• Convergence Security Journal
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• v.5 no.1
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• pp.29-34
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• 2005

Internet becomes absolutely necessary tools due to rapid progress of information technology. Educational correspondence about an age of information demand is focused on a learner and remote education based on information technology WBI(Web Based Instruction) is a formation that remotly educate a learner using web, possible mutual reaction between instructor and learner, submit various studying material, has a good point to overcome spatial restriction. Internal and external standardization working is accelerated and recently XML security studies are activated using XML which is next generation web standard document format. In this paper, we propose multi-Certification of agent system using XML digital signature to satisfy security requirement.

• Journal of Elementary Mathematics Education in Korea
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• v.7 no.1
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• pp.1-22
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• 2003

This study was done in order to suggest a desirable organizational and developmental strategy of electronic textbooks for elementary school mathematics. First of all, it was intended to set up the exact definition of electronic textbooks for elementary school mathematics by surveying previous researches and examinations regarding elementary mathematics and electronic textbooks, and showed were the total organizational model of electronic textbooks for elementary mathematics by analyzing related cases. Finally, it was intend to present a developmental strategy for each level and an effective method to secure its content.

• The Journal of Society for e-Business Studies
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• v.9 no.1
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• pp.61-81
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• 2004

Learning Contents Management System(LCMS) manages the contents and the process of teaming by incorporating the both in a flexible system to help learners to be able to utilize them efficiently. The e-Learning comprises all types of educations based on electronic technologies as well as the Web. The existing e-Learning system is a simple information system providing Web based contents, therefore, it cannot support flexibility for both learners and teachers. The core part of the e-Learning system should be a remote education system which creates bilateral response between the learners and teachers, by providing substantial contents based on the LCMS. In this paper, a new e-Learning system is constructed with reusable modules generated by Component-Based software Development(CBD). Furthermore, creates new contents groups and enables to develop learning courses utilizing the Learning Objects. And also, observing the SCORM standard, lecture contents are designed and prepared to support learners Learners are supported to produce profiles which enables themselves to manage, measure, and evaluate their own capabilities, so that they can develop themselves properly in accordance with their levels, build prototypes for self development. Also a system that comprises all these individual components is suggested.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.449-456
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• 2019

This paper is a study on the construction of a wall-climbing mobile robot using vacuum suction and wheel-type movement, and a comparison of the performance of an automatic wall crack detection algorithm based on machine learning that is suitable for such an embedded environment. In the embedded system environment, we compared performance by applying recently developed learning methods such as YOLO for object learning, and compared performance with existing edge detection algorithms. Finally, in this study, we selected the optimal machine learning method suitable for the embedded environment and good for extracting the crack features, and compared performance with the existing methods and presented its superiority. In addition, intelligent problem - solving function that transmits the image and location information of the detected crack to the manager device is constructed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.290-293
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• 2021

In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.197-202
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• 2019

토큰화는 입력 텍스트를 더 작은 단위의 텍스트로 분절하는 과정으로 주로 기계 학습 과정의 효율화를 위해 수행되는 전처리 작업이다. 현재까지 자연어 처리 분야 과업에 적용하기 위해 다양한 토큰화 방법이 제안되어 왔으나, 주로 텍스트를 효율적으로 분절하는데 초점을 맞춘 연구만이 이루어져 왔을 뿐, 한국어 데이터를 대상으로 최신 기계 학습 기법을 적용하고자 할 때 적합한 토큰화 방법이 무엇일지 탐구 해보기 위한 연구는 거의 이루어지지 않았다. 본 논문에서는 한국어 데이터를 대상으로 최신 기계 학습 기법인 전이 학습 기반의 자연어 처리 방법론을 적용하는데 있어 가장 적합한 토큰화 방법이 무엇인지 알아보기 위한 탐구 연구를 진행했다. 실험을 위해서는 대표적인 전이 학습 모형이면서 가장 좋은 성능을 보이고 있는 모형인 BERT를 이용했으며, 최종 성능 비교를 위해 토큰화 방법에 따라 성능이 크게 좌우되는 과업 중 하나인 기계 독해 과업을 채택했다. 비교 실험을 위한 토큰화 방법으로는 통상적으로 사용되는 음절, 어절, 형태소 단위뿐만 아니라 최근 각광을 받고 있는 토큰화 방식인 Byte Pair Encoding (BPE)를 채택했으며, 이와 더불어 새로운 토큰화 방법인 형태소 분절 단위 위에 BPE를 적용하는 혼합 토큰화 방법을 제안 한 뒤 성능 비교를 실시했다. 실험 결과, 어휘집 축소 효과 및 언어 모델의 퍼플렉시티 관점에서는 음절 단위 토큰화가 우수한 성능을 보였으나, 토큰 자체의 의미 내포 능력이 중요한 기계 독해 과업의 경우 형태소 단위의 토큰화가 우수한 성능을 보임을 확인할 수 있었다. 또한, BPE 토큰화가 종합적으로 우수한 성능을 보이는 가운데, 본 연구에서 새로이 제안한 형태소 분절과 BPE를 동시에 이용하는 혼합 토큰화 방법이 가장 우수한 성능을 보임을 확인할 수 있었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.999-1004
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• 2014

