• 한국학교수학회논문집
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• 제22권3호
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• pp.241-256
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• 2019

본 연구는 '확률과 통계' 교과서에 제시된 과제의 맥락 유형과 과제를 수행할 때 요구되는 인지적 역량이 학생들에게 어떠한 학습기회를 제공하는지 살펴보았다. 이를 위해 2015 개정 수학과 교육과정에 따른 '확률과 통계' 검정교과서 전체 9권을 분석한 결과, 맥락 기반 과제(CF유형, RE유형)는 각 교과서마다 전체 과제 개수의 67.5%부터 78.0%로 나타났지만 실생활에 연관된 본질적인 과제(RE유형) 비율은 0.4%부터 2.0%로 나타나 교과서에 제시된 대부분의 맥락 기반 과제는 실생활 소재를 위장한 과제임을 알 수 있었다. 그리고 맥락 기반 과제의 인지적 역량은 각 교과서마다 재생산(Rp)범주에 속하는 과제 비율은 29.6%부터 50.0%로 다양하게 나타났고, 연결(Co)범주 과제 비율은 33.8%부터 54.3%, 반성(Rf)범주 과제 비율은 8.8%부터 20.0%로 나타나 과제수행 시 학생들이 반성적 인지 과정을 경험할 수 있는 학습기회는 다소 충분하지 않음을 알 수 있었다.

• International Journal of Contents
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• 제15권3호
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• International Journal of Advanced Culture Technology
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• 제8권2호
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• pp.209-215
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• 2020

Russia has an interesting history as a donor, recipient and re-emerging donor in international development assistance (IDA). This article introduces the history, policy and challenges of Russian IDA, and provides suggestions for such challenges. The main barrier to Russian IDA is the absence of a central government agency and Russia can learn from other country's experience. Concerning lack of data on the provision of assistance to each sector of IDA and the large number of recipient countries, Russia can learn from Korea particularly in education sector. With respect to building a system ensuring the efficiency of the Russian IDA works, a tool for analysis of the effect of the Russian IDA programmes may be drawn in consultation with international institutions or successful programmes of other donor countries.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• 한국가정과교육학회지
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• 제9권1호
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• pp.19-37
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• 1997

The aim of this study is to research and analyse how junior-high students, both male and female in Inchon area, are recognizing the contents of the curriculum in the subject of home economics and how effectively they are learning and applying it in their actual life. 772 students, both male and female, who started to learn the subject of home economics from the 7th grade as compulsory are the respondents, and the survey is done by using questionnaire. The result shows that after taking the course of home economics, both male and female students have got more positive view on the necessity of learning the subject. But still, on the whole, female students are more intersted and more active than males the subject in learning. As for food and nutrition part, large percentage of the respondents, both male and female, answer that it is very helpful. They tend to be on more balanced diet and when they purchase food or when they eat at restaurant they refer what they learn about nutrition at school more often than not. A number of the students are re-practicing cooking at home after they learn it at school. Also the fact in the survey shows that more and more mothers are getting active in asking their children to re-practice cooking. One of the difficulties for male students to take the course is stereo-typed thinking on the separate role of man and woman in the family. But many of them started cooking some food, even though it is very simple, and the survey shows that their interest in nutrition and health increased after they were initiated into this course.

• 한국응급구조학회지
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• 제5권1호
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• pp.73-88
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• 2001

Post these safety tips in any swimming area for all swimmers to read. 1. Always swim with companions. Swim only in area well supervised by lifeguards. 2. Never drink alcohol or use drugs when you're swimming or boating. 3. Always check the water depth. Walk in from shore or ease in from the dock or the edge of the pool. 4. Know the limits of your own swimming abilities. If you're a good swimmer, don't tempt nonswimmers or beginner swimmers to try to keep up with you. 5. Keep any eye on younger swimmers at all times. 6. Learn the proper way to dive in the water safely. Follow guidelines for safe diving. 7. Follow the lifeguards instructions and respect their judgment. Never fake an Emergency. Obey all swimming rules.

• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.323-332
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• 2005

본 논문에서는 웹 기반에서 학습자의 학습 성취도 향상을 도모할 수 있는 다양한 학습 내용 구성 환경을 제공할 수 있으며, 실시간으로 과제물을 평가할 수 있는 e-Learning 시스템을 개발하였다. 우리는 학습자의 특성과 흥미를 유발할 수 있는 특징들을 고려하여 문제풀이와 Quiz를 갖춘 학습 내용 구성을 개발하여, 학습자 스스로가 동적으로 다양한 학습 내용 구성 환경을 선택할 수 있도록 하였다. 그리고 과제물 시스템은 객관식 및 서술형 과제물을 교수자와 학습자간에 상호 실시간으로 처리할 수 있도록 개발하였다.

• Nuclear Engineering and Technology
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• 제53권2호
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• pp.522-531
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• 2021

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.

• 한국인터넷방송통신학회논문지
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• 제19권2호
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• pp.135-141
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• 2019

최근 강화 학습을 통해 게임을 학습하는 인공지능 에이전트를 만드는 연구가 활발히 진행되고 있다. 게임을 에이전트에게 학습 시킬 때 어떠한 딥러닝 활성화 함수를 사용하는지에 따라 그 학습 성능이 달라진다. 본 논문은 2D 슈팅 게임 환경에서 에이전트가 강화 학습을 통해 게임을 학습할 경우 어떤 활성화 함수가 최적의 결과를 얻는지를 비교 평가 한다. 이를 위해 비교 평가에서 사용할 메트릭을 정의하고 각 활성화 함수에 따른 메트릭 값을 학습 시간에 따라 그래프로 나타내었다. 그 결과 ELU (Exponential Linear Unit) 활성화 함수에 1.0으로 파라미터 값을 설정할 경우 게임의 보상 값이 다른 활성화 함수보다 평균적으로 높은 것을 알 수 있었고, 가장 낮은 보상 값을 가졌던 활성화 함수와의 차이는 23.6%였다.

• 건축역사연구
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• 제2권1호
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• pp.126-135
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• 1993

Italian Rationalism held a specific position in the Modern Movements of Architecture, due to the fact that Italian Rationalism could not totally escape from the classical tradition of Italy. Until the seventies, Italian Rationalism had been criticized for having made no contribution to the progressive aspects of the Modern Movements owing to the very keeping of tradition. After the seventies, however, there emerged a movement which tries to reinterprete the Modern Movements of Architecture in relation to tradition and under this new situation, Italian Rationalism is believed to have a historical possibility of unifying tradition with modernity. This study is to show how Italian Rationalism struggled with the issue of tradition, why Italian Rationalism was under-evaluated and which historic lesson we can learn from it in the contemporary days of the revivalistic Post-Modernism.