• Animal cells and systems
• /
• v.2 no.1
• /
• pp.65-69
• /
• 1998

The hippocampus and prefrontal cortex are regarded as the highest-order association cortices. The hippocampus has been proposed to store "cognitive maps" of external environments, and the prefrontal cortex is known to be involved in the planning of behavior, among other functions. Considering the prominent functional roles played by these structures, it is not surprising to find direct monosynaptic projections from the hippocampus to the prefrontal cortex. Rhythmic stimulation of this projection patterned after the hippocampal EEG theta rhythm induced stable long-term potentiation of field potentials in the prefrontal cortex. Comparison of behavioral correlates of hippocampal and prefrontal cortical neurons during an a-arm radial maze, working memory task shows a striking contrast. Hippocampal neurons exhibit clear place-specific firing patterns, whereas prefrontal cortical neurons do not show spatial selectivity, but are correlated to different stages of the behavioral task. These data lead to the hypothesis that the role of hippocampal projection to the prefrontal cortex is not to impose spatial representations upon prefrontal activity, but to provide a mechanism for learning the spatial context in which particular behaviors are appropriate.propriate.

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

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.36 no.1
• /
• pp.151-156
• /
• 2016

Making a learning city which allows unlimited accessibility to a chance of learning plays an important role to accomplish individuals'self-realizaton and advance quality of life so that improve whole competition of the city. Although securing enough space is necessary to realize the learning city, our reality has an imbalanced city structure which could hamper it. The purpose of this study is to examine the way to resolve the spatial imbalance by utilizing unused facilities located in elementary schools. The resulting conclusions provide implications that current low effectiveness is originated from passive participation of schools and leading better participation is needed to improve it.

• Journal of the Korean association of regional geographers
• /
• v.1 no.1
• /
• pp.103-116
• /
• 1995

The purpose of this paper is to explore a brief review of trends in existing geographical research on music and to analyze music by the 5 themes of geography and to explore a variety of classroom techniques which examine song lyrics for their geographic content. The results of this paper are summarized as followed : Firstly, the trends in geographical research on music can be classified into five areas, the first is on spatial diffusion in music, the second on spatial diffusion in music, the third on regional division in music, the fourth on regional characteristics in music, the fifth on pedagogical tools in the teaching of geography. Secondly, music holds numerous possibilities for regional geographical study. The lyrics of music are littered with geographical term through which song writers impart image of culture, the distinct geographical nature of music lyrics gives rise to many geographical question, also, music lyrics gives place its special character. The results of analyses by the 5 themes of geography indicate that music are useful to learning of regional geography. The application of music to learning regional geography attracts much attentions. In the respect of importance of learning new regional geography, and in the respect of adapting globalization have to be focused on this subject.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.3
• /
• pp.95-103
• /
• 2017

Horizon correction is a crucial stage for image composition enhancement. In this paper, we propose a deep learning based method for estimating the slanted angle of a photograph and correcting it. To estimate and correct the horizon direction, existing methods use hand-crafted low-level features such as lines, planes, and gradient distributions. However, these methods may not work well on the images that contain no lines or planes. To tackle this limitation and robustly estimate the slanted angle, we propose a convolutional neural network (CNN) based method to estimate the slanted angle by learning more generic features using a huge dataset. In addition, we utilize multiple adaptive spatial pooling layers to extract multi-scale image features for better performance. In the experimental results, we show our CNN-based approach robustly and accurately estimates the slanted angle of an image regardless of the image content, even if the image contains no lines or planes at all.

• Journal of the Korean association of regional geographers
• /
• v.12 no.2
• /
• pp.281-290
• /
• 2006

Map uses graphic language of dot, line and area to represent surface of the earth. Map has been adopted as tools for regional and cartography learning to improve graphicacy in geography education. Due to the rapid development in GIS and internet, practical use of maps has been extended in various study area. This Study is to develope web-based leaning materials for self-controled geography instruction. As learning materials for this aim, it has been constructed WebGIS for topography and thematic maps with boundary map of Chungbuk, digital map of Jochiwon(1:25,000), statistic data and field images. Function of WebGIS intend to improve skills on geo-information collection and spatial query, regional difference of spatial distribution. Individual learning using internet can make an improvement of learner centeredness and problem-solving. Finally, it will be expected to be suggest one of the education guide as blueprint in info-society.

• The Journal of the Korea Contents Association
• /
• v.18 no.11
• /
• pp.447-454
• /
• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.819-834
• /
• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.2
• /
• pp.29-36
• /
• 2023

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

• The Korean Journal of Physiology and Pharmacology
• /
• v.13 no.4
• /
• pp.315-319
• /
• 2009

Astragalus Membranaceus (AM) is a useful Korean herb that has been clinically prescribed for stress-related illness. The objective of the present study was to examine the anti-stress effects of AM on repeated stress-induced alterations of anxiety, learning and memory in rats. Restraint stress was administered for 14 days (2h/day) and AM (400mg/kg) given by oral administration, in the AM group, for the same period. Starting on the eighth day, the rats were tested for spatial memory on the Morris water maze test (MW) and for anxiety on the elevated plus maze (EPM). Changes of expression on immunohistochemistry were studied for cholineacetyl transferase (ChAT) and tyrosine hydroxylase (TH) in the brain. The results showed that the rats treated with AM had significantly reduced stress-induced deficits on learning and memory on the spatial memory tasks. In addition, the ChAT immunoreactivities were increased. In the EPM, treatment with AM increased the time spent in the open arms (p<0.001) compared to the control group. In addition, AM treatment also normalized increases of TH expression in the LC (p<0.001). In conclusion, administration of AM improved spatial learning and memory and reduced stress-induced anxiety. Thus, the present results suggest that AM is able to recover behavioral and neurochemical impairments induced by stress.