• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2989-3000
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• 1999

Until now, most Intelligent Tutoring Systems are lacking in the modularity, the extensibility of the system, and the flexibility in the dynamic environment due to the static exchanges of knowledge among modules. To overcome these flexibility in the dynamic due to the static exchanges of knowledge among modules. To overcome these problems, we will suggest, in this paper, a Distance Intelligent Tutoring Framework, called DELFOM, based on the multiagent to cope with the various and complicated learner's requests. We could make different types of learning systems by simply changing the contents of DELFOM External that is variant part of DELFOM. This framework, therefore, provides software reuse and the extensibility based on object-oriented paradigm. And we will propose two different distance learning systems using DELFOM. Therefore this framework gives the developer/the learner the effective and easy development/learning environment. DELFOM is implemented using CORBA and Java for the network transparency and platform independence.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.480-483
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• 2003

We propose a new framework that aims at multi-purpose image recognition, a difficult task for the conventional rule-based systems. This framework is farmed based on the idea of computer-based learning algorithm. In this research, we introduce the new functions of an additional learning and a knowledge reconstruction on the Fuzzy Inference Neural Network (FINN) (1) to enable the system to accommodate new objects and enhance the accuracy as necessary. We examine the capability of the proposed framework using two examples. The first one is the capital letter recognition task from UCI machine learning repository to estimate the effectiveness of the framework itself, Even though the whole training data was not given in advance, the proposed framework operated with a small loss of accuracy by introducing functions of the additional learning and the knowledge reconstruction. The other is the scenery image recognition. We confirmed that the proposed framework could recognize images with high accuracy and accommodate new object recursively.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.27-41
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• 2000

As the application domains of rule-based systems become larger and more complicated, the integration of rule-based systems within the database systems has become the topic of many research works. This paper suggests a simulation modeling using expert system and database. The integration methods employed in this research are as follows. First, we defined new states and state transition functions to interrelate simulation model and expert system. Second, we designed and implemented FCL(Fact Class Library) as a interface of expert system and database. FCL has facilities of filtering data from database, and assigning a meaning to the filtered data. Also, FCL detects the violation of the integrity rules in database, as the result of inference is reflected. Some implementation problems are pointed out and the methods to solve these problems are discussed in this paper, We developed a simulation model of the grating production line and executed it to validate the functions of the proposed method.

• Management & Information Systems Review
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• v.14
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• pp.181-193
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• 2004

This paper is concerned with the study of 'Knowledge Management', a paradigm of thought based on the idea that the future will be a knowledge-centered society. This paper also provides an overview of both 'what to manage' and 'how to manage' if an organization wants to develop in this environment. The process of 'Knowledge Management' begins with a decision. Presented with a myriad of information, of which we can't afford to obtain it all, what information is of value to the organization? Further, by systematically organizing not only the primary information but also that which was accumulated in the process, Knowledge Management is capable of suggesting a value-added plan of action. This research presents the necessary principles needed in order to successfully use Knowledge Management, specifically a harmony between the human and technological aspects. There cannot be a static solution to the problem, as the environment of the enterprise's problem is dynamic in nature. Throughout the process, the enterprise will constantly change its strategy and structure in order to adapt to variable knowledge, technology, strategy, regulations, consumer interest, etc... The essential constant of these problems is the importance of spending on education. Investments in educations help develope a infrastructure for Information Technology, the development of knowledge distribution strategies, and the creation of efficient Knowledge Management policy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• Industrial Engineering and Management Systems
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• v.13 no.3
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• pp.258-266
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• 2014

This paper proposes a management system for the educational program of production managers on the basis of value co-creation by the learner and the instructor. The program combines an intelligent knowledge-based approach with the kaizen activity program. The program helps individuals acquire knowledge and skills to ensure the total rather than the partial optimization of processes and operations facilitating continuous improvement in the workplace. To achieve these goals, the program uses models of a learning process and a swing of enlightenment. In addition, the program is supported by a framework of academic, business people, consultants, and government officers. The program was developed using an instructional design approach. This paper discusses the process of developing and managing the educational program between 2006 and 2012 as well as the results obtained.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.88-96
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• 1996

Computer vision-based gesture recognition systems consist of image segmentation, object tracking and decision. However, it is difficult to segment an object from image for gesture in computer systems because of vaious illuminations and backgrounds. In this paper, we describe a method to learn features for segmentation, which improves the performance of computer vision-based hand-gesture recognition systems. Systems interact with a user to acquire exact training data and segment information according to a predefined plan. System provides some models to the user, takes pictures of the user's response and then analyzes the pictures with models and a prior knowledge. The system sends messages to the user and operates learning module to extract information with the analyzed result.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.689-696
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• 2001

Large-scale Multi-disciplinary Systems(LMS) such as transportation, aerospace, defense etc. are complex systems in which there are many subsystems, interfaces, functions and demanding performance requirements. Because many contractors participate in the development, it is necessary to apply methods of sharing common objectives and communicating design status effectively among all of the stakeholders. The processes and methods of systems engineering which includes system requirement analysis; functional analysis; architecting; system analysis; interface control; and system specification development provide a success-oriented disciplined approach to the project. This paper shows not only the methodology and the results of model-based systems engineering to Automated Guided Transit(AGT) system as one of LMS systems, but also propose the extension of the model-based tool to help manage a project by linking WBS (Work Breakdown Structure), work organization, and PBS (Product Breakdown Structure). In performing the model-based functional analysis, the focus was on the operation concept of an example rail system at the top-level and the propulsion/braking function, a key function of the modern automated rail system. The model-based behavior analysis approach that applies a discrete-event simulation method facilitates the system functional definition and the test and verification activities. The first application of computer-aided tool, RDD-100, in the railway industry demonstrates the capability to model product design knowledge and decisions concerning key issues such as the rationale for architecting the top-level system. The model-based product design knowledge will be essential in integrating the follow-on life-cycle phase activities. production through operation and support, over the life of the AGT system. Additionally, when a new generation train system is required, the reuse of the model-based database can increase the system design productivity and effectiveness significantly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.450-453
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• 1999

In this paper, the cooperative retrieval system to interface between users and DB, image data and knowledge-based database(KDB), being formed in a linguistic knowledge expression, of system is presented. Conventional database retrieval systems provide the data only in case that the data exactly corresponding with users' requirements exist in these systems, but don't in other cases. In order to resolve this problem, if the data users require are not in existence, this system shows the data and image information which are approximate with knowledge-based database materialized by fuzzy clustering and allocation of linguistic label.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.