• 의학교육논단
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• 제23권1호
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• pp.11-22
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• 2021

This review of the literature explored the experiences and effects of peer-assisted learning in basic medical education. Peer-assisted learning is most commonly utilized to teach clinical skills (including technical skills) and medical knowledge (76.4%). It has also been used, albeit less frequently, to facilitate small-group discussions including problem-based learning, to promote students' personal and professional development, to provide mentoring for career development and adaptation to school, to give tutoring to at-risk students, and to implement work-based learning in clinical settings. Near-peer learning is a common type. The use of active learning techniques and digital technology has been increasingly reported. Students' leadership had frequently been described. Student tutor training, programs for teaching skills, institutional support, and assessments have been conducted for effective peer-assisted learning. There is considerable positive evidence that peer-assisted learning is effective in teaching simple clinical skills and medical knowledge for tutees. However, its effects on complex skills and knowledge, small-group discussions, personal and professional development, peer mentoring, and work-based learning have rarely been studied. Additionally, little evidence exists regarding whether peer-assisted learning is effective for student tutors. Further research is needed to develop peer-assisted learning programs and to investigate their learning effects on student tutors, small-group discussion facilitation, personal and professional development, peer mentoring, and peer-led work-based learning in the clinical setting in South Korea. Formal programs and system advancement for a student-led learning culture is needed for effective peer-assisted learning.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• 한국게임학회 논문지
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• 제18권5호
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• pp.93-102
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• 2018

본 논문은 이론과 실기 교육, 학계와 산업계의 융합 교육을 지향하는 게임 교과목 교육의 효과적이고 체계적인 학습 방법론을 제안하는 것을 목적으로 한다. 특히 교수자 중심 교육에서 학습자 중심 학습 환경으로 변화하고 있는 시대적 흐름을 반영하여 4PBL 모델을 제시하고자 한다. 4PBL 모델은 학습 접근 방식을 기반으로 하는 3P 모델과 문제기반학습(Problem based Learning) 방법론을 보강, 발전시킨 모델로 개인지(Personal based Learning), 문제기반학습(Problem based Learning), 프로젝트기반학습(Project based Learning), 실행기반학습(Performance based Learning)으로 구성된다. 본고에서는 구체적인 게임 교과목 교육 사례를 들어 각 단계별 PBL의 개념과 특징을 설명하였다. 이와 같은 시도는 변화하는 교육 패러다임 속에서 게임 기획과 개발에 대한 지식을 주체적으로 구조화할 수 있는 학습 환경을 제시할 수 있다는 점에서 유의미한 가치가 있다고 할 수 있다.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• 한국정보통신학회논문지
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• 제25권3호
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• 인터넷정보학회논문지
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• 제9권6호
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• pp.127-139
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• 2008

UMPC(Ultra Mobile Personal Computer)와 같은 최첨단 모바일 PC는 이동용이성과 실시간 의사소통 가능성 등의 특징과 동료학생과의 대화, 학습 자료의 자유로운 송부 및 공유 등과 같은 학습활동이 요구되는 학습 환경에서 그 가치를 크게 인정받고 있다. 본 연구에서는 초등학교 5학년 과학시간에 한국학술정보원(KERIS)에서 제시한 u-러닝통합탐구모형을 중심으로 UMPC를 활용하는 수업을 전개하고, 학습 활동전개과정에서 의미 있는 요소를 찾아내어 UMPC가 u-러닝에서 의미 있는 역할을 하고 있는지 알아보고자 하였다. 본 연구결과에서 UMPC의 역할은 수업전개에서 학습활동과 관계가 될 수 있는 요소로 활용되지만 학습활동 중에 교사와 지속적인 피드백이 있어야만 UMPC가 학습활동의 역할을 담당할 수 있었다.

• 한국실천공학교육학회논문지
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• 제3권1호
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• pp.69-75
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• 2011

본 논문에서는 유비쿼터스 컴퓨팅 환경에 기반을 둔 e-러닝 모델을 제안하였다. 이를 위해 국내외 대학의 진보된 e-러닝 시스템을 조사 및 분석하였으며, 이를 근간으로 유비쿼터스 환경에 기반을 둔 최적의 e-러닝 모델을 제안하였다. 제안한 모델은 최적의 e-러닝 하드웨어 및 소프트웨어, 그리고 다양한 e-러닝 서비스를 포함하고 있다. 여기에는 출결체크 서비스, 수업운영 서비스, 공용지식 서비스, 성적처리 서비스, 편의시설 서비스, 개인운영 서비스, 신용조회 서비스, 캠퍼스안내 서비스, 강의실운영 서비스 등이 있다. 또한, 실험.실습에 관련된 서비스도 포함하고 있다.