• 컴퓨터교육학회논문지
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• 제7권5호
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• pp.23-32
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• 2004

최근 웹 기반 학습의 우수성이 크게 대두되고 여러 방면의 연구들이 진행되고 있으나 학습자의 주도적인 참여를 요구하기 때문에 이를 극복하려는 연구가 필요하다. 본 연구에서는 기존의 웹 기반 학습 시스템이 가지고 있는 결함을 보완하기 위해서 학습 환경을 촉진시키기 위한 자기조절학습 전략을 개발하였다. 자기조절학습 전략은 일반적이고 추상적인 내용으로 구성되어 있어 이를 구체적으로 구현하기 위한 하위 전략들을 도출한다. 또한 좀 더 체계화된 시스템 개발을 위해 하위 전략 요소에 대한 상호작용적 설계를 하여 보다 진보된 웹 기반 학습 시스템을 구축하고 이를 검증하였다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• 한국산학기술학회논문지
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• 제19권8호
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• pp.322-328
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• 2018

본 연구의 목적은 구글 클래스룸(GC)을 기반으로 문제중심학습(PBL)의 평가를 조사하는 것입니다. GC는 쉽고 자유로운 온라인 학습으로 최근 교육과 훈련에 중요한 역할을 하고 있다. 학생들은 아이디어와 자원을 공유하고, 도메인 전체에 걸쳐 지식을 적극적으로 전달하고 PBL의 주어진 문제에 대한 해결책을 연구하면서 점점 더 자기 주도적인 학습에 독립적이 된다. 학생들은 과제 페이지에서 제출일을 확인하고 클릭 한 번으로 과제를 시작할 수 있고 선생님은 과제 완료 여부를 빠르게 확인할 수 있으며 GC에서 점수를 바로 매길 수 있다. 본 연구에서는 GC를 기반으로 한 PBL의 평가를 설계하였다. 학생들은 주어진 자료를 읽고 과목의 목적을 파악하여 학습 문제를 조사한다. 그 후에 그들은 주제를 토론하고 보고서를 작성하여 연구 논문, 책, 인터넷 자료를 가지고 그것들을 공부한다. 연구 결과는 GC에 기반을 둔 PBL이 함께 학습하는 데 효과적이라는 것을 보여 주었다. 학생들은 PBL학습 환경에서 긍정적인 태도를 보였다. 이 연구는 GC에서 PBL평가의 개발이 가능하다는 것을 제안한다.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• 한국정보통신학회논문지
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• 제24권5호
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• pp.584-590
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• 2020

일상생활에서 디지털 스크린을 오랜 시간 사용하면 눈의 피로, 안구 건조, 두통 등 컴퓨터 시각 증후군을 경험하게 된다. 컴퓨터 시각 증후군을 예방하기 위해서는 스크린 사용 시간을 제한하고 수시로 휴식을 취하는 것이 중요하다. 최근 스마트폰에서는 스크린 사용 시간을 알 수 있도록 도와주는 다양한 애플리케이션이 존재한다. 하지만, 사용자는 스마트폰 스크린뿐만 아니라 데스크탑, 노트북, 태블릿 등 다양한 스크린을 보기 때문에 이러한 앱만으로는 한계가 있다. 본 논문에서는 color, IMU, lidar 센서 데이터를 이용하여, 사용 중인 스크린 디바이스를 감지하는 머신 러닝 기반 모델을 제안하고 여러 가지 모델의 성능을 비교한다. 성능 비교 결과 신경망 기반 모델이 전통적인 머신 러닝 모델보다 높은 F1 스코어를 보였다. 신경망 기반 모델에서는 MLP, CNN 기반 모델이 LSTM 기반 모델보다 높은 스코어를 보였으며, 전통적인 머신 러닝 모델에서는 RF 모델이 가장 우수했으며, 다음으로는 SVM 모델이었다.

• 반도체디스플레이기술학회지
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• 제18권2호
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

• Smart Structures and Systems
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• 제31권4호
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1147-1148
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• 2008

In this paper, we propose action-learning method based on teaching. By adopting this method, we can handle an exception case which cannot be handled in an Ethology-based Action SElection mechanism. Our proposed method is verified by employing AIBO robot as well as EASE platform.