities of the performers. Conclusions - These results indicate that the core resources and capabilities of K-POP are robust. It will take a significant amount of time and money to imitate for followers, because these resources and capabilities are the result of time investment and are embedded into producers' and performers' know-how. Taking Luo (2000)'s argument, K-POP is in the second stage of the globalization process, which is configuring and allocation resource capabilities to a global scope.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.411-418
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• 2009

Vector quantization algorithm based on fuzzy clustering has been widely used in the field of data compression since the use of fuzzy clustering analysis in the early stages of a vector quantization process can make this process less sensitive to its initialization. However, the process of fuzzy clustering is computationally very intensive because of its complex framework for the quantitative formulation of the uncertainty involved in the training vector space. To overcome the computational burden of the process, this paper introduces an array architecture for the implementation of fuzzy vector quantization (FVQ). The arrayarchitecture, which consists of 4,096 processing elements (PEs), provides a computationally efficient solution by employing an effective vector assignment strategy during the clustering process. Experimental results indicatethat the proposed parallel implementation providessignificantly greater performance and efficiency than appropriately scaled alternative array systems. In addition, the proposed parallel implementation provides 1000x greater performance and 100x higher energy efficiency than other implementations using today's ARMand TI DSP processors in the same 130nm technology. These results demonstrate that the proposed parallel implementation shows the potential for improved performance and energy efficiency.

• Journal of vocational education research
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• v.30 no.4
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• pp.281-300
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• 2011

This study tried to look into what are happening in the 'class for the talented in invention' using COS-R developed by VanTassel-Baska. Teaching and learning activities within the classroom were observed and analyzed in terms of teacher's observation and teacher's observation, respectively. Based on results of this study, conclusions are as follows. First, it was founded that there are some commonalities between teacher observations and student observations. Based on teacher observations, differentiated teaching activities considering individual characteristics are rarely observed, and for students, it was true. Therefore, supplying a special training program for teachers are needed in order to make teachers and students engage in changing their teaching and learning behaviors. Second, on the side of teachers, they usually emphasize the importance of curriculum planning and implementation, problem solving, creative thinking et al. However, they barely stress the characteristics of research methods, critical thinking, and considering individual characteristics and the level of intellectual ability. Third, on the side of students, they frequently respond to solving problems and critical thinking at the same degree. On the other hand, systemic efforts of considering individual differences and adapting to them have been less regarded in both teaching and learning. In sum, for the successful 'Invention gifted classroom', establishing an educational environment to consider individually guided instruction and taking a balance among various factors embedded in teaching and learning situation should be required.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.575-596
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• 2012

Students' perception on a science program for gifted was investigated. The whole program was designed in consistency and integrity based on the Autonomous Learner Model suggested by Betts & Kercher(1999). 7th, 8th and 9th grade students were enrolled in this program, offered by G Education Institute for Gifted(GEI) located in Seoul. A survey was done to ask students' perception regarding the effect of the program. The survey consisted of statements about the expected effects of the program and students were asked if they agreed with the statements. Most students strongly agreed that GEI's program has positive effects. Students replied that they learned useful and interesting science contents, enjoyed meaningful experience of cooperating with members in small groups, and were challenged by the inquiry tasks. They recognized that they were being trained to become autonomous learners. They also said that their choices and decisions were respected, which resulted in positive effects on their ability to negotiate or to inquire actively. These implies that Autonomous Learner Model had been successfully applied. Although it was not clear autonomy of students was fully grown, the possibility of becoming an autonomous learner was evident. Satisfaction level is higher for the older students, implying that the integrity in the program gave accumulating effect. Students response showed that three sub-programs of GEI, the classes of each subject, conference at the end of the year and autonomous learner training played equally important role for students to learn the process of scientific inquiry and autonomous learning. This was a positive sign that the strategies for scientific inquiry and autonomous learning were embedded and integrated deeply in the program. The results of current research suggests that the integrity of a program based on a specific education model for the gifted could provide better education environment for the gifted students.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.