• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• 대한가정학회지
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• 제45권8호
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• pp.13-24
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• 2007

This study was conducted to evaluate the relationship between the actual state of extra curricula education for kindergarteners and maternal beliefs regarding child rearing. This study included 238 mothers who had a child that was either four or five years old and currently attending private kindergarten in Seoul, Korea. The major findings of this study were as follows: 1) 222 of the subjects indicated that their children participated early in extra curricula education, and 56 of the respondents reported that their children were involved in four types of extra curricula education. 2) In general, the respondents indicated that they felt extra curricula activities were more important for instilling values regarding humanity to children of very young ages than for learning and that the environment in which a child is raised is more important than maturity when rearing children. 3) Parental beliefs regarding humanity and learning were significantly correlated with the actual state of extra curricula education in kindergarteners.

• 한국초등수학교육학회지
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• 제18권1호
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• pp.19-35
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• 2014

본 연구는 수학 교과서에서 스토리텔링의 효과를 알아보기 위해, 초등학교 3~4학년 군 수학 교과서를 실험하고 있는 실험학교 중에서 3개교를 택하여 교사와 학생을 대상으로 설문지 조사를 하였다. 조사 결과, 교사들은 교과서의 스토리텔링에 대하여 전반적으로 긍정적인 인식을 하고 있었다. 특히 스토리텔링이 학습 동기유발이나 의사소통과 수학내용의 학습지도에 상당히 도움이 된다고 하였다. 또 학생들도 수학 교과서의 스토리텔링에 대하여 전반적으로 긍정적인 반응을 보였고, 특히 3학년 학생들이 4학년보다 더 긍정적이었다. 구체적으로 수업에서 스토리텔링은 재미있다, 학원공부와 다르다는 내용에 상당히 긍정적이었다. 그러나 하위수준의 학생들은 변화가 없어 스토리텔링의 도입의 의도와 차이가 있어 이에 대한 대책이 필요해 보였다.

• 한국학교수학회논문집
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• 제17권2호
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• pp.193-205
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• 2014

본 연구는 개인별 지도에 초점을 둔 코칭(Coaching)학습법을 대학수학에 적용하여 학습능력과 성취도에서의 효과를 검증하였다. 대학수학에서 코칭학습법에 참여한 집단은 비참여 집단보다 학업성취도에서 유의미한 효과가 있었다. 또한, 학습부진 집단이 우수 집단보다, 여학생이 남학생보다 학업성취도에서 더 많이 향상되었다. 본 연구는 제한된 인원과 시간으로 인해 대학수학에서 코칭학습법의 효과에 대해 성급한 일반적 결론을 이끌어 내기에는 부족함이 있다. 하지만 대학수학에서 코칭학습법이 처음 적용되었다는 점과 수학교과에서 새로운 교수법으로서 토대를 마련했다는 것에 의미가 있다.

• 응용통계연구
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• 제28권1호
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• pp.1-8
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• 2015

인터넷 트래픽의 증가로 인하여 네트워크의 보안 문제가 중요한 문제로 대두되고 있다. 그 중에서도 특히 P2P 트래픽의 증가는 모든 서버의 관리자에게는 해결해야할 중요한 문제로 대두되고 있다. 서버에서 네트워크 트래픽을 조사하여 문제가 있는 트래픽을 미리 차단하는 것은 서비스 품질의 향상과 자원의 효율적인 사용 측면에서 바람직하나 오가는 패킷의 내부정보를 조사하는 것은 개인정보보호 차원에서 문제가 있을 수 있으며 시간과 노력이 많이 소요되므로 요즘은 통계적인 기계학습의 방법을 이용하여 이상 트래픽을 찾아내는 연구가 주를 이루고 있다. 본 연구에서는 최근의 기계학습방법 중에서 널리 쓰이는 방법들을 비교 연구하여 그 결과 랜덤포리스트(random forest)라고 불리는 방법의 우수함을 보였다.

• 안보군사학연구
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• 통권15호
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• pp.31-55
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• 2018

It has been fifteen years since military science was introduced into private and public universities since 2004. The department focuses on the improvements of the South Korean Army quality based on the Korean Army's traits including: an increase of power in the armed force and operations through research, development, and the expansion of a cooperation between the public (civilians) and military. Approximately, four hundred students from various universities in the military science department graduate in order to become an officer. The fourth industrial revolution causes structural transformation to our lives. Through the use of Artificial Intelligence (AI,) war and the military as a whole will be altered significantly particularly with regard to efficiency. Nevertheless, it is important for us to train officers in creative ways so that they can deal with situations where machines will be unable to handle situations. Considering this change in our lives, it is necessary for the military science departments to change the way to teach and train their students. In order to accomplish this goal, we need to introduce a method called "Flipped Learning" and during the process all the members need to participate and communicate in an interactive way. By doing this, the military science departments will play an important role by improving human resource in terms of military and national security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1653-1670
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• 2022

The fear that location privacy may be compromised greatly hinders the development of location-based service. Accordingly, some schemes based on the distributed architecture in peer-to-peer network for location privacy protection are proposed. Most of them assume that mobile terminals are mutually trusted, but this does not conform to realistic scenes, and they cannot make requirements for the level of location privacy protection. Therefore, this paper proposes a scheme for location attribute-based security authentication and private sharing data group, so that they trust each other in peer-to-peer network and the trusted but curious mobile terminal cannot access the initiator's query request. A new identifier is designed to allow mobile terminals to customize the protection strength. In addition, the caching mechanism is introduced considering the cache capacity, and a cache replacement policy based on deep reinforcement learning is proposed to reduce communications with location-based service server for achieving location privacy protection. Experiments show the effectiveness and efficiency of the proposed scheme.

• Educational Technology International
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• 제18권2호
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• pp.159-191
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• 2017

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on Learning Management System(LMS) help understanding instructors' pedagogical approach and the integration level of technologies. Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. As a case, we have clustered academic courses based on the usage levels and patterns of LMS in higher education using those three clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and suggest that academic leaders and university staff should look into the usage levels and patterns of LMS with more elaborated view and take an integrated approach with different analytic methods for their strategic decision on development of LMS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.924-926
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• 2021

AI (Artificial Intelligence) is being utilized in various fields and services to give convenience to human life. Unfortunately, there are many security vulnerabilities in today's ML (Machine Learning) systems, causing various privacy concerns as some AI models need individuals' private data to train them. Such concerns lead to the interest in ML systems which can preserve the privacy of individuals' data. This paper introduces the latest research on various attacks that infringe data privacy and the corresponding defense techniques.