• Korean Journal of Child Studies
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• v.33 no.5
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• pp.19-35
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• 2012

The purposes of this study were to investigate how children's categorization differs in object categorical hierarchy and to examine whether these aspects were different according to the children's age of 18 months, 24 months, 30 months. The participants consisted of 120 young children aged 18 months, 24 months, and 30 months from 31 child-care centers located in middle-income regions of Seoul and Kyonggi Province. The major findings were as follows : First, all the children from all three age groups could consistently differentiate the superordinates; however, they could not consistently differentiate basic categories. Second, 24 month appears to be a critical change period in category development. Third, as the children become older, they are able to acquire more knowledge regarding categories. These results suggested that the advent of ordering, in terms of basic categories as well as superordinates which occurred around the age of 24 month, was confirmed in category development.

• Journal of Fisheries and Marine Sciences Education
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• v.6 no.2
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• pp.198-216
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• 1994

The problem of promoting instructional effect using reorganizing the content of textbook is one of the major concerns of many education theorists and teachers. The results of many researches about above problem reveal that reorganizing the content of textbook promotes the ability of recall and problem solving of learners. The content structure of current navigation textbook revealed a categorical structure as its basic framework, though it seems to be a poor one. A categorical structure is known as providing an inferior information processing mechanism for learners than a learning hierarchy content structure is. Furthermore current content structure hasn't given any considerations to navigation in practice, spatial contexts and sequential events of ships from a harbor to another harbor. The learning hierarchy content structure has an advantage of giving learners more systematic and stronger knowledge networks than a categorical structure.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.87-96
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• 2006

Most conventional database systems support specific queries that are concerned only with data that match a query qualification precisely. A cooperative query answering supports query analysis, query relaxation and provides approximate answers as well as exact answers. The key problem in the cooperative answering is how to provide an approximate functionality for alphanumeric as well as categorical queries. In this paper, we propose a metricized knowledge abstraction hierarchy that supports multi-level data abstraction hierarchy and distance metric among data values. In order to facilitate the query relaxation, a knowledge representation framework has been adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. The numeric domains also compatibly incorporated in the knowledge abstraction hierarchy by calculating the distance between target record and neighbor records.

• The Journal of Information Systems
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• v.16 no.3
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.33-41
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• 2015

Since bitmap indices are useful for OLAP queries over low-cardinality data columns, they are frequently used in data warehouses. In many data warehouse applications, the domain of a column tends to be hierarchical, such as categorical data and geographical data. When the domain of a column is hierarchical, hierarchical bitmap index is able to significantly improve the performance of queries with conditions on that column. This strategy, however, has a limitation in that when a large scale hierarchy is used, building a bimamp for each distinct node leads to a large space overhead. Thus, in this paper, we introduce the way to build hierarchical bitmap index on an attribute whose domain is organized into a large-scale hierarchy in space-constrained environments. Especially, in order to figure out space overhead of hierarchical bitmap indices, we propose the cut-selection strategy which divides the entire hierarchy into two exclusive regions.

• MALSORI
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• no.33_34
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• pp.1-10
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• 1997

Korean stop consonants are well known for their tripple distinction. In word initial position lax, tense and aspirated consonants are all voiceless. They are differentiated by the degree of tension, aspiration and VOT(voice onset time). But in intervocalic position, lax consonants become voiced. In this study I compare the acoustic features of Korean intervocalic lax and tense stops. The closure duration of lax stops is shorter than that of tense consonants. The preceding vowel length is longer in tan than that in tense consonants. I modify the above acoustic characteristics by an experimental methods. For example, I shorten the closure duration of intervocalic tense stops by 5 steps. r also do auditory tests which will show us listener's reaction on the above examples. And do the same job with the preceding vowels. According to the auditory test, the closure duration does an important role in differentiating Korean intervocalic lax and tense stops. But the preceding vowel length has almost nothing to do with the distinction between lax and tense stops. So I conclude that acoustic features also have hierarchy. Some features have categorical characteristics and others don't.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.