• The Journal of Korean Association of Computer Education
• /
• v.12 no.3
• /
• pp.75-83
• /
• 2009

Moving picture has become the important media in education with expanded e-learning paradigm, but Korea Educational Metadata has limitation about representing information of lots of events and objects in moving picture. Announcing the MPEG-7 specification the information of lots of events and objects in it can be presented in terms of semantic and structural description of moving pictures. In this paper moving picture searching system model that integrates two metadata specifications, such as KEM and MPEG-7, is proposed. In this model one ontology to combine two metadata specifications is designed, and the other ontology about knowledge of a subject matter is added to search efficiently in searching system. As some moving picture data from Edunet were selected and stored in our server, our prototype of searching system using MPEG-7 and KEM shows the results that we are expected.

• Industry Promotion Research
• /
• v.1 no.2
• /
• pp.63-69
• /
• 2016

This study analyzed the effect of Chung-nam province small manufaturing firm male workers' participation in training on perceptions of effectiveness. The study results showed that Off-the-job training satisfaction significantly affected satisfaction of training system and helpfulness of self-development. It means that workers considered lecture or e-learning method is better than on-the-job training, so it is suggested that small manufacturing businesses should establish more organized training system for on-the-job training because workers' perception of satisfaction and effectiveness of OJT was lower than Off-JT. This study provided implications for verifying the effectiveness depend on the type of training and presenting important points to enhance workers' satisfaction of education and training.

• The Journal of Korean Association of Computer Education
• /
• v.12 no.6
• /
• pp.41-51
• /
• 2009

The purpose of this study was to suggest policy implications for bridging digital divide of the students of multicultural background. Specifically, through maximizing equity of Information technology, the current study suggested the need for greater intervention and comprehensive strategies in the interests of a socially inclusive information society, of benefit to the IT disadvantaged. To achieve the research goal, I reviewed a variety of policy reports and previous studies related to the issue of digital divide and multicultural education, and draw useful implications for the policy change. The major research results were as the following: a policy change in a support of personal computer toward public place rather than individual home, widening use of e-learning service as a tool for bridging the digital divide, and the training for teachers and parents to provide an effective education to reduce the digital divide.

• Communications of Mathematical Education
• /
• v.24 no.3
• /
• pp.731-744
• /
• 2010

Nowadays in school mathematics, the skill and method for solving problems are often emphasized in preference to the theoretical principles of mathematics. Students pay attention to how to make an equation mechanically before even understanding the meaning of the given problem. Furthermore they do not get to really know about the principle or theorem that were used to solve the problem, or the meaning of the answer that they have obtained. In contemporary textbooks the conic section such as circle, ellipse, parabola and hyperbola are introduced as the cross section of a cone. But they do not mention how conic section are connected with the quadratic equation or how these curves are related mutually. Students learn the quadratic equations of the conic sections introduced geometrically and are used to manipulating it algebraically through finding a focal point, vertex, and directrix of the cross section of a cone. But they are not familiar with relating these equations with the cross section of a cone. In this paper, we try to understand the quadratic curves better through the analysis of the discussion made in the process of the discovery and eventual development of the conic section and then seek for way to improve the teaching and learning methods of quadratic curves.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.10
• /
• pp.1010-1014
• /
• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.445-445
• /
• 2018

기후변화에 따른 환경오염은 21세기 인류에게 가장 심각한 문제 중의 하나로 대두되고 있다. 환경적인 측면에서 하천오염은 경제적으로 많은 문제를 발생시키고 있다. 이러한 하천오염 문제를 해결하기 위해서는 오염물질의 농도 측적 및 데이터 축적이 필수적이라 할 수 있다. 그러나 일반적으로 오염물질 부하량에 대한 직접적인 측정은 비용 측면에서 쉽지 않은 것이 사실이다. 또한 실시간으로 BOD, COD, TN, TP 등의 자료를 이용하여 예측하는 것에는 자료의 부족성으로 인해 한계가 있다. 본 연구에서는 구글의 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 기계학습을 통한 하천오염 예측을 목적으로 하고 있다. 기계학습을 위하여 텐서플로우를 활용하여 RNN, LSTM 인공신경망 모형을 구축하였다. 하천오염의 학습과 예측을 위해 결과치 분석을 위한 자료로는 금강 유역에 위치한 옥천 관측소 충청북도 옥천군 이원면 이원대교에 위치한 $36^{\circ}14'31.0''N$ $127^{\circ}40'02.6''E$의 관측소에서 BOD, COD, DO, 부유물질의 자료를 사용하였다. 모형의 학습을 위해서 입력자료는 수위, 유량, 평균기온, 평균풍속 자료를 2004년 ~ 2017년까지의 14년간의 자료를 사용하였다. 연구를 위해 BOD, COD, DO 부유물질 자료는 물환경정보시스템(http://water.nier.go.kr/)의 자료를 활용하고 수위, 유량등의 자료는 국가수자원관리종합정보시스템 (http://www.wamis.go.kr/)의 자료를 사용하였다. 그러나 수온, 수위, 풍속등의 자료는 일 자료가 있는가 반면 BOD, COD, TN, TP등의 자료는 일 자료가 있지 않아 이를 원활히 활용할 수 있도록 예측을 위한 결과치의 선형보간법을 통해 일 자료를 획득한 후 연구를 하였다. RNN, LSTM의 분석 시 학습속도, 반복시행횟수 sequence length의 길이 등의 값을 조절 하면서 결과치를 분석하였다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.287-294
• /
• 2021

