• Journal of Ginseng Research
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• 제41권2호
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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.

• 한국초등과학교육학회지:초등과학교육
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• 제24권2호
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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.

• 영재교육연구
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• 제21권4호
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• pp.993-1009
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• 2011

본 연구는 과학영재를 위한 울산과학관을 활용한 RSM(Regional Science Educational Resource Map; 지역 과학교육 자원지도) 기반 천체관측 프로그램을 개발하여 과학영재를 대상으로 체험학습 프로그램을 운영함으로써 그 효과성을 검증하고자 계획되었다. 이를 위하여 울산과학관에 설치되어 있는 천체투영실, 별보미 천체관측실 및 주관측실 등의 첨단 시설 및 기자재를 활용하여 RSM 기반 천체관측 프로그램(10차시)을 개발하였고, 이를 과학영재학생을 대상으로 운영한 후 이들의 천문학적 공간개념과 자기주도적 학습능력에 어떤 영향을 미치는지 알아보았다. 본 연구에서 개발한 RSM 기반 천체관측 프로그램은 준비단계(2차시), 관측단계(6차시), 정리단계(2 차시) 등 3단계로 구성하였으며, 관측단계에서는 천체투영실을 활용한 3D 시뮬레이션 별자리 관측(2차시), 밤 하늘의 별자리 안시 관측(2차시), 천체망원경을 활용한 망원경 관측(2차시) 등으로 구성하여 지역 과학교육 자원을 많이 활용할 수 있도록 구성하였다. RSM 기반 천체관측 프로그램이 과학영재의 천문학적 공간개념 및 자기주도적 학습능력에 미치는 효과를 살펴보기 위해 연구 대상은 울산과학관에서 운영하고 있는 우주과학 영재교실(초 5학년) 20명을 선정하였다. 검사도구는 천문학적 공간개념 및 자기주도적 학습능력 검사지를 사용하였고, 연구의 결과 분석은 종속표본 t검증으로 분석하였다. 연구의 결과는 울산과학관을 활용한 천체관측 프로그램은 과학영재의 천문학적 공간개념(t=3.371, p=.003) 형성에 긍정적인 효과가 있었고(p<.05), 과학 영재의 자기주도적 학습능력(t=2.371, p=.028)에도 긍정적인 효과가 있었다(p<.05). 본 연구를 통해 RSM 기반 천체관측 프로그램은 과학영재들의 천문학적 공간개념 형성과 자기주도적 학습능력 신장에 도움이 된다는 것을 알 수 있었고, 특히 각 지역의 초, 중등 영재학급의 심화학습의 기회로써 RSM 기반 천체관측 프로그램을 활용할 수 있을 것으로 예상된다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.790-794
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• 2008

본 논문에서는 사용자의 직관적인 상호작용을 통하여 효과적인 정보 획득이 가능하도록 하기 위한 방안으로 멀티터치스크린과 접목된 공간 증강 현실 기반 교육용 콘텐츠 전시 시스템을 제안한다. 기존 시스템의 경우 사용자와 시스템의 상호작용은 단순히 버튼 및 스위치 조작을 통하여 다음 단계로 넘어가는 것에 불과하여 실질적인 학습효과는 기대하기 어려운 수준이었다. 하지만 본 논문에서 제안하는 교육용 콘텐츠 전시 시스템에서는 단순한 버튼 조작이 아닌 콘텐츠진행에 알맞은 사용자의 움직임을 통하여 진행을 함으로써 보다 효과적인 상호작용이 가능하도록 한다. 또한, 본 시스템은 영상을 출력하는 방법으로 공간 증강 현실 기법을 활용함으로써 사용자의 호기심을 유발시키며, 사실감 있는 시각정보를 제공함으로써 효과적인 정보 전달이 이루어지도록 한다.

• Spatial Information Research
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• 제2권2호
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• pp.175-188
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• 1994

본 연구에서는 최근 많은 분야데서 패턴인식을 위한 효과적인 기법으로 이용되고 있는 신경망 기법을 원격탐사자료의 군집화 기법으로서 적용하고자 하였다. 이를 위해 선택된 신경망 모델은 경쟁학습 신경망이며 이를 구성하는 각종 변수들을 재구성하여 원격탐사자료의 군집화를 위한 신경망모델을 설정하였다. 본 신경망을 이용한 군집화 기법은 항공기를 이용하여 획득된 원격탐사자료를 이용하여 순차적(sequential)군집화 기법 K 평균 군집화 기법과 비교되었다. 계산시간은 순차적 기법이나 K 평균기법에 비하여 더 많이 소요되나 정확도면에 있어서는 비교적 우수한 결과를 나타냈다.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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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.

• 한국지구과학회지
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• 제25권3호
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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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• 제17권3호
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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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• 제16권9호
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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.

• 대한원격탐사학회지
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• 제35권5_1호
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• pp.681-692
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• 2019

이 논문에서는 작물 분류를 목적으로 작물의 시공간 특징을 고려할 수 있는 딥러닝 모델 2D convolution with bidirectional long short-term memory(2DCBLSTM)을 제안하였다. 제안 모델은 우선 작물의 공간 특징을 추출하기 위해 2차원의 합성곱 연산자를 적용하고, 추출된 공간 특징을 시간 특징을 고려할 수 있는 양방향 LSTM 모델의 입력 자료로 이용한다. 제안 모델의 분류 성능을 평가하기 위해 안반덕에서 수집된 다중시기 무인기 영상을 이용한 밭작물 구분 사례 연구를 수행하였다. 비교를 목적으로 기존 딥러닝 모델인 2차원의 공간 특징을 이용하는 2D convolutional neural network(CNN), 시간 특징을 이용하는 LSTM과 3차원의 시공간 특징을 이용하는 3D CNN을 적용하였다. 하이퍼 파라미터의 영향 분석을 통해, 시공간 특징을 이용함으로써 작물의 오분류 양상을 현저히 줄일 수 있었으며, 제안 모델이 공간 특징이나 시간 특징만을 고려하는 기존 딥러닝 모델에 비해 가장 우수한 분류 정확도를 나타냈다. 따라서 이 연구에서 제안된 모델은 작물의 시공간 특징을 고려할 수 있기 때문에 작물 분류에 효과적으로 적용될 수 있을 것으로 기대된다.