• Journal of Ginseng Research
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• v.41 no.2
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• pp.151-158
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• 2017

Background: Chong-Myung-Tang (CMT) extract is widely used in Korea as a traditional herbal tonic for increasing memory capacity in high-school students and also for numerous body ailments since centuries. The use of CMT to improve the learning capacity has been attributed to various plant constituents, especially black ginseng, in it. Therefore, in this study, we have first investigated whether black ginseng-enriched CMT extracts affected spatial learning using the Morris water maze (MWM) test. Their molecular mechanism of action underlying improvement of learning and memory was examined in vitro. Methods: We used two types of black ginseng-enriched CMT extracts, designated as CM-1 and CM-2, and evaluated their efficacy in the MWM test for spatial learning behavior and their anti-inflammatory effects in BV2 microglial cells. Results: Our results show that both black ginseng-enriched CMT extracts improved the learning behavior in scopolamine-induced impairment in the water maze test. Moreover, these extracts also inhibited nitric oxide production in BV2 cells, with significant suppression of expression of proinflammatory cytokines, especially inducible nitric oxide synthase, cyclooxygenase-2, and $interleukin-1{\beta}$. The protein expression of mitogen-activated protein kinase and nuclear $factor-{\kappa}B$ pathway factors was also diminished by black ginseng-enriched CMT extracts, indicating that it not only improves the memory impairment, but also acts a potent anti-inflammatory agent for neuroinflammatory diseases. Conclusion: Our research for the first time provides the scientific evidence that consumption of black ginseng-enriched CMT extract as a brain tonic improves memory impairment. Thus, our study results can be taken as a reference for future neurobehavioral studies.

• Journal of Korean Elementary Science Education
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• v.24 no.2
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• pp.103-110
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• 2005

The purpose of this study was to investigate the relation between the learning styles and science process skills of students of the gifted class in elementary school. Subjects were forty-eight students of the gifted class who are in the fifth grade studying at the gifted class of S elementary school in Bucheon, M and Y elementary school in Incheon on learning styles and science process skills of students. Learning Style Profile (LSP) was used as instrument to survey learning style of students of the gifted class which was developed by NASSP, and consists of four categories (cognitive skills, perceptual response, orientation and teaming preferences) and twenty-four subscales. The results of this study were as follows: 1. In the learning styles test, students of the gifted class have higher scores of spatial skill, sequential processing skill, persistence orientation, manipulative preference, temperature preference and afternoon preference than general class students, but they have lower scores of discrimination skill and lighting preference, and there were statistically significant difference. 2. In science process skills test, there were statistically significant difference between students of the gifted class and general students. 3. In the correlation between the learning styles and science process skills, there was positive correlation of observing skill with spatial skill and manipulate skill of cognitive skill domain. For classifying skill, there was positive correlation with visual perceptual response, but was negative correlations with auditory and emotive perceptual response of perceptual response domain and with evening preference and verbal risk orientation of study preference domain. For measuring skill, there was positive correlation with sequential processing skill of cognitive skill domain. For formulating hypotheses, there was controlling variables, there was positive correlation with sequential processing skill and simultaneous processing skill of cognitive skill domain, and with verbal-spatial preference and early morning study preference of study preference domain. When planning and managing the gifted class, it will be beneficial and effective to consider the meaningful relations between the elements of loaming style and science process skills in order to improve science process skills.

• Journal of Gifted/Talented Education
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• v.21 no.4
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• pp.993-1009
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• 2011

The purpose of this study was to find the effects of RSM-based astronomical observation program about Astronomical Spatial Concept and Self-Directed Learning for the Scientific Gifted Students. For this purpose, this research developed RSM-based astronomical observation program. This program was totally consisted 10 lessen. there was 3 part in this program. It contained Preparation Stage (step 1-2), Observation Stage (step 3-8), Clean up Stage (step 9-10). To find the effects of RSM-based astronomical observation program on Astronomical Spatial Concept and Self-Directed Learning for Scientific Gifted Students. 20 participants was selected. these students were attended at a scientific gifted class(5th grade) of an elementary school located in Ulsan. First, Astronomical Spatial Concept was used to find the effect of the Astronomical Observation program based RSM. And the results were analyzed by SPSSWIN 18.0. The results of this study were as follows. First, RSM-based astronomical observation program was a positive effects on Astronomical Spatial Concept of the Scientific Gifted Students (t=3,875, p=.001). Second, RSM-based astronomical observation program was a positive effects on Self-Directed Learning of the Scientific Gifted Students (t=5.783, p=.000). According to this research, RSM-based astronomical observation program was verified to improve Astronomical Spatial Concept and Self-Directed Learning on the Scientific Gifted Students. It will be contribute on the curriculum construction of the gifted school or gifted class.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.790-794
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• 2008

This paper proposes a educational content display system based on spatial augmented reality with a multi-touch screen for effective learning with intuitive interaction. It is hardly expected that the existing display systems have major learning effects because the user-system interaction can only be achieved through buttons or switches, In contrast, the system proposed by this paper ensures more effective interaction through the user's movement, not through buttons or switches. This system uses a spatial augmented reality method to display images, thereby attracting the users' attention. In addition, it ensures the effective dissemination of information by providing visual images that are more realistic.

• Spatial Information Research
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• v.2 no.2
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• pp.175-188
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• 1994

A competitive learning network was proposed as unsupervised training method of remote sensing data, Its performance and computational re¬quirements were compared with conventional clustering techniques such as Se¬quential and K - Means. An airborne remote sensing data set was used to study the performance of these classifiers. The proposed algorithm required a little more computational time than the conventional techniques. However, the perform¬ance of competitive learning network algorithm was found to be slightly more than those of Sequential and K - Means clustering techniques.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Journal of the Korean earth science society
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• v.25 no.3
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• pp.176-184
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• 2004

The concept of 'the lunar and planetary phases' is very difficult to understand and students may have various misconceptions on this concept. A module drawing the lunar and planetary phases was developed with the application of the simplifying conditions method. The effects of instruction using the module drawing the lunar and planetary phases on the conceptual change and the achievement was investigated in the consideration of learners' characteristics (spatial perception ability, science inquiry ability, required pre-requested learning ability). Findings were as follows: 1) This module was effective for learners' conceptual change and achievement, 2) This module had a positive influence for development the learners' characteristics and conceptual change with the middle level of science inquiry ability, the middle and low level of required pre-requisite learning ability, and middle level of the spatial perception ability.

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• 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.