• 컴퓨터교육학회논문지
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• 제6권4호
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• pp.115-123
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• 2003

일반적인 코스웨어에서는 단순한 분기 수준에서 학습자료를 제시하는 것 이외의 개별화에 관한 노력을 찾아보기 힘들다. 이러한 문제의 원인은 다양한 측면에서 찾아볼 수 있지만, 코스웨어 자체가 융통적이지 못하고 재사용이 불가능한 하나의 고정된 구조로 구성되어 있으며 개발하는 데 있어 많은 비용과 시간이 소모된다는 것이다. 소프트웨어 개발 방법에서 객체지향개념이 등장한 것과 같은 맥락으로 코스와 컨텐트 개발에서는 학습객체라고 하는 개념이 대두되어 이를 통한 융통적인 코스 설계의 가능성을 보여주고 있다. 하지만 학습객체 기반의 코스 설계에서도 여전히 기존의 코스웨어와 비슷한 형태와 구조를 보이고 있으며, 학습객체를 활용한 개별화학습 구현에 대한 노력은 아직 미비하다. 본 연구에서는 기존 학습객체를 확장하여 개별화학습을 지원할 수 있는 개략적인 개별화학습지원-학습객체모델을 제안하며, 이를 기초로 개별화된 학습경로를 제시해 줄 수 있는 교수설계모형을 ADDIE 모델을 기초로 설계해 보았다.

• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.562-570
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• 2012

This paper proposes a robust object tracking algorithm using object features and on-line learning based particle filter for mobile robots. Mobile robots with a side-view camera have problems as camera jitter, illumination change, object shape variation and occlusion in variety environments. In order to overcome these problems, color histogram and HOG descriptor are fused for efficient representation of an object. Particle filter is used for robust object tracking with on-line learning method IPCA in non-linear environment. The validity of the proposed algorithm is revealed via experiments with DBs acquired in variety environment. The experiments show that the accuracy performance of particle filter using combined color and shape information associated with online learning (92.4 %) is more robust than that of particle filter using only color information (71.1 %) or particle filter using shape and color information without on-line learning (90.3 %).

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.119-128
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• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1679-1686
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• 2007

In this paper, we introduce the sharable and reusable learning objects which are suitable for XML Web services in e-learning systems. These objects are extracted from the principles of pedagogical paradigms for reusable learning units. We call them LIO (Learning Item Object) objects. Existing models, such as Web-hosted and ASP-oriented service model, are difficult to cooperate and integrate among the different kinds of e-learning systems. So we developed the LIO objects that are suitable for XML Web services. The reusable units that are extracted from pedagogical paradigms are tutorial item, resource, case example, simulation, problems, test, discovery and discussion. And these units correspond to the LIO objects in our learning object model. As a result, the proposed model is that learner and instruction designer should increase the power of understanding about learning contents that are based on pedagogical paradigms. By using XML Web services, this guarantees the integration and interoperation of the different kinds of e-learning systems in distributed environments and so educational organizations can expect the cost reduction in constructing e-learning systems.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.46-50
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• 2016

Recently, Depending on expectancy effect and ripple effect of augmented reality, the convergence between augmented reality and culture & arts are being actively conducted. This paper proposes a learning method for effective object tracking in 3D storytelling augmented reality in cultural properties. The proposed system is based on marker-less tracking, and there are four modules that are recognition, tracking, detecting and learning module. Recognition module is composed of SURF and LSH, and then this module generates standard object information. Tracking module tracks an object using object tracking based on reliability. This information is stored in Learning module along with learned time information. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. Also, it proposes a method for robustly implementing a 3D storytelling augmented reality in cultural properties in the future.

• Korean Journal of Radiology
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• 제24권7호
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• 대한수학교육학회지:학교수학
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• 제18권3호
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• pp.441-455
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• 2016

수학 학습이 구체물이나 친숙한 상황을 다양하게 제시해주는 것으로부터 시작되어야 한다는 입장은 CSA(Concrete-Semiconcrete-Abstract)라는 이름으로 잘 알려져 있다. 이에 비하여 최근 Kaminski 등의 연구는, 다양한 맥락을 가진 구체물로 수학적 개념을 학습하는 것보다 추상적인 개념을 먼저 학습하는 것이 지식의 전이 측면에서 효과적일 수 있음을 주장한다. 본고에서는 이러한 상반된 관점을 고려하여, 구체물, 반구체물, 추상적 개념의 지도순서를 다르게 적용한 수업을 분석하고 그 교육적 시사점을 확인하고자 하였다. 연구 결과 구체물로 시작하여 개념을 도입한 수업은 수학에 대한 긍정적인 태도를 가지게 한 것으로 보였으나 그 효과가 지속적이지는 않았으며, 성취도 면에서도 유의미한 차이를 보이지 않았고, 오히려 구체물이 가지는 과도한 구체성으로 인해 오류를 보이는 경우가 관찰되었다. 이러한 오류는 반구체물로 개념을 도입한 수업에서는 발견되지 않았는데, 이는 비본질적 요소가 사상된 반구체물이 추상적인 개념 학습에 효율적으로 사용될 수 있음을 시사한다.

• 컴퓨터교육학회논문지
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• 제6권2호
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• pp.61-70
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• 2003

현재 많은 기업 및 사이버대학에서는 표준화된 컨텐츠의 개발에 많은 시간과 자본을 투자하고 있다. 특히 SCORM(Sharable Content Object Reference Model)표준을 적용한 콘텐츠개발은 물론, 표준에 맞는 LCMS(Learning Content Management System)의 도입을 적극 추진하고 있다. 그러나, 성인학습 대상의 e-Learning 분야에서의 기대와 관심과는 다르게 초 중등교육 부문에서의 SCORM의 활용에 대한 견해는 대체로 부정적이며, 그 실효성에 의문이 제기되고 있기도 하다. 이에 SCORM의 핵심적인 개념인 학습객체 개념에 대해 조사하고 이러한 학습객체 개념이 초 중등 교육에 활용될 수 있는지에 대한 한계점과 가능성을 살펴봄으로써 SCORM에서의 학습객체가 갖는 교육학적 시사점을 모색해 보았다.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.