This intelligent keyboard instrument self learning system developed in this study consists of smartphone based learning application and keyboard-instrument auxiliary module. The keyboard instrument auxiliary module receives playing information of the instrument through smartphone application and bluetooth communication. Then the module shows it through LED display so that the relationship between the keyboard and scale could be easily recognized even for beginners. Also, this system provides saving function and analyzing function of user's performance data, making learning more effective.

• Electronics and Telecommunications Trends
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• v.31 no.5
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• pp.1-10
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• 2016

빅데이터를 통한 학습, GPU를 활용한 고속 컴퓨팅 및 다양한 알고리즘 개발과 더불어 기계학습은 다양한 분야에서 종래에 이루어내지 못한 뛰어난 성과를 달성하고 있다. 그동안 상용화된 통신 시스템에서 기계학습이 활성화되지 못했지만, 전례없는 다양한 서비스와 단말을 아우르는 5G 통신에서는 더욱 적극적으로 활용될 것으로 예상된다. 기계학습은 링크 적응 등 무선접속기술, 다양한 망이 혼재된 이종망 기술, 트래픽 분류 등을 위한 네트워크 기술, 침입 탐지를 위한 보안 기술 등 다양한 통신기술에서 연구됐다. 또한, 최근에는 유럽의 Public Private Partnership(5G PPP) 프로젝트를 비롯하여 다양한 그룹에서 활발히 연구되고 있으며, 컬컴/노키아/에릭슨 등 통신 관련 기업들도 적극적인 투자를 하고 있다. 본고에서는 기계학습 관련 통신기술, 연구그룹 및 기업 동향을 소개하고, 이를 통해 5G 통신 적용 가능성을 짚어본다.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.1-14
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• 2019

Recent trends in deep reinforcement learning (DRL) have revealed the considerable improvements to DRL algorithms in terms of performance, learning stability, and computational efficiency. DRL also enables the scenarios that it covers (e.g., partial observability; cooperation, competition, coexistence, and communications among multiple agents; multi-task; decentralized intelligence) to be vastly expanded. These features have cultivated multi-agent reinforcement learning research. DRL is also expanding its applications from robotics to natural language processing and computer vision into a wide array of fields such as finance, healthcare, chemistry, and even art. In this report, we briefly summarize various DRL techniques and research directions.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1223-1226
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• 2021

본 논문은 비대면 한국어 회화 시험 연습용 안드로이드 애플리케이션을 제안한다. 한국어 학습에 대한 수요가 증가함에 따라 효과적인 한국어 회화 학습을 위해선 시·공간의 제약이 없는 학습 환경에서 사용자에게 구체적인 평가 지표를 제공할 필요성이 있다. 본 연구는 자연어 처리 기술을 활용하여 사용자의 한국어 회화 능력을 평가하는 알고리즘과 개인의 취약점을 보완할 수 있는 비대면 학습 플랫폼을 제시하였다는데 의의가 있다. 본 논문의 결과를 통해 회화 학습의 비용을 절감하고, 효율적인 언택트 학습 지원이 가능할 것으로 기대한다.