• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of The Korean Association For Science Education
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• v.13 no.3
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• pp.327-333
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• 1993

The purpose of this study was to investigate children's ideas about growth of plants and their conceptural change after instruction to apply SPACE (Science Processes And Concepts Exploration) Learning strategy. For this study, a total of 55 students from infants to 5th grade were sampled. Data were obtained by the individual interview procedure and summarized by using network analysis. The major results of this study were as follows : 1. A very small number of students responded reforming or reorganisation of materials about the mechanism of germination and growth inside the seeds. 2. Almost all students confused between conditions necessary for germination and growth. Water and sunlight were mentioned by many students, while air and temperature were mentioned by a few students as the condition. 3. Some of the students showed that growth occurs continuously. Many students explaned occurence of growth about Plants related to the night, monning, or evening. 4. With the explaning about the necessary conditions for caterpillar growth, students mentioned conditions related in terms of human experience. 5. Many young childrens showed tendency of the egocentric and human-centered view of the world. Students scientific concepts increased significantly with age. Many students conceptions depend on the context-specificity ideas.

• Hwankyungkyoyuk
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• v.25 no.1
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• pp.35-49
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• 2012

The purpose of this study was to investigate satisfaction of student, adult and forest interpreters about facilities and program of children's garden, which was constructed as edutainment space within Korea National Arboretum on May 2011. The results are as follows. First, an information about construction of children's garden should be provided actively, because 70.8% student and 68.6% adult of respondent did not know existence of children's garden. Second, it has been analysed that a fundamental conception of GREEN-I be reflected in construction children's garden, especially, reflecting conception of Enjoy and Edutainment was comparatively high. Third, It was found that ecological experiencing activities program of children's garden was positively to develop environmental literacy for student. Fourth, It was found that satisfaction of uncommon planting area was very low among planting area and satisfaction of forest interpreters was very low about place of ecological experience learning using five senses. Finally, for effective operation of children's garden, children's garden should be provided planting for environmental education connected with the school curriculum and ecological experiencing activities program to enjoy time with the family. Therefore, these results will be applied to improve children's garden and could be applied to construct other children's garden in botanical gardens.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.39-45
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• 2019

We propose an active color model based method for tracking motions of multiple human using a networked multiple-camera system in IoT space as a human-robot coexistent system. An IoT space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of IoT space as well. One of the main goals of IoT space is to assist humans and to do different services for them. In order to be capable of doing that, IoT space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and IoT space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in IoT space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.3
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• pp.13-17
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• 2019

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.5
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• pp.26-31
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• 2019

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.225-233
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• 2019

The purpose of this study was to investigate the factors affecting e-learning usage intention of university students who have no prior e-learning experience. To address this issue, the study was carried out in two stages on university students at Y and K universities of Gyeongbuk province. After conducting the first survey, we constructed the measurement items and conducted the second survey on the factors that influence the learner's intention to use as the basic theory based on the results of the survey and the interviews with the experts. The result shows, first, due to the autonomy, the biggest advantage for e-learning, students' self-control decreases which affects e-learning fault. Second, university students use e-learning because of its convenience, ease of use and usefulness. However, convenience of learning space found to be useful in using e-learning, but it does not affect their actual intention to use e-learning. The result of this study can be used as basic data for the implementation of e-learning within the university and enhancing the individuals' learning effectiveness.

• Knowledge Management Research
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• v.14 no.4
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• pp.19-46
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• 2013

This paper aims to show the learning process and the educational effects of Divide-and-Combine principles and State Models, which are included in the Butterfly Model for creative problem solving. In our State Models, there are Time State Model, Space State Model, and Whole-Parts State Model. We have taught middle school students (for 18 hours), high school students (for 24 hours), and undergraduate students (for 1 semester) about our proposed Models when they solved contradiction problems. Also, we have made the students learn our contradiction resolution algorithms by themselves based on team-based discussion. By learning and by using our Models, the students had the higher level of expertise in contradiction problems and had the growth mindset that made them have confidence in themselves and kept them challenging themselves about problems. Also, learning and solving with our Models improved the students' growth mindset as well as their problem-solving ability.