• Toxicological Research
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• v.17
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• pp.257-261
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• 2001

Recently, a traditional medicine called Gouteng-san, which consists of eleven herbs, was reported to be effective in treating vascular dementia with a double-blind, placebo-controlled study. Gout-eng-san is also used for patients with vascular dementia in combination with Si-Wu-Tang. The effect of Gouteng-san and Si-Wu-Tang on deficit of learning behavior was investigated using step-down passive avoidance task in mice. Hot-water extract of Gouteng-san (1.5 and 6 g/kg, p.o.) significantly prolonged the step-down latency shortened by scopolamine. The extract of Uncaria hook (150 mg/kg, p.o.), one of the component herb of Gouteng-san, significantly prevented the decrease in the latency after scopolamine. Hot-water extract of Si-Wu-Tang (1.5 and 6 g/kg of dried herbs, p.o.) prevented dose-dependently scopola-mine-induced disruption qf learning behavior. Si-Wu-Tang also prevented the ischemia-induced deficit of learning behavior. Both hot water extract of peony and angelica (1.5 g/kg, p.o.), which are component herbs qf Si-Wu-Tang, prevented the scopolamine-induced learning behavior deficit. Scopolamine (10 uM) suppressed long-term potentiation (LTP) of population spike in the CA1 region of the rat hippocampal slices. Peoniflorin (0.1~ 1uM) extracted from paeony root significantly ameliorated scopolamine-induced inhibition of LTR These results suggest that improvement of deficit of learning behavior by Gouteng-san and Si-Wu-Tang is mediated by direct and/or indirect activation of the cholinergic system in the brain.

• Convergence Security Journal
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• v.5 no.4
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• pp.33-40
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• 2005

As e-learning studying is activated, learner's requirement increased. Therefore, need correct e-learning model augmented requirement of learner and new ubiquitous surrounding. In this treatise when, proposed to supplement studying contents relationship conversion service and cooperation studying service function to LMS that analyze existing e-learning model's limitation for ubiquitous environment e-learning model that can study regardless of, ubiquitously some contents and do based on SCORM ubiquitous-network and next generation sensor technology etc. Learning form conversion service senses a learner's surrounding situations and recognize his/her body condition using smart sensor technology and provides the learner with contents in the optimal form. Using sensing projects like Orestia and SOB, users can more effective collaborative learning service.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.435-440
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• 2007

본 연구는 무리행동과 인지적 유용성을 중심으로, e-learning 컨텐츠 구매에 미치는 영향을 e-learning 수강경험자 집단과 경험하지 못한 잠재구매자 집단의 차이점을 경험적인 측면에서 밝히고자 시도하였다. 전체 528명의 표본을 경험자(395명)와 비경험자(133명)로 나누어 PLS(Partial Least Square)를 통하여 분석한 결과 e-learning 구매 경험자는 인지적 유용성이 구매의도에 주는 영향이 무리행동의 영향보다 큰 것으로 나타났고, 잠재 구매자는 무리행동이 구매의도에 주는 영향이 더 큰 것으로 나타났다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.99-109
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• 2011

Ubiquitous computing technology makes an impact on the appearance of u-learning and presents an advanced direction of futuristic school education. In ubiquitous learning environments, various embedded computational devices will be pervasive and interoperable across the network for supporting the learning, so users may utilize these devices anytime anywhere. An important next step for ubiquitous learning is the introduction of context-aware learning service that employing knowledge and reasoning to understand the local context and share this information in support of intelligent learning services. However, the existing studies on design and application of ontology context model to support context-aware service in actual school environments are incomplete state. This paper, therefore, suggests a scheme of constructing ubiquitous learning space for existing school network by introducing USN to support context-aware ubiquitous learning services. This paper, also, designs an ontology based context model for ubiquitous school environments which describes context information through OWL. To determine the suitability of proposed ubiquitous learning space and ontology context model, we implement some of context-aware learning services in the ubiquitous learning environments.

• Computers and Concrete
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• v.32 no.6
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• 한국정보교육학회:학술대회논문집
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• 2007.08a
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• pp.96-101
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• 2007

u-learning (Ubiquitous Learning) 환경에서는 학습자들이 언제, 어디에서나 어떤 단말기로도 자유롭게 학습할 수 있는 학습자 중심의 교육과정이 가능해야 한다. 최근 각광을 받고 있는 태블릿 (Tablet) PC는 펜을 주로 입력 매개로 활용하고, 음성을 인식하여 이를 문자로 변환하여 저장할 수 있는 기능도 가지고 있어 초등교육 현장에 적합한 단말기이다. 또한 PBL (Problem-based Learning)은 학습자들이 자기주도적으로 문제를 해결해 가는 과정에서 문제해결력과 비판적 사고 기능을 신장시킬 수 있는 학습형태로 태블릿 PC 기반의 PBL 학습시스템은 u-learning 환경에서 학습자 중심의 교육과정 실현에 적합한 시스템이다. 본 연구에서는 태블릿 PC 기반의 PBL 학습시스템의 인터페이스 설계 방안을 제시해 보았다. 본 연구에서 제시하는 사용자 인터페이스의 특징은 다음과 같다. 첫째, 학습의 프로세스를 프로젝트의 목적 및 과제 파악 단계, 학습계획 수립단계, 자료의 수집 및 정리단계, 프로젝트 마무리 단계로 구분하고, 각 단계에서의 학습자의 활동을 지원하는 시스템으로 구성한다. 둘째, 태블릿 PC 기반에서의 학습 활동에 서투른 학습자도 직관적으로 접근 할 수 있도록 인터페이스에 아이콘을 적절하게 활용한다. 셋째, 태블릿 PC에 펜이나 음성으로 입력된 자료를 문자, 도형, 이미지로 손쉽게 저장하고 축적할 수 있도록 한다. 넷째, 학습자들간의 상호작용과 교사의 피드백을 손쉽게 할 수 있도록 게시판, 자료실, 통합 메시지함의 기능을 활성화한다.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.148-155
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• 2014

The purpose of this research was to analyze the effects of smart learning. By using meta analysis method, twenty MA and Ph.D degree papers published from 2006 to 2013 were analyzed and 104 effect sizes were calculated. Followings were the results of the research: (a) Smart learning turned out to be more statistically effective comparing to traditional education. The total mean effect size was .886 and the value of U3 was 66.53%. (b) All effect size of sub dependent variables(ie, academic achievement, learning satisfaction, learning attitude) were also effective by adapting smart learning. (c) The moderated variables likes learner characteristics, learning content, and interaction had high effect sizes. Operation system variable had a low effect size but it was not significant.

• Korean Journal of Computational Design and Engineering
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• v.15 no.5
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• pp.375-382
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• 2010

As the workers are getting old, they need to be expert in their fields. Expertise is an invaluable means of shipbuilding industries because of the technological advancements. Therefore, the workers have to attend training seminars and upgrade their technical knowledge. In shipbuilding industries, most workers operate transporters based on experience not on learning from specialization in the shipyard. It is needed for the workers of transporters to be educated technical manual and system. Thus, this research shows that educational system which is interactive and very effective with Augmented Reality(AR) for the non-specialist workers. The educational interactive system based on AR is very supportive and worth for the workers. This study considers the system which makes the workers reduce malfunction of the products and mistakes.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.