• Journal of Korean Physical Therapy Science
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• v.9 no.4
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• pp.141-153
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• 2002

Problem based learning(PBL) is one of the learning strategies from the constructivism. It is a learning centered students. The tutors are facillitators as activators, helpers and cooperators not organizer in the classrooms. PBL makes that students learn creativity, independence, reasoning skits, communication and collaboration for problem solving. As the PBL process, students get the problems that are in real situation, discussed with others for brain storming, self directed study and revisited to the situation. They think critically and apply to the real situation. When students are to be physical therapists, they are easy to adopt their job and efficient to manage well. But inspite of a lot of advantages to them, there are much conflict to use as the learning strategies. Students perceived one of best learning method that they have experienced, but there are stress, burden, anxiety, timeless to prepare, lack of information and so on. PBL is effective to learning health oriented subjects, problem solving, even a lot preparation and processing for learning. It is reduced the differences between theories in colleges and practices in the fields. In processing of PBL, students get more many skills than the conventional learning. As trying many times to the classrooms, we can fixed to PBL with mistakes and conflict for better the development of the teaching and learning.

• Smart Media Journal
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• v.9 no.3
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• pp.9-17
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• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

• Journal of Korean Elementary Science Education
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• v.33 no.3
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• pp.579-587
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• 2014

In National Curriculum of Science revised in 2007, 'Free Inquiry' was newly introduced to increase student's interest in science and to foster creativity by having students make their own questions and find answers by themselves. The purpose of the study was to analyze characteristics deployed in the processes of elementary school students' free inquiry activities applying a brain-based evolutionary science teaching and learning principles. For this study, 106 the fifth grade students participated, and they performed individually free inquiry activities according to a brain-based evolutionary approach. In order to characterize the diversifying, estimating-evaluating-executing, and extending-applying activities in behavioral domain, the free inquiry diary constructed by the students, observations by the researcher, and interviews with the students were analyzed both quantitatively and qualitatively. The major results of this study were as follows: First, the students preferred basic inquiry process skills and the majority of the students selected observation as a major approach of their inquiry. The reason was found to be that they were accustomed to only typical basic inquiry skills which is frequently presented at textbooks and regular instruction and didn't have appropriate experience for using relevant integrative inquiry skills. Second, most of the methods diversified and selected by the students were confined to descriptive explanation rather than causal one. Third, both of the science attitude and academic achievement were associated with the number of diversified methods and the selection of appropriate method. Based on these findings, implications for supporting domain novices in inquiry learning environments are advanced.

• Journal of The Korean Association For Science Education
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• v.21 no.3
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• pp.516-528
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• 2001

In the educational study, the measure of EEG(brain waves) can be useful method to study the functioning state of brain during learning behaviour. This study investigated the changes of neuronal response according to four times repetition of audio-visual learning. EEG data at the prefrontal$(Fp_{1},Fp_{2})$ were obtained from twenty subjects at the 8th grade, and analysed quantitatively using FFT(fast Fourier transform) program. The results were as follows: 1) In the first audio-visual learning, the activities of $\beta_{2}(20-30Hz)$ and $\beta_{1}(14-19Hz)$ waves increased highly, but the activities of $\theta(4-7Hz)$ and $\alpha$ (8-13Hz) waves decreased compared with the base lines. 2). According to the repetitive audio-visual learning, the activities of $\beta_{2}$ and $\beta_{1}$ waves decreased gradually after the 1st repetitive learning. And, the activity of $\beta_{2}$ wave had the higher change than that of $\beta_{1}$ wave. 3). The activity of $\alpha$ wave decreased smoothly according to the repetitive audio-visual learning, and the activity of $\theta$ wave decreased radically after twice repetitive learning. 4). $\beta$ and $\theta$ waves together showed high activities in the 2nd audio-visual learning(once repetition), and the learning achievement increased highly after the 2nd learning. 5). The right prefrontal$(Fp_{2})$ showed higher activation than the left$(Fp_{1})$ in the first audio-visual learning. However, there were not significant differences between the right and the left prefrontal EEG activities in the repetitive audio-visual learning. Based on these findings, we can conclude that the habituation of neuronal response shows up in the repetitive audio-visual learning and brain hemisphericity can be changed by learning experiences. In addition, it is suggested once repetition of audio-visual learning be effective on the improvement of the learning achievement and on the activation of the brain function.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Korean Institute of Interior Design Journal
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• v.22 no.5
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• pp.171-178
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• 2013

