• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.358-364
• /
• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.1061-1065
• /
• 2004

The knowledge-based society of the 21st century requires effective education and learning methods in each professional field because the development of human resource determines its competence more than any other factors. It is highly desirable to develop an intelligent tutoring system, which meets ever increasing demands of education and learning. Such a system should be adaptive to each individual learner's demands as well as the continuously changing state of the learning process, thus enabling the effective education. The development of a learning environment based on learner modeling is necessary in order to be adaptive to individual learning variants. An intelligent learning environment is being developed targeting the heritage education, which is able to provide a customized and refined learning guide by storing the content of interactions between the system and the learner, analyzing the correlations in learning situations, and inferring the learning preference from the learner's learning history. This paper proposes a heritage learning system of Bulguksa temple, integrating the ontology-based learner modeling and the learning preference which considers perception styles, input and processing methods, and understanding process of information.

• School Mathematics
• /
• v.7 no.3
• /
• pp.303-318
• /
• 2005

If students can use mathematics to solve their problems and learn the mathematical knowledge through it, they may think mathematics useful and valuable. This study is for the teaching through problem solving in mathematics education, which I consider in terms of the problem-based learning and mathematical modeling. 1 think mathematical modeling is applied to teaching mathematics as a problem-based learning. So I developed the teaching model, and showed the example that students learn the formal and hierarchic mathematics through mathematical modeling.

• The Journal of Information Systems
• /
• v.28 no.4
• /
• pp.271-291
• /
• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.241-244
• /
• 1996

There is a growing tendency to consider organizational learning as a mechanism for improving organizations and the rate at which organizations learn becomes perceived as a source for attaining competitive advantage. The objective of this research is to present a two-phase(learning efficient, and learning-effective) organizational modeling methodology based on the cognitive-maps and agents concept, and to describe how the result of the modeling can be used in the organizational learning context.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.23-32
• /
• 2024

This study aims to improve the quality of distance learning by developing webcast-based e-learning media and integrating it into an e-learning platform for functional job training purposes at the National Research and Innovation Agency, Indonesia. This study uses a Technology Acceptance Model (TAM) to assess and predict user perceptions of information systems using webcast platforms as an alternative to conventional applications. The research method was an online survey using Google Forms. Data collected from 136 respondents involved in practical job training were analyzed using structural equation modeling to test the technology acceptance model. The results showed that the proposed model effectively explained the variables associated with the adoption of web-based e-learning during the COVID-19 pandemic in Indonesia for participants engaged in functional job training. These findings suggest that users' perceptions of ease of use, usefulness, benefits, attitudes, intentions, and webcast usage significantly contribute to the acceptance and use of a more effective and efficient webcast-based e-learning platform.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.11
• /
• pp.2121-2142
• /
• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• The Journal of the Korea Contents Association
• /
• v.14 no.4
• /
• pp.513-521
• /
• 2014

The purpose of this research is to develope and implement a convergent educational contents based on theoretical background of integrated education using agent based modeling in the ubiquitous learning environment. The structure of this contents consists of three modules that were designed by trans-disciplinary concept and situated learning theory. These three modules are: convergent problem presenting module, resource of knowledge module and learning of agent based modeling and IT tools module. After the satisfaction survey of the implemented content, out of 5 total value, the average value was 3.86 for effectiveness, 4.13 for convenience and 3.86 for design. The result of the survey shows that the users are generally satisfied. By using this u-learning contents, learners can experience and learn how to solve the convergent problem by utilizing IT tools without any limitation of device, time and space. At the same time, the proposal of structural design of contents can be a good guideline to the researchers to develop the convergent educational contents in the future.

• Education of Primary School Mathematics
• /
• v.15 no.2
• /
• pp.107-134
• /
• 2012

The purpose of this study is to investigate the effectiveness of two teaching methods of word problems, one based on mathematical modeling learning(ML) and the other on traditional learning(TL). Additionally, the influence of mathematical modeling learning in word problem solving behavior, application ability of real world experiences in word problem solving and the beliefs of word problem solving will be examined. The results of this study were as follows: First, as to word problem solving behavior, there was a significant difference between the two groups. This mean that the ML was effective for word problem solving behavior. Second, all of the students in the ML group and the TL group had a strong tendency to exclude real world knowledge and sense-making when solving word problems during the pre-test. but A significant difference appeared between the two groups during post-test. classroom culture improvement efforts. Third, mathematical modeling learning(ML) was effective for improvement of traditional beliefs about word problems. Fourth, mathematical modeling learning(ML) exerted more influence on mathematically strong and average students and a positive effect to mathematically weak students. High and average-level students tended to benefit from mathematical modeling learning(ML) more than their low-level peers. This difference was caused by less involvement from low-level students in group assignments and whole-class discussions. While using the mathematical modeling learning method, elementary students were able to build various models about problem situations, justify, and elaborate models by discussions and comparisons from each other. This proves that elementary students could participate in mathematical modeling activities via word problems, it results form the use of more authentic tasks, small group activities and whole-class discussions, exclusion of teacher's direct intervention, and classroom culture improvement efforts. The conclusions drawn from the results obtained in this study are as follows: First, mathematical modeling learning(ML) can become an effective method, guiding word problem solving behavior from the direct translation approach(DTA) based on numbers and key words without understanding about problem situations to the meaningful based approach(MBA) building rich models for problem situations. Second, mathematical modeling learning(ML) will contribute attitudes considering real world situations in solving word problems. Mathematical modeling activities for word problems can help elementary students to understand relations between word problems and the real world. It will be also help them to develop the ability to look at the real world mathematically. Third, mathematical modeling learning(ML) will contribute to the development of positive beliefs for mathematics and word problem solving. Word problem teaching focused on just mathematical operations can't develop proper beliefs for mathematics and word problem solving. Mathematical modeling learning(ML) for word problems provide elementary students the opportunity to understand the real world mathematically, and it increases students' modeling abilities. Futhermore, it is a very useful method of reforming the current problems of word problem teaching and learning. Therefore, word problems in school mathematics should be replaced by more authentic ones and modeling activities should be introduced early in elementary school eduction, which would help change the perceptions about word problem teaching.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1254-1259
• /
• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.