• Education of Primary School Mathematics
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• v.10 no.1 s.19
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• pp.1-13
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• 2007

For the last 30 years many researches were conducted to supply a lot of information on brain that were worth using in teaching and learning. They have showed that students received information through multi-path, and used familiar learning sense to present the received informations. However, nowadays only visual materials are mainly used in mathematics classroom. The purpose of this research is aimed to investigate implications of which learning senses are dominated to learn mathematics. Learning sense were catagorized to visual, auditory, and kinesthetic sense according to Politano & Paquin's classification. We surveyed student's learning senses using questionares. Subjects were composed of 141 elementary students, 117 middle school students, 145 high school students,, and 99 college students. T-test were used to investigate whether there are differences in student's learning senses according to grade levels or not.

• Journal of Power Electronics
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• v.14 no.1
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• pp.74-81
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• 2014

In this study, a brain emotional learning-based intelligent controller (BELBIC) is developed for the speed control of an interior permanent magnet synchronous motor (IPMSM). A novel and simple model of the IPMSM drive structure is established with the intelligent control system, which controls motor speed accurately without the use of any conventional PI controllers and is independent of motor parameters. This study is conducted in both real time and simulation with a new control plant for a laboratory 3 ph, 3.8 Nm IPMSM digital signal processor (DSP)-based drive system. This DSP-based drive system is then compared with conventional BELBIC and an optimized conventional PI controller. Results show that the proposed method performs better than the other controllers and exhibits excellent control characteristics, such as fast response, simple implementation, and robustness with respect to disturbances and manufacturing imperfections.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.154.1-154
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• 2001

The paper describes a new type of robot called "artificial liferobot" which is able to learn, make decisions, and behave by itself based on a brain-type computing technique called "artificial brain". The artificial liferobot has self-learning ability from the environment by the interactions between human being and it. The artificial brain makes the artificial liferobot to behave by itself with its intensions like living things as human being. We briefly introduce one attempt of our researches for developing cognitive and behavioral intelligent artificial liferobot in out laboratory. One of our purposes is the development of the artificial liferobot, which plays an Important role in taking care of elderly and infirm people in a rapidly aging society.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of The Korean Association For Science Education
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• v.27 no.1
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• pp.93-104
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• 2007

The purpose of this study is to investigate brain activity both during the processing of a scientific hypothesis about biological phenomena and mental arithmetic using 3.0T fMRI at the KAIST. For this study, 16 healthy male subjects participated voluntarily. Each subject's functional brain images by performing a scientific hypothesis task and a mental arithmetic task for 684 seconds were measured. After the fMRI measuring, verbal reports were collected to ensure the reliability of brain image data. This data, which were found to be adequate based on the results of analyzing verbal reports, were all included in the statistical analysis. When the data were statistically analyzed using SPM2 software, the scientific hypothesis generating process was found to have independent brain network different from the mental arithmetic process. In the scientific hypothesis process, we can infer that there is the process of encoding semantic derived from the fusiform gyrus through question-situation analysis in the pre-frontal lobe. In the mental arithmetic process, the area combining pre-frontal and parietal lobes plays an important role, and the parietal lobe is considered to be involved in skillfulness. In addition, the scientific hypothesis process was found to be accompanied by scientific emotion. These results enabled the examination of the scientific hypothesis process from the cognitive neuroscience perspective, and may be used as basic materials for developing a learning program for scientific hypothesis generation. In addition, this program can be proposed as a model of scientific brain-based learning.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.163-171
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• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• Journal of Korean Elementary Science Education
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• v.31 no.4
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• pp.541-557
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• 2012

Interest is acknowledged to be a critical motivational variable that influences learning and achievement. The purpose of this study was to investigate the interest of the elementary students when free inquiry activities were performed through a brain-based evolutionary scientific teaching and learning model. For this study, 106 fifth grade students were chosen and performed individually free inquiry activities. The results of this study were as follows: First, after free inquiry activities, as to free inquiry science related attitude, a statistically significant difference was not observed. But they came to have positive feelings about the free inquiry. Especially students marked higher mean score in openness showed consistency in sub-areas of free inquiry science related attitude. Second, students had interests in various fields, especially they had many interests in area of biology. They chose inquiry subjects that seems to be easily accessible from surrounding and as an important criterion of free inquiry they thought the possibility that they could successfully perform it. And students who belong to the high level in the science related attitudes and academic achievement diversified more topics. Third, most of students failed to further their topics. However, the students who specifically and clearly extended their topics suggested appropriate variables in their topics. On the other hand, students who couldn't elaborate their topics were also failed to suggest further topics and their performance of inquiry was more incomplete. In conclusion, the experiences of success in free inquiry make the science attitude of students more positive and help them extend their inquiry. These results have fundamental implications for the authentic science inquiry in the elementary schools and for the further research.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.