• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.1-12
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• 2000

Model checking is a formal verification technique which checks the consistency between a requirement specification and a behavior model of the system by explorating the state space of the model. We apply model checking to the formal verification of the concurrent object-oriented system, using an existing model checker SPIN which has been successful in verifying concurrent systems. First, we propose an Actor-based modeling language, called APromela, by extending the modeling language Promela which is a modeling language supported in SPIN. APromela supports not only all the primitives of Promela, but additional primitives needed to model concurrent object-oriented systems, such as class definition, object instantiation, message send, and synchronization.Second, we provide translation rules for mapping APromela's such modeling primitives to Promela's. As an application of APromela, we suggest a verification method for UML models. By giving an example of specification, translation, and verification, we also demonstrate the applicability of our proposed approach, and discuss the limitations and further research issues.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2840-2848
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• 1997

Recognition of 3D objects using computer vision is complicated by the fact that geometric features vary with view orientation. An important factor in designing recognition algorithms in such situations is understanding the variation of certain critical features. The features selected in this paper are the angles between landmarks in a scene. In a class of polyhedral objects the angles at certain vertices may form a distinct and characteristic alignment of faces. For many other classes of objects it may be possible to identify distinctive spacial arrangements of some readily identifiable landmarks. In this paper given an isotropic view orientation and an orthographic projection the two dimensional joint density function of two angles in a scene is derived. Also the joint density of all defining angles of a polygon in an image is derived. The analytic expressions for the densities are useful in determining statistical decision rules to recognize surfaces and objects. Experiments to evaluate the usefulness of the proposed methods are reported. Results indicate that the method is useful and powerful.

• Journal of Korean language education
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• v.29 no.3
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• pp.325-344
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• 2018

The purpose of this study is to summarize PWIM (Picture Words Inductive Model) which is one of learner-centered vocabulary teaching-learning models, and suggest ways to implement them in Korean language education. The pictures that are used in the Korean language education field help visualize the specific shape, color, and texture of the vocabulary that is the learning target; thus, helping beginner learners to recognize the meaning of the sound. Visual material stimulates the intrinsic schema of the learner and not only becomes a 'bridge' connecting the mother tongue and the Korean language, but also reduces difficulty in learning a foreign language because of the ambiguity between meaning and sound in Korean and all languages. PWIM shows commonality with existing learning methods in that it uses visual materials. However, in the past, the teacher-centered learning method has only imitated the teacher because the teacher showed a piece-wise, out-of-life photograph and taught the word. PWIM is a learner-centered learning method that stimulates learners to find vocabulary on their own by presenting visual information reflecting the context. In this paper, PWIM is more suitable for beginner learners who are learning specific concrete vocabulary such as personal identity (mainly objects), residence and environment, daily life, shopping, health, climate, and traffic. The purpose of this study was to develop a method of using PWIM suitable for Korean language learners and teaching procedures. The researchers rearranged the previous research into three steps: brainstorming and word organization, generalization of semantic and morphological rules of extracted words, and application of words. In the case of PWIM, you can go through all three steps at once. Otherwise, it is possible to divide the three steps of PWIM and teach at different times. It is expected that teachers and learners using the PWIM teaching-learning method, which uses realistic visual materials, will enable making an effective class together.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.393-400
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• 2007

We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Korean Journal of Child Education & Care
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• v.19 no.3
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• pp.141-156
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• 2019

Objective: This study examined the gossip, an evaluative conversation about an absent third party, through qualitative research methods, and explored the subjects and the social function of gossip among young children. Methods: The subject of this study included 24 five-year-olds children in Somang class at Baram kindergarten in Seoul. The data consisted of 20 participant observation, 2 in-depth interviews with the teacher, and informal interviews with the children. Results: The subjects of gossip among young children were peers, teachers, and family members. The social function of gossip among children was strengthening peer relationship, selecting peers, confirming rules, and pleasure. The results of this study confirmed that children are sensitive observers of their surroundings and that their peers, teachers, and families are important beings with influence in their lives. It also showed that children's gossip was a social conversation in which children build peer relationships, learn norms and experience pleasant emotions. Conclusion/Implications: This study has the significance of providing various perspectives on the socialization process of young children by looking at gossip which was perceived as a negative image, from a new perspective.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.317-325
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• 2021

By analyzing the recognition analysis and motivation method of the determinants, this study investigates the future development direction of health exercise biodata analysis face-to-face practice education content. The participants were 40 millennial and zoomers (MZ) generation college graduates. Factors related to the decision to participate in face-to-face practice classes in the field of health exercise biodata and bio-digital content convergence technology in the era of COVID-19 were measured. Of the participants, 67.5% voluntarily decided to participate in small group classes while observing social distancing rules. This study presented the most effective and learning motive methods to participate in face-to-face training. Health exercise biodata needs improvement in terms of integrating with adjacent disciplines such as big data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.107-114
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• 2022

Delegation is a powerful mechanism that allocates access rights to users to provide flexible and dynamic access control decisions. It is also particularly useful in a distributed environment. Among the representative delegation models, the RBDM0 and RDM2000 models are role delegation as the user to user delegation. However, In RBAC, the concept of inheritance of the role class is not well harmonized with the management rules of the actual corporate organization. In this paper, we propose an Adding Attributes on Permission-Based Delegation Model (ABDM) that guarantees the permanence of delegated permissions. It does not violate the separation of duty and security principle of least privilege. ABDM based on RBAC model, supports both the role to role and user to user delegation with an attribute. whenever the delegator wants the permission can be withdrawn, and A delegator can give permission to a delegatee.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.103-109
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• 2023

In Korea, software education is implemented from elementary school. As a representative software education tool for elementary schools, various chess games reconstructed based on the rules of Western chess games are being used. On the other hand, Yutnori, one of our traditional games, also includes elements of software education, so research on this is needed. Therefore, in this study, a Yutnori program based on computational thinking using an educational programming language, Entry, and a turtle robot was developed and its validity verified. As a result of the validity verification, the CVR value was higher than 0.7 in the degree of agreement with the subject achievement standard (3 questions), the appropriateness of learning materials (4 questions), and the possibility of class application (3 questions). Therefore, it could be judged that the learning program developed in this study has a high level of agreement with the subject achievement standards, appropriate learning materials, and high possibility of being applied to classes. In order to generalize this content in the future, the effectiveness will need to be verified, and experimental research will be needed to understand this.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.77-87
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• 2023

Applying various association rule mining algorithms to the network intrusion detection task involves two critical issues: too large size of generated rule set which is hard to be utilized for IoT systems and hardness of control of false negative/positive rates. In this research, we propose an association rule mining algorithm based on the newly defined measures called coverage and exclusion. Coverage shows how frequently a pattern is discovered among the transactions of a class and exclusion does how frequently a pattern is not discovered in the transactions of the other classes. We compare our algorithm experimentally with the Apriori algorithm which is the most famous algorithm using the public dataset called KDDcup99. Compared to Apriori, the proposed algorithm reduces the resulting rule set size by up to 93.2 percent while keeping accuracy completely. The proposed algorithm also controls perfectly the false negative/positive rates of the generated rules by parameters. Therefore, network analysts can effectively apply the proposed association rule mining to the network intrusion detection task by solving two issues.