• Journal of the Korean Society of Physical Medicine
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• v.7 no.1
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• pp.11-20
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• 2012

Purpose : This study is intended to examine the repetitive transcranial magnetic stimulation on cognitive function in the focal ischemic stroke rat model. Methods : This study selected 30 Sprague-Dawley rats of 8 weeks. The groups were divided into two groups and assigned 15 rats to each group. Control group: Non-treatment after injured by focal ischemic stroke; Experimental group: application of repetitive transcranial magnetic stimulation(0.1 Tesla, 25 Hz, 20 min/time, 2 times/day, 5 days/2 week) after injured by focal ischemic stroke. To assess the effect of rTMS, the passive avoidance test, spatial learning and memory ability test were analyzed at the pre, 1 day, $7^{th}$ day, $14^{th}$ day and immunohistochemistric response of BDNF were analyzed in the hippocampal dentate gyrus at $7^{th}$ day, $14^{th}$ day. Results : In passive avoidance test, the outcome of experimental group was different significantly than the control group at the $7^{th}$ day, $14^{th}$ day. In spatial learning and memory ability test, the outcome of experimental group was different significantly than the control group at the $7^{th}$ day, $14^{th}$ day. In immunohistochemistric response of BDNF in the hippocampal dentate gyrus, experimental groups was more increased than control group. Conclusion : These result suggest that improved cognitive function by repetitive transcranial magnetic stimulation after focal ischemic stroke is associated with dynamically altered expression of BDNF in hippocampal dentate gyrus and that is related with synaptic plasticity.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Journal of the Korean School Mathematics Society
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• v.13 no.2
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• pp.303-322
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• 2010

The purpose of this study was to analyze the effects of using the geometric manipulative for the development of spatial sense and thus to find out a better mathematics teaching and learning method that could help develop students' spatial senses. The two fifth grade classes were randomly chosen as an experimental group (31 students) and a control group (32 students), respectively. This study implemented nonequivalent control group pretest-posttest design of quasi-experimental design. The test instrument used in this study was a spatial sense test. The pretest and posttest were implemented with the same instrument. In addition, their classes were observed and videotaped, and the data and their study activities were analyzed. In conclusion, first, the geometric manipulative-aided activities contributes to developing students' spatial senses and their two sub-factors involves perceptual consistency and perception of spatial relationship. Second, the activities of grasping the components of solid figures, sketches and development figures by using the geometric manipulative contribute to boost students' perceptual consistencies and their perceptions of spatial relationship.

• Journal of Music and Human Behavior
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• v.3 no.1
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• pp.63-76
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• 2006

Melody is defined as adding spatial dimension to the rhythm which is temporal concept. Being able to understand melodic pattern and to reproduce the pattern also requires cognitive skills. Since 1980, there has been much research on the relationship between academic skills and music cognitive skills, and how to transfer the skills learned in music work to the academic learning. The study purported to examine various research outcomes dealing with the correlational and causal relationships between musical and academic skills. The two dominating theories explaining the connection between two skills ares are "neural theory" and "near transfer theory." The theories focus mainly on the transference of spatial and temporal reasoning which are reinforced in the musical learning. The study reviewed the existing meta-analysis studies, which provided evidence for positive correlation between academic and musical skills, and significance of musical learning in academic skills. The study further examined specific skills area that musical learning is correlated, such as mathematics and reading. The research stated that among many mathematical concepts, proportional topics have the strongest correlation with musical skills. Also with reading, temporal processing also has strong relationship with auditory skills and motor skills, and further affect language and literacy ability. The study suggest that skills learned in the musical work can be transferred to other areas of learning and structured music activities may be every efficient for children for facilitating academic concepts.

• Journal of the Korean earth science society
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• v.40 no.1
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• pp.86-106
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• 2019

The purpose of this study is to develop an astronomy education program reflecting astronomical thinking to be used at science museum and to investigate the effect of the program on the improvement of astronomical thinking ability of high school students. After selecting the components of astronomical thinking through literature studies, we developed an astronomy education program consisting of four stages: demonstration and observation, and question and thinking, support and group discussion, demonstration and assessment. In order to verify the effectiveness of the program, we conducted a covariance analysis on the pre- and post-tests of the experimental group and control group to examine the level of students' thinking before and after using the program in teaching and learning. As a result, it was confirmed that the astronomy education program reflecting astronomical thinking was effective in promoting students' astronomical thinking ability. In particular, this program was effective in enhancing the ability of modeling by reconstructing the observed astronomical phenomenon from the viewpoint of the universe with respect to spatial thinking in the astronomy domain. It was also effective to improve the ability of organizing the system by grasping the relationship between the elements constituting the astronomical system in relation to the system thinking in the astronomy domain. This study is significant in suggesting a specific teaching and learning program to develop students' astronomical thinking.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.77-83
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• 2020

The technologies of 3D spatial information, such as Smart City and Digital Twins, are developing rapidly for managing land and solving urban problems scientifically. In this construction of 3D spatial information, an object using aerial photo images is built as a digital DB. Realistically, the task of extracting a texturing image, which is an actual image of the object wall, and attaching an image to the object wall are important. On the other hand, occluded areas occur in the texturing image. In this study, the ResNet algorithm in deep learning technologies was tested to solve these problems. A dataset was constructed, and the street tree was detected using the ResNet algorithm. The ability of the ResNet algorithm to detect the street tree was dependent on the brightness of the image. The ResNet algorithm can detect the street tree in an image with side and inclination angles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Animal cells and systems
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• v.16 no.4
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• pp.302-312
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• 2012

The purpose of this study was to examine whether Phellodendri Cortex extract (PCE) could improve learning and memory impairments caused by lipopolysaccharide (LPS)-induced inflammation in the rat brain. The effect of PCE on modulating pro-inflammatory mediators in the hippocampus and its underlying mechanism were investigated. Injection of LPS into the lateral ventricle caused acute regional inflammation and subsequent deficits in spatial learning ability in the rats. Daily administration of PCE (50, 100, and 200 mg/kg, i.p.) for 21 days markedly improved the LPS-induced learning and memory disabilities in the Morris water maze and passive avoidance test. PCE administration significantly decreased the expression of pro-inflammatory mediators such as tumor necrosis factor-${\alpha}$, interleukin-$1{\beta}$, and cyclooxygenase-2 mRNA in the hippocampus, as assessed by RT-PCR analysis and immunohistochemistry. Together, these findings suggest that PCE significantly attenuated LPS-induced spatial cognitive impairment through inhibiting the expression of pro-inflammatory mediators in the rat brain. These results suggested that PCE may be effective in preventing or slowing the development of neurological disorders, including Alzheimer's disease, by improving cognitive and memory function because of its anti-inflammation activity in the brain.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.96-109
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• 2011

Recently, the geographic information system(GIS) is generally used in various fields with the development of information and communication technology, with expansion of its applications and utilization scope. Especially, utilizing GIS is expected to have positive effects on the geography learning and more helpful for the geographic information observation compared to the picture or 2D based media. The effective visualization of complex geographic data does not only take realization of its visual information but also increases the human ability in analysis and understanding to use the geographic information. In this paper, we examine a method to develop the geography learning contents based on the technology with augmented reality and GIS, and then we have a case study for various kinds of visualization techniques and examples to use in geography learning situation. Moreover, we introduce an example of the manufacturing process from the existing GIS data to augmented reality based geography learning system. From the above, we show that the usefulness of our method is applicable for effective visualization of the three-dimensional geographic information in the geography learning environment.