• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• 한국군사과학기술학회지
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• 제23권2호
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• pp.139-146
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• 2020

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• 기본간호학회지
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• 제11권3호
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• pp.292-298
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• 2004

Purpose: this study was to describe patient learning needs and the relationship between health promoting behavior and health concept with women with disabilities. Methods: A descriptive survey design was used and the SPSS 11.0 program was used for data analysis, which included t-test, ANOVA and Pearson correlation coefficients. The women (n=50) were in-patients in a rehabilitation center. Results: The study results indicate that they had high levels of patient learning needs and the most important information for patient learning needs was support and care. Patient learning need was correlated with health promoting behavior. Conclusions: The findings of this study give useful information to construct further studies in educational programs and rehabilitation nursing care and to support a healthcare system for women with disabilities.

• 디지털융복합연구
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• 제10권7호
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• pp.173-178
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• 2012

본 논문은 B대학에 재학 중인 학부생들의 연구윤리와 학습윤리의식을 중심으로 연구 분석한 내용을 기술하였다. 제시된 데이터는 B대학 학부생들을 위한 연구학습윤리 교육방향을 수립할 목적으로 연구윤리, 학습윤리에 대한 인지도 설문을 통해 얻었고, 인지도 분석을 통해 학부생들의 윤리의식 수준과 윤리교육의 필요성을 인식할 수 있었다. 본 연구 결과는 B대학의 윤리교육 가이드라인 수립에 도움이 될 것이다.

• 산업경영시스템학회지
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• 제29권2호
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• 공학교육연구
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• 제22권1호
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• pp.22-28
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• 2019

The purpose of this study is to develop instructional design model of flipped learning suitable for engineering education field and to draw out effects and improvements by applying it to actual lessons for engineering college students. Literature review and case studies were conducted to achieve the purpose of the study. For a case study, flipped learning was applied to 'creative problem solving methodology' which is a liberal arts course of engineering college at D university in Gyeonggi-do. As a result of the literature review, the PARTNER model was applied and weekly instructional guide was presented by each stage. In addition, the results of analysis on the reflection journal showed that the students were more able to achieve the deepening learning stage through active participation in class than the existing class, and found that they had a more challenging plan after the class.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.110-113
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• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• 공학교육연구
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• 제13권6호
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• pp.3-13
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• 2010

본 연구의 목적은 공과대학생의 학습양식 유형을 분석하고, 학습양식에 따른 의사소통 불안 수준의 차이를 검증하는 것이다. K대학교에 재학 중인 공과대학 1학년생 405명을 대상으로 학습양식을 분석한 결과, 감각적 학습자는 61%, 시각적 학습자는 73.1%, 숙고하는 학습자는 80%, 총체적 학습자는 66.7%로서 우세한 비율을 차지하는 나타났다. 성별에 따른 학습양식의 차이에서는 정보처리 차원에서 유의한 차이를 보였으며, 여자가 남자보다 숙고하는 학습양식을 선호하는 것으로 나타났다. 학습양식에 따른 의사소통 불안 수준의 차이를 분석한 결과, 정보지각과 정보처리 차원에서 유의한 차이를 보였다. 감각적 학습자가 직관적 학습자보다, 숙고하는 학습자가 적극적 학습자보다 더 높은 수준의 의사소통 불안을 느끼는 것으로 나타났다. 연구결과에 기초하여 의사소통 교육 프로그램을 실행할 때, 학습자의 학습양식을 고려하여 의사소통 불안 수준을 최소화할 수 있는 전략에 대한 시사점을 제안하였다.

• 디지털콘텐츠학회 논문지
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• 제18권4호
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• pp.671-679
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• 2017

영어 교육 전문가 R. Ellis를 비롯한 다수의 외국어학습연구자의 연구에 따르면, 외국어를 학습자의 실제상황에서 반복적으로 학습함으로써 영어의 실제성을 높이는 학습 방법이 효과적이며 중요한 방법이다. 본 논문에서는 상황인식 서비스가 접목된 외국어 학습 서비스를 제안함으로써 영어 학습의 실제성을 높이는 외국어 학습 콘텐츠의 개발을 모색한다. 이와 같은 접근은 실제 환경에서 무의식인 외국어 습득을 돕는 디지털 교육 콘텐츠에 대한 연구와 실시간 다중 센서 데이터를 기반으로 학습자의 경험적 특성을 분석하는 연구에 기반 한다.