• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.170-182
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• 2012

The purpose of this study was to develop learning materials based on the Kepler's abductive reasoning and to identify high school students' rule-inferring strategies on the law of planetary motion. The learning materials including the concepts of solar magnetic field, conservation of figure skater's angular momentum and Kepler's polyhedral theory were developed and the questions about Kepler's 2nd and 3rd law of planetary motion were also created. The participants were 79science high school students and 83general high school students. The patterns and properties of their abductive inference were analyzed. The findings revealed that the students showed 'incomplete analogy abduction', 'analogy abduction' and 'reconstruction' to generate the hypotheses concerning the Mars' motion related to the solar magnetic field. There were more general high school students who showed the incomplete analogy abduction than science high school students. On the other hand, there were more science high school students who showed the analogy abduction and reconstruction strategy than general high school students. Also, they showed 'incomplete analogy abduction', 'analogy abduction' and 'model construction and manipulation' to generate the hypotheses concerning Kepler's second law. A number of general high school students showed the incomplete analogy. It is suggested that because the analogy of figure skater cause the students' alternative framework to use, more detailed demonstration is necessary in class. In addition, students combined Kepler's polyhedral theory with their prior knowledge to infer Kepler's third law.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.818-831
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• 2007

Recognizing the importance of abductive inquiry in Earth science, some theoretical approaches that deploy abduction have been researched. And, it is necessary that the abductive inquiry in a geological field excursion as a vivid locale of Earth science inquiry should be researched. We developed a geological field trip based on the abductive learning model, and investigated students' abductive inference, thinking strategies used in those inferences, and the impact of a teacher's pedagogical intervention on students' abductive inference. Results showed that students, during the field excursion, could accomplish abductive inference about rock identification, process of different rock generation, joints generation in metamorpa?ic rocks, and terrains at the field trip area. They also used various thinking strategies in finding appropriate rules to construe the facts observed at outcrops. This means that it is significant for the enhancement of abductive reasoning skills that students experience such inquiries as scientists do. In addition, a teacher's pedagogical interventions didn't ensure the content of students' inference while they helped students perform abductive reasoning and guided their use of specific thinking strategies. Students had found reasoning rules to explain the 01: served facts from their wrong prior knowledge. Therefore, during a geological field excursion, teachers need to provide students with proper background knowledge and information in order that students can reason rues for persuasive abductive inference, and construe the geological features of the field trip area by the establishment of appropriate hypotheses.

• Journal of Gifted/Talented Education
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• v.25 no.1
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• pp.77-93
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• 2015

The purpose of this study was to explore the problem solving strategy for Goldberg machine tasks of the gifted students in elementary science depending on the cognitive style(tendency to field-dependent and field independent). It was aimed to provide suggestions for the features and differences of the problem solving strategies of the gifted students in elementary science according to their cognitive styles. A total of 16 students, from the gifted class of P elementary school in Hwaseong were sampled for the research, cognitive styles Test was conducted to divide the students in teams, and the teams were classified according to cognitive style tendencies to five groups of field-dependent group, weak field-dependent group, mixed group, weak field-independent group and field-independent group. The Goldberg device task given was to make a Goldberg device within the angle framework of (Figure) 1, for a bead to start from the starting point and to reach the final point the last. The results are as follows: First, regarding the plan for producing the device, the stronger the field-independent tendency, they established more specific strategy-reflected plan; the stronger the field-dependent tendency, they established less specific strategy-reflected plan. Second, all cognitive style groups took a limited period of time into consideration, to fabricate the devices for the ball to arrive the last using a fine adjustment rather than many devices. Third, the field-independent group used a lot of logical reasoning; the field-dependent group used a lot of intuitive thinking. Fourth, the field independent group properly utilized strategies such as cooperation and role allocation; the field-dependent group tried to solve the task personally rather than cooperatively with poor role allocation. Fifth, the intermediate mixed group solved the problem better than the inclined groups such as field-dependent or field-independent groups.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.69-77
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• 2014

System Dynamics(SD) is one of the complexity theories that has attracted attention as a computer-aided simulation methodology to analyze a dynamic problem and to develop a policy(strategy) in social science. Though there are properly unproven cases in research models which were developed in various fields by SD methodology during the last five decades, they are utilized as models to represent SD sub-theories. For this reason, this study targeted the population dynamics model which was frequently utilized to explain SD fundamentals and it proved errors of reasoning a structure of the existing causal and dominant feedback loop. Consequently, we presented a strategy to strengthen the coherence between CLD(causal loop diagram) and SFD(stocks-and-flows diagram) for improving validity of the existing model. The findings of this study contribute to the advancement of the existing SD and to the reinforcement of validation for policy research models of SD.

• Journal of Educational Research in Mathematics
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• v.22 no.1
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• pp.101-115
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• 2012

Analogy, a plausible reasoning on the basis of similarity, is one of the thinking strategy for concept formation, problem solving, and new discovery in many disciplines. Statistics educators argue that analogy can be used as an useful thinking strategy in statistics as well. This study investigated the characteristics of students' analogical thinking in statistics. The mathematically gifted were asked to construct similar problems to a base problem which is a statistical problem having a statistical context. From the analysis of the problems, students' new problems were classified into five types on the basis of the preservation of the statistical context and that of the basic structure of the base problem. From the result, researchers provide some implications. In statistics, the problems, which failed to preserve the statistical context of base problem, have no meaning in statistics. However, the problems which preserved the statistical context can give possibilities for reconceptualization of the statistical concept even though the basic structure of the problem were changed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.120-129
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• 1992

In this paper we propose a fuzzy-neural network(FNN) which includes both advantages of the fuzzy logic and the neural network. The basic idea of the FNN is to realize the fuzzy rule-base and the process of reasoning by neural network and to make the corresponding parameters be expressed by the connection weights of neural network. After constructing the FNN, a novel controller consisting of a conventional P-controller and a FNN is explained. In this control scheme, the rule-base of a FNN are automatically generated by error back-propagation algorithm. Also the parallel connection of the P-controller and the FNN can guarantee the stability of a plant at initial stage before the rules are completely created. Finally the effectiveness of the proposed strategy will be verified by computer simulations using a 2 degree of freedom robot manipulator.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정 (knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스 (data warehouse)를 기반으로 하는 OLAP환경에서의 데이터와 인터넷을 통한 웹 (web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지 (explicit knowledge)외에 암묵지 (tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스 (prior knowledge base)와 선행지식관리시스템 (prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템 (knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.1
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• pp.1-16
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• 2017

This study conducted in-depth interviews with twelve men in their twenties and employed the Zaltman Metaphor Elicitation Technique (ZMET) to identify the ideal self-image and fantasy of men wearing makeup. The results are as follows. First, the ideal self-images of men wearing makeup can be divided into 7 images (well-managed, dissimilar from real identity, masculine, neat, stylish, standing out, and formal). Men who wear makeup pursued an alternative decent image that is different from their reality. They want to be manly, attractive, decorous, and eye-catching through a better looking face. Second, men who wear makeup have insecurities about their looks and personalities that creates dissatisfaction with reality and a desire for a different idealistic self. Makeup was the tool to create the other entity. Makeup facilitated a fantasy of becoming another to gain increased confidence in social relationships. However, without makeup, they showed a lack of confidence and became intimidated that made them even further dependent on makeup. Third, the process helped participants complete a consensus map that represented the emotional and reasoning structures of men wearing makeup. This study showed 7 ideal self-images of men wearing makeup with a fantasy to create a desired ideal self by wearing makeup. The study can be applied to marketing strategy for men's cosmetics and plates' designs.