• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.15-23
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In this environments, users want to get a variety of customized services by using only an individual mobile device which has limitations such as tiny display screens, limited input, and less powerful processors. Therefore, The device sharing for solving these limitation problems and its efficient processing is one of the new research topics. This paper proposes a device recommender system which searches and recommends devices for composing user requested services. The device recommender system infers devices based on environmental context of a user. However, customized devices for each user are different because of a variety of user preference even if users want to get the same service in the same space, Therefore the paper considers the user preference for device recommendation. Our device recommender system is implemented and tested on the real mobile object developed for device sharing in ubiquitous environments. Therefore we can expect that the system will be adaptable in real device sharing environments.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1781-1791
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• 1996

A number of research issues such as knowledge acquisition, inferencing techniques, and knowledge refinement methodologies have been involved in the development of expert systems. Since each issue is considered very com- plicated, there has been little effort to take all the issues into account collectively at once. However, knowledge acquisition and inferencing are closely reated because the knowledge is extracted by human experts from the inferencing process for solving a specific task or problem. Knowledge refinement is also accomplished by hand-ling problems caused during the inferencing process of the system due to incompleteness and inconsistency of the knowledge base. From this perspecitive, we present a method by which software platform is established in which those issues are integrated in the development of expert systems, especially in the domain where the domain models and concepts are hard to be constructed because of inherent fuzziness of the domain. We apply a machine learning technique,technique, conceptual clustering,to build a knowledge base and rual models by which an efficient inferencing,incermental knp\owledge acquisition and refinment are possible.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.521-528
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• 2017

Research on AI is essential to build a system with collective intelligence that allows a large number of UAVs to maintain their flight while carrying out various missions. A typical approach of AI includes 'top-down' approach, which is a rule-based logic reasoning method including expert system, and 'bottom-up approach' in which overall behavior is determined through partial interaction between simple objects such as artificial neural network and Flocking Algorithm. In the same study as the existing Flocking Algorithm, individuals can not perform individual tasks. In addition, studies such as UAV formation flight can not flexibly cope with problems caused by partial flight defects. In this paper, we propose organic integration between top - down approach and bottom - up approach through multi - agent system, and suggest a flight flight algorithm which can perform flexible mission through it.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.252-266
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• 2016

An architectural pre-design, which is conducted prior to the architecture design, supports fundamental configuration during the entire AEC project by predicting the cost, demand, etc., of the building, and is therefore gaining importance. In particular, the massing calculation of the pre-design phase should be prioritized, as it is fundamental to architectural outline. However, most architects depend on only their experience and intuition while conceptualizing an integrated framework of design conditions, including the building code and requirements for the massing calculation of the object. Therefore, many difficulties arise in terms of performing appropriate tasks. Thus, the purpose of this study is to implement a knowledge-based BIM for explicitly representing the design knowledge, which is the basis of decision making for an architect while performing the massing calculation. In particular, the 3D knowledge relevant to a project can be provided and accumulated in the massing calculation by the BIM system; this facilitates an integral understanding. Consequently, the approximate result of massing calculation in 3D BIM environment, through both the knowledge-based BIM template and plug-in, can be swiftly provided to the architect. In addition, the architect can invent various alternatives, estimate resulting costs, and reuse the accumulated knowledge in future BIM design processes.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.179-187
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• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of The Korean Association of Information Education
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• v.22 no.3
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• pp.367-374
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• 2018

In order to cultivate the human resources needed in the 4th industrial revolution era, it is necessary to select the gifted students and educate them systematically. Although excellent gifted students are important in a specific field, more convergent talents in the fields of mathematics, science, and information are required. The purpose of this study is to investigate how evaluation factors reflecting the characteristics of information gifted students affect the selection of science gifted students of a university gifted education center. In the characteristics of information gifted students, the cognitive factors such as Rule creation ability, Reasoning ability, Efficiency ability, Generalization ability, Structuring ability and Abstraction ability were highly correlated in selecting the science gifted students. Correlations in the applicants group of students for science gifted education center are higher than those in the first passers group and higher than those in the final successful candidates group. This means that the factors that shows the characteristics of the information gifted have a great influence on the selection of the science gifted.

• Journal of Science and Technology Studies
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• v.1 no.2 s.2
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• pp.399-422
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• 2001

Models for human cognition currently discussed in cognitive science cannot be appropriate ones. The symbolic model of the traditional artificial intelligence works for reasoning and problem-solving tasks, but doesn't fit for pattern recognition such as letter/sound cognition. Connectionism shows the contrary phenomena to those of the traditional artificial intelligence. Connectionist systems has been shown to be very strong in the tasks of pattern recognition but weak in most of logical tasks. Brooks' situated action theory denies the. notion of representation which is presupposed in both the traditional artificial intelligence and connectionism and suggests a subsumption model which is based on perceptions coming from real world. However, situated action theory hasn't also been well applied to human cognition so far. In emphasizing those characteristics of models I refer those models 'left-brain model', 'right-brain model', and 'robot model' respectively. After I examine those models in terms of substantial items of cognitions- mental state, mental procedure, basic element of cognition, rule of cognition, appropriate level of analysis, architecture of cognition, I draw three arguments of embodiment. I suggest a way of unifying those existing models by examining their theoretical compatability which is found in those arguments.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.202-209
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• 2002

This paper deals with simplification of classification rules for data mining and rule bases for control systems. Datamining that extracts useful information from such a large amount of data is one of important issues. There are various ways in classification methodologies for data mining such as the decision trees and neural networks, but the result should be explicit and understandable and the classification rules be short and clear. The rough sets theory is an effective technique in extracting knowledge from incomplete and inconsistent data and provides a good solution for classification and approximation by using various attributes effectively This paper investigates granularity of knowledge for reasoning of uncertain concopts by using rough set approximations and uses a hierarchical classification structure that is more effective technique for classification by applying core to upper level. The proposed classification methodology makes analysis of an information system eary and generates minimal classification rules.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.