• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.80-92
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• 2007

For the pervasive computing in ubiquitous environment, it is very important to manage the context model to provide pertinent context knowledge to context-aware applications. The context model should be able to support efficiently the context knowledge reusing and sharing as well as reasoning. Previous works focus mainly on the context knowledge representation scheme for reasoning. This paper proposes a context knowledge modeling scheme especially for reusing and sharing. This scheme provides well-established principles and guides for 1) context knowledge modularization and hierarchization, and for 2) context knowledge identification and organization. Once the context models are built according to the scheme, the structure of the context model and the meanings of the context knowledge elements become clear and consistent, so that context-aware applications can share and reuse the context knowledge in easy and error-reduced manner. This paper also discusses the implementation of a context model and an application for Presentation Helper scenario running on a software middleware system (CAMUS) for ubiquitous service robots which is being developed by ETRI Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3211-3230
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• 2014

Recently, sparse coding has achieved remarkable success in image representation tasks. In practice, the performance of clustering can be significantly improved if limited label information is incorporated into sparse coding. To this end, in this paper, a novel semi-supervised algorithm, called constrained sparse concept coding (CSCC), is proposed for image representation. CSCC considers limited label information into graph embedding as additional hard constraints, and hence obtains embedding results that are consistent with label information and manifold structure information of the original data. Therefore, CSCC can provide a sparse representation which explicitly utilizes the prior knowledge of the data to improve the discriminative power in clustering. Besides, a kernelized version of our proposed CSCC, namely kernel constrained sparse concept coding (KCSCC), is developed to deal with nonlinear data, which leads to more effective clustering performance. The experimental evaluations on the MNIST, PIE and Yale image sets show the effectiveness of our proposed algorithms.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.43-55
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• 2008

This study investigates how learners extract their implicit knowledge into explicit form of the knowledge. The process of implicit-explicit transfer is known to help learners to reconstruct and refine their knowledge which was constructed before in some ways. Also we investigate which types of explicit form are more effective when it is delivered to other learners. In a classroom-based learning environment, students take educational content that is delivered by instructor and go through the process in which they try to fit the content into their cognitive structure by reconstructing the knowledge into their cognitive model. When they try to deliver their own cognitive model for the knowledge to other learners, they have to transform it into explicit form, and through the process, they reconstruct and refine the cognitive model of the knowledge, and find effective and appropriate way to express it. In this research, we experimented the process on a group of 77 college students and analyzed the results. We also did peer evaluated experiments to see which types of explicit format and factors are more effective than others. The results indicate that the types of explicit form of implicit knowledge play an important role in effectiveness of learning.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2075-2082
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• 1998

Rule-based knowledge representHtion is, the most popular technique for ,storage and manipulation of domain knowledge in expert system. By the way, the amount of knowledge increases more and more in this representatiun technique, it, relationship becomes complex, and even its contents can be modified. This is the reason why rule-based knowledge representation technique requires a verification ,system which can maintain consistency and completeness of knowledge base. This paper is to propose a verification system for consistency and completeness of knowledge base to promote the efficiency and reliability of expert system. After verifying the potential errors both in structure and in semantics whenever a new rule is added, this system renders knowledge base consistent and complete by correcting them automatically or by making expert correct them if it fails.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.21-31
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• 2012

The virtual reality consists of a virtual space constructed similar to the reality and agents residing in it. Our virtual space refers to an orderly space that is governed by such physical properties as mass, gravity, friction, and associated rules on top of the usual visual rendering. To construct this virtual world we are to develop virtual agents behaving like humans and the environment surrounding them. In order to improve the existing reactive agents designed to act to their designers' dictation in predetermined space or memory into autonomous agents, we need diverse kinds of knowledge among others related to the spaces for the agents to act in. Our design and implementation focuses on the spatial knowledge among those diverse aspects of knowledge required. The developed knowledge representation scheme is used on a basis for realistic and efficient physical cyber-environment, and as the knowledge structure to simulate the virtual agents' knowledges on spaces.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.410-420
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• 2001

A primitive conceptualization is defined as the set of all intended situations. A non-primitive conceptualization is defined as the set of all the pairs every of which consists of an intended knowledge system and the set of all the situations admitted by the knowledge system. The reality of a domain is considered as the set of all the situation which have ever taken place in the past, are taking place now and will take place in the future. A conceptualization is defined as precise if the set of intended situations is equal to the domain reality. The representation of various elements of a domain ontology in a model of the ontology is considered. These elements are terms for situation description and situations themselves, terms for knowledge description and knowledge systems themselves, mathematical terms and constructions, auxiliary terms and ontological agreements. It has been shown that any ontology representing a conceptualization has to be non-primitive if either (1) a conceptualization contains intended situations of different structures, or (2) a conceptualization contains concepts designated by terms for knowledge description, or (3) a conceptualization contains concept classes and determines properties of the concepts belonging to these classes, but the concepts themselves are introduced by domain knowledge, or (4) some restrictions on meanings of terms for situation description in a conceptualization depend on the meaning of terms for knowledge description.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

• Korean Institute of Interior Design Journal
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• no.11
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• pp.70-73
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• 1997

This paper explores ways to develop efficient methods to construct the web-based interior design database based on hypermedia technology. First the role of hypermedia in design was investigated. We propose the structure of knowledge representation for interior design works. Also, we discuss how a design case can be retrieved from the design database. This paper also identified further research issues such as design case retrieval from numerous design cases and components of the virtual design studio.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.435-441
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• 2008

Building Information Modelings(BIM) are more complex than typically required for early phase of the design process of a building. Construction projects have many participants from various disciplines involved throughout the entire process. Therefore the success of the project greatly depend on the efficiency of decision-making using the information generated from each process stage. This research utilized an ontology to provide an underlying structure of objects and relationships of a building. The OWL is introduced as a main vehicle to encode the information and knowledge about the building structures and spaces. A case study was conducted to develop a structured representation of an ontology where the relationships among the necessary components in the stage of preliminary design were to be automatically utilized to plan on the sizes of each room in a building and structured thoroughly with a simple structured representation.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.1
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• pp.99-117
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• 1999

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine Interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adepts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms using query generalization and specialization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically. four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual Join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.