• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.533-536
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• 1998

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.122-128
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• 2006

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.47-58
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• 2004

To generate individualized and adaptive course, it's required to have the foundation structure in which learning objects are connected with each other with logical relevances. Each learner can have peculiar learning path at each point of time of learning through the logical relevancy between those learning objects and various links, considering individual learner. The purpose of this study is to design a learning object-basis individualized learning system structure, considering semantic sequencing by learning task types. It is our understanding that the individualized learning system design model of this study, considering the relevancy between learning objects, can be a fresh trial to accommodate semantic learning and true educational spirits in e-Learning at this point of time when criticism, such as the learning object based course design is simply a collection of meaningless objects, etc., is becoming influential.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.73-79
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• 2021

Recently, in the field of computer animation, a method for generating motion using deep learning has been studied away from conventional finite-state machines or graph-based methods. The expressiveness of the network required for learning motions is more influenced by the diversity of motion contained in it than by the simple length of motion to be learned. This study aims to find an efficient network structure when the types of motions to be learned are diverse. In this paper, we train and compare three types of networks: basic fully-connected structure, mixture of experts structure that uses multiple fully-connected layers in parallel, recurrent neural network which is widely used to deal with seq2seq, and transformer structure used for sequence-type data processing in the natural language processing field.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.944-947
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• 1996

Realization of autonomous agents that organize their own internal structure in order to behave adequately with respect to their goals and the world is the ultimate goal of AI and Robotics. Reinforcement learning gas recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. In this paper, we present a method of reinforcement learning by which a multi robots learn to move to goal. The results of computer simulations are given.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.94-96
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.127-134
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• 2002

Although the reward based on group accomplishment in cooperative learning has a merit to emphasize interdependency, it may have some undesirable side effects such as free rider effect and sucker effect. For the purpose of reducing these side effects, this study examined how the adjustment of the reward structure affected the scholastic achievement, the perception of learning environments, and the attitude toward science class by adding individual reward to group reward. We selected 2 classes of sixth grade in an elementary school, and taught on oxygen and carbon dioxide for 13 class hours in cooperative learning strategies. Group reward was applied to one class, and both group and individual rewards were applied to the other class. Analysis of the results indicated that the achievement scores of the students under the group and individual rewards were significantly higher than those under the group reward. In addition, they had more difficulty in science class and felt less satisfied. The upper level students under the group and individual rewards were also found to exhibit more competition. Educational implications were discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.499-501
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• 2021

Recently, artificial intelligence and SW have occupied an important position worldwide as the foundation technology of the era of the 4th industrial revolution, and web browser-based programming learning systems are becoming common due to changes in the learning environment caused by COVID-19. In accordance with this trend, this paper proposes a functionally scalable microservice-based system structure for an online evaluation system as a tool for learning algorithms that are the basis of artificial intelligence and SW. In addition, a functional structure for applying machine learning to automatic evaluation functions under the proposed system structure is also proposed.