• Journal of The Korean Association For Science Education
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• v.21 no.3
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• pp.497-505
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• 2001

In this study, the relationships among students' conceptual understanding of molecular structure and cognitive variables were investigated for 165 high school students. After they had learned 'High School Chemistry II' for two semesters, the tests of conception concerning molecular structure, spatial visualization ability, logical thinking ability, mental capacity, and learning approach were administered. The results indicated that students' conceptual understanding of molecular structure was not sound, and several misconceptions were found. The scores of the conception test were significantly correlated with all the cognitive variables studied. Multiple regression analyses were conducted to examine the predictive influences of students' cognitive variables on their conceptual understanding. Meaningful learning approach was the most significant predictor and were followed by logical thinking ability, rote learning approach, and mental capacity. However, spatial visualization ability did not have the predictive power.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.11-19
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• 1997

Students' protocols obtained from think-aloud interviews were analyzed in the aspects of the success at first two problem-solving stages (understanding and planning), the time to complete a problem, the time at each problem-solving stage, the number of transition, and the transition rate. These were compared in the aspects of the context of problem, the success in solving problem, students' logical reasoning ability, spatial ability, and learning approach. The results were as follows:1. Students tended to spend more time in everyday contexts than in scientific contexts, especially at the stages of understanding and reviewing. The transition rate during solving a problem in everyday contexts was greater than that in scientific contexts. 2. Unsuccessful students spent more time at the stage of understanding, but successful students spent more time at the stage of planning. 3. Students' logical reasoning ability, as measured with the Group Assessment of Logical Thinking, was significantly correlated with the success in solving problem. Concrete-operational students spent more time in completing a problem, especially understanding the problem. 4. Students' spatial ability, as measured with the Purdue Visualization of Rotations Test and the Find A Shape Puzzle, was significantly correlated with their abilities to understand a problem and to plan for its solution. 5. Students' learning approach, as measured with the Questionnaire on Approaches to Learning and Studying, was not significantly correlated with the success in solving problem. However, the students in deep approach had more transitions and greater transition rates than the students in surface approach.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1415-1429
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• 2019

Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

• 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 Korean Society for Library and Information Science
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• v.51 no.2
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• pp.5-22
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• 2017

The purpose of this study is to analyze the teaching and learning strategies included in the inquiry tasks of elementary moral textbooks with multiple intelligences (M.I), and to propose educational information services of teacher librarians. It was found that the tasks were mainly designed by the linguistic intelligence, logical & mathematical intelligence and spatial intelligence. In terms of the information literacy process, linguistic intelligence and spatial intelligence are mainly applied to the analysis-understanding stage. Logical & mathematical intelligence is applied to the stage of comprehensive-application and linguistic intelligence is applied to expression-delivery step. In order to cultivate the insufficient M.I in inquiry activities, teacher librarians should improve room and teaching materials of their school library and provide workbooks using the graphic organizer after analyzing the linkage of the inquiry tasks between the subjects.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Journal of Korean Library and Information Science Society
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• v.50 no.3
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• pp.293-315
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• 2019

Based on the current status of use of L-Commerce installed in the Yongsan Public Library and Mapo Lifelong Learning Center, this study suggested the direction of spatial design for the public library's makerspace with L-Commons model. To this end, we investigated the literature research on library makerspaces and the case studies of makerspaces installed in 25 public libraries in Korea and 18 public libraries in the US. And In-depth interviews and user surveys were conducted. The public library makerspace presented through this study should be an open space where everyone in the community can easily enter, break down barriers between all classes in the region, and lead to smooth communication. Second, it should be a learning, cooperation, and creative space where resources can be shared and cooperation for creative activities and projects can be carried out. Third, it should be a creative workspace where community members can turn ideas into physical things that anyone can't do elsewhere or work on something interesting.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.157-165
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• 2021

Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.

• 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.