In this study, we sought to find out the effect of different emotional interior images of the community facilities in an apartment complex on a youth brain wave by analyzing an Electroencephalograph (EEG). Based on the frequency of usage, we selected learning facilities, cultural facilities, and sport facilities. For brain stimulation, the visual stimulants with three different emotional words, cheerful, gentle, and elegant, were used based on I.R.I image scale. Overall, total nine different emotional images were used. Based on our findings, we conclude that: first, in order to improve learning concentration of the youth, a learning facility for the youth needs to be designed by skillfully combining the soft and comfortable colors from the gentle image and the murky and dark colors from the elegant image. Second, when designing a cultural facility, it is preferable to consider the elegant image for a calm and comfortable space. Third, a sport facility design needs to preclude dark colors and apply light colors to create a dynamic and lively space. Furthermore, we found out that the youth has established static images of each functionally different facility through their experience and learning. Therefore, it is imperative to plan community facilities in an apartment complex in a way to connect the space function with the emotional characteristics of the youth in order to support and encourage energetic activities and learning of the community youth.

• Clinical and Experimental Pediatrics
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• v.49 no.4
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• pp.341-353
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• 2006

Learning disorders are diagnosed when the individual's achievement on standardized tests in reading, mathematics, or written expression is substantially below that expected for age, schooling, and level of intelligence. Subtypes of learning disorders may be classified into two groups, language-based type learning disorders including reading and writing disorder, and nonverbal type learning disorder (NLD) such as those relating to mathematics & visuospatial skills, and those in the autism spectrum. Converging evidence indicates that reading disorder represents a disorder within the language system and more specifically within a particular subcomponent of that system, phonological processing. Recent advances in neuroimaging technology, particularly the development of fMRI, provide evidences of a neurobiological basis for reading disorder, specifically a disruption of two left hemisphere posterior brain systems, one parieto-temporal, the other occipito-temporal. The former is the reading system for beginner reading, the latter for skilled reading. Compensatory engagement of anterior systems around the inferior frontal gyrus(Broca's area) and a posterior(right occipito-temporal) system is noted in persistent poor readers in long-term follow up study. The theoretical model proposed to explain NLD's source is not right hemisphere damage, but rather the white matter model. The working hypothesis of the white matter model is that the underdevelopment of, damage to, or dysfunction of cerebral white matter(long myelinated fibers) is the source of this disorder. The role of an evidence-based effective intervention in the remediation of children with learning disorder is discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.951-953
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• 2015

Learning for thinking says the behavior of the organism changes as a result of practice or experience. It is very difficult to identify focusing ability objectively when students study. But, brain of the body is not so. EEG signal means continuously electric records of brain potential variation between two points on the scalp when brain activities take place. In types of EEG, there are delta(0~4Hz), theta(4~8Hz), alpha(8~13Hz), beta(13~30Hz) and gamma waves(30~50Hz). SMR waves and Mid-beta waves appear when focused for studying. Part for the most influence on concentrating reported that Mid-beta waves. In relation to brain activities, EEG has been actively researched for evaluating brain focus index system during learning and study. So, By using Biopac system for this study, measured brain wave was converted into FFT for extracting Mid-beta domain signals that are related to learning after giving focus invoked subjects to a small number of people. When concentrating, we measured the change in the power of the Mid-beta frequency domain and presented a correlation. Based on these results, we analyzed whether students are concentrated objectively on learning or not. and hope to offer more efficient learning method.

• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.