• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.84-87
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• 2003

This paper proposes the enhanced REF network, which arbitrates learning rate and momentum dynamically by using the fuzzy system, to arbitrate the connected weight effectively between the middle layer of REF network and the output layer of REF network. ART2 is applied to as the learning structure between the input layer and the middle layer and the proposed auto-turning method of arbitrating the learning rate as the method of arbitrating the connected weight between the middle layer and the output layer. The enhancement of proposed method in terms of learning speed and convergence is verified as a result of comparing it with the conventional delta-bar-delta algorithm and the REF network on the basis of the ART2 to evaluate the efficiency of learning of the proposed method.

• Educational Technology International
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• v.8 no.2
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• pp.1-19
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• 2007

This study is to analyze the implications of effective learning in a ubiquitous environment. Research proceeded according to the multiple case study analysis method. This paper is one result of the Korean case study to examine the effectiveness of and satisfaction with u-learning. We will introduce necessary conditions for effective learning in a ubiquitous environment. Each condition was elicited through the case study, and the analyzing framework was classified into hardware related to infra structure; software such as learning contents, teaching-learning activity and support, and class management; human-ware related to learner and teacher; system-ware as an education system, and administrative supporting.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• The Journal of Korean Association of Computer Education
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• v.21 no.4
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• pp.11-20
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• 2018

This study is to propose a problem bank of problem solving programming using Online Judge System as one of the ways to motivate learners and increase for immersion to students who take Data Structure lecture that is the basis of problem solving ability using information science. In order to do this, we developed a question bank for each major topic in the Data Structure, by developing 70 problem solving programming problems suitable for the main topics of the Data Structure. By mounting it on an Online Judge System and applying to actual classes, and by analyzing the motivation for learning and the degree of immersion according to the result after the application of the lesson, we propose a teaching-learning contents and usage for problem solving programming and Data Structure classes at the teacher training university which give motivation for learning and immerse in problem solving programming.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1371-1376
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• 2019

In case of fire in a structure, it is difficult to suppress fire because it can not accurately grasp the location of fire in case of fire. In this paper, we propose a system algorithm that can guide the optimal evacuation route in case of deep learning-based (RNN) structure disaster. The present invention provides a service to transmit data detected by sensors to a server in real time by using installed sensor, to transmit and analyze information such as temperature, heat, smoke, toxic gas around the sensor, to identify the safest moving path within a set threshold, to transmit information to LED guide lights and direction indicators in a structure in real time to avoid risk factors. This is because the information of temperature, heat, smoke, and toxic gas in each area of the structure can be grasped, and it is considered that the optimal evacuation route can be guided in case of structure disaster.

• Korean Journal of Clinical Pharmacy
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• v.24 no.2
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• pp.90-97
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• 2014

Learning portfolio is a collection of evidence that learning has taken place. It has gained its reputation as a useful assessment tool in the education of health professionals. The purpose of this study is to describe the pharmacy students' experiences and perceptions upon the introduction of a learning portfolio into the Introductory Pharmacy Practice Experience course. Methods: Fifty five students from one pharmacy school who used a learning portfolio to document their progress in the IPPE course participated in 16-item questionnaire exploring opinions and experiences of learning portfolio preparation, assessment, and personal and professional development and reflection. Results: Most students agree that a learning portfolio is a valuable tool in promoting self-directed and reflective learning. However most of them (46/55) also feel developing a portfolio is time-consuming, and when compared to their effort, an appropriate reward has not been given. Conclusion: To make the use of learning portfolios successful students should receive clear guidelines on their purpose, content and structure. Also the assessment criteria should be provided before the introduction of learning portfolio and their effort in developing learning portfolio should be rewarded.

• The Research Journal of the Costume Culture
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• v.30 no.3
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• pp.444-454
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• 2022

Flipped learning research has been applied in various educational fields since 2015 and the educational effects have been discussed in previous literature. In the beauty field, flipped learning research is insufficient; in particular, it is difficult to find research on flipped learning specifically concerning nail beauty education. The purpose of this study is to develop a model for applying flipped learning to nail beauty education which should involve practical training based on theory. Such an approach is considered effective. Data were collected and analyzed focusing on previous studies with flipped learning applied as a research method. The subject of the research is "Nail Color Design 1", a common nail major elective subject at J college. The "Nail Color Design 1" course is a practice-oriented course in the form of theory and practical classes. Consequently, the flipped learning education model for nail beauty was designed by reflecting learners' needs through the ADDIE instructional design model. It was applied based on the education structure of the Pre-class, In-class, and Post-class of the PARTNER instructional learning model. This study deviates from the traditional practical education model, and has educational significance as a practical model in which flipped learning is applied to nail beauty subjects and self-reflection is derived through project practice.

• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.140-161
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• 2014

The purpose of this study was to analyze 181 papers about brain-based learning appeared in domestic scientific journals from 1989 to May of 2012 and suggest application conditions in elementary science education. The results of this study summarizes as follows; First, learning activity suggested by brain-based learning study is mainly explained by working of brain function. Learning activity explained by brain-based learning study are divided into 'learning according to specialized brain function, learning according to brain function integration and learning beyond specialization and integration of hemispheres'. Second, it searched how increased knowledge of brain structure and function affects learning. Analysis from this point of view suggests that brain-based learning study affects learning in many ways especially emotion, creativity and learning motivation. Third, brain-based learning study suggests various possibilities of learning activity reflecting brain plasticity. Plasticity which is one of most important characteristics of brain supports the validity of learning activity as learning disorder treatment and explains the possibility of selective increment of brain function by leaning activity and the need of whole-brain approach to learning activity. Fourth, brain-based learning brought paradigm shifts in education field. It supports learning sophistication on the understanding of student's learning activity, guides learning method that reflects the characteristics of subject and demands reconstruction of curriculum. Fifth, there are many conditions to apply brain-based learning in elementary science education field, learning environment that fits brain-based learning, change of perspectives on teaching and learning of science educators and development of brain-based learning curriculum are needed.