A Python-based LSTM model was constructed using a Tensorflow backend to estimate the amount of outflow during floods in the Gokgyo-cheon basin flowing into the Sapgyo Lake. To understand the effects of the length of input data used for learning, i.e., the sequence length, on the performance of the model, the model was implemented by increasing the sequence length to three, five, and seven hours. Consequently, when the sequence length was three hours, the prediction performance was excellent over the entire period. As a result of predicting three extreme rainfall events in the model verification, it was confirmed that an average NSE of 0.96 or higher was obtained for one hour in the leading time, and the accuracy decreased gradually for more than two hours in the leading time. In conclusion, the flood level at the Gangcheong station of Gokgyo-cheon can be predicted with high accuracy if the prediction is performed for one hour of leading time with a sequence length of three hours.

• Communications of Mathematical Education
• /
• v.33 no.3
• /
• pp.395-423
• /
• 2019

The purpose of the study is to analyze the levels of cognitive demands and components of the reasoning process presented in the mathematical sequence section of three high school mathematics textbooks in order to provide implications for the development of reasoning tasks in the future mathematics textbooks. The results of the study have revealed that most of the reasoning tasks presented in the mathematical sequence section of the three high school mathematics textbooks seemed to require low-level cognitive demands and that low-level cognitive demands reasoning tasks required only a component of one reasoning process. On the other hand, only a portion of the reasoning tasks appeared to require high-level of cognitive demands, and high-level cognitive demands reasoning tasks required various components of reasoning process. Considering the results of the study, it seems to suggest that we need more high-level cognitive demands reasoning tasks to develop high-level cognitive reasoning that would provide students with learning opportunities for various processes of reasoning, and that would provide a deeper understanding of the nature of reasoning.

• Journal of the Korea Society for Simulation
• /
• v.29 no.4
• /
• pp.1-7
• /
• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• Journal of Environmental Health Sciences
• /
• v.48 no.2
• /
• pp.96-105
• /
• 2022

Background: Pyrethroid insecticides account for more than 30% of the global insecticide market and are frequently used in agricultural settings and residential and public pest control among the general population. While several animal studies have suggested that exposure to pyrethroids can alter glucose homeostasis, there is only limited evidence of the association between environmental pyrethroid exposure and diabetes in humans. Objectives: This study aimed to report environmental 3-phenoxybenzoic acid (3-PBA) concentrations in urine and evaluate its association with the risk of diabetes in Korean adults. Methods: We analyzed data from the Korean National Environmental Health Survey (KoNEHS) Cycle 2 (2012~2014) and Cycle 3 (2015~2017). A total of 10,123 participants aged ≥19 years were included. Multiple logistic regressions were used to calculate the odds ratios (ORs) for diabetes according to log-transformed urinary 3-PBA levels. We also evaluated age, sex, education, monthly income, marital status, alcohol drinking, physical activity, urinary cotinine, body mass index, and sampling season as potential effect modifiers of these associations. Results: After adjusting for all the covariates, we found significant dose-response relationships between urinary 3-PBA as quartile and the prevalence of diabetes in pooled data of KoNEHS Cycles 2 and 3. In subgroup analyses, the adverse effects of pyrethroid exposure on diabetes were significantly stronger among those aged 19~39 years (p-interaction<0.001) and those who consumed high levels of cotinine (p-interaction=0.020). Conclusions: Our findings highlight the potential diabetes risk of environmental exposure to pyrethroids and should be confirmed in large prospective studies in different populations in the future.