• Journal of Astronomy and Space Sciences
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• v.41 no.2
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• pp.61-78
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• 2024

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.73-91
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• 2011

This research aims to compare the features and environments for media acceptance of tablet against laptop computer in the u-Learning environment. While it is need to accept suitable media for u-Learning learner, most of existing research has focused on finding factors which can affect perceived usefulness and perceived ease of use on extended technology acceptance model and how to fulfill those factors. Thus this research drew four categories need to use media in the u-Learning environment, then adopts Kano's model and IPA methodology. The results by Kano's model identify exciting and basic attributes which do not match overall satisfaction of learners. Moreover the research analyses by IPA methodology illustrate whether the factors considered important by learners are fulfilled. The research also emphasis the significance of enhancing relative importance as well as satisfaction of the properties for media acceptance in the u-Learning environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.249-256
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• 2016

Recently, a number of techniques to ensure the free walking for the visually impaired and transportation vulnerable have been studied. As a device for free walking, there are such as a smart cane and smart glasses to use the computer vision, ultrasonic sensor, acceleration sensor technology. In a typical technique, such as techniques for finds object and detect obstacles and walking area and recognizes the symbol information for notice environment information. In this paper, we studied recognization algorithm of the selected symbols that are required to visually impaired, with the deep learning algorithm. As a results, Use CNN(Convolutional Nueral Network) technique used in the field of deep-learning image processing, and analyzed by comparing through experimentation with various deep learning architectures.

• Tunnel and Underground Space
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• v.29 no.1
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• pp.1-11
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• 2019

This study investigated use cases of machine learning technology in domestic medical, manufacturing, finance, automobile, urban sectors and those in overseas mining industry. Through a literature survey, it was found that the machine learning technology has been widely utilized for developing medical image information system, real-time monitoring and fault diagnosis system, security level of information system, autonomous vehicle and integrated city management system. Until now, the use cases have not found in the domestic mining industry, however, several overseas projects have found that introduce the machine learning technology to the mining industry for improving the productivity and safety of mineral exploration or mine development. In the future, the introduction of the machine learning technology to the mining industry is expected to spread gradually.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.135-143
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• 2011

The smartphone allows learners to be involved in learning environments in which students actively study from anywhere and at anytime. Because learners can keep engaged in the environment where they can access to the internet, they can efficiently study in transit using various features and functions of smartphone. Smart learning is a unique learning based on mobility and functions of mobile digital devices including searching and sharing information and using various applications. For the effective use of smartphones in e-learning systems, the contents and learning management systems should be designed to meet effective teaching and learning principles, such as interactivity and collaborations. In smart learning, learning contents for effective learning need to be integrated with typical functions of smartphones and to develop small pieces of learning contents according to learning topics. In the case of learning management systems, it should reflect understanding of learners' environment using a PA agent program and provide personalized learning services.

• Journal of The Korean Association of Information Education
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• v.13 no.4
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• pp.497-508
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• 2009

As the educational use of intelligent service robots has been proved to be effective, educational service robots have been utilized in kindergarten. In addition, service robots will be used in elementary schools from 2010 for the after-school English program. This trend indicates that r-Learning using service robots will become a major educational paradigm in preparing for future education. This article consists of the following four parts. First, the concept and the type of educational robots were defined and the trend of previous research was examined. Second, the characteristics of robot-assisted learning were analyzed as a part of r-Learning, and difference between r-Learning and u-Learning was compared. Third, the contents and service using a robot-assisted learning system were discussed, the models and trend of service using the robot-assisted learning system were examined, and the aspects of viewing evolution were compared. Finally, suggestions for activating the service market of robot-assisted learning were made for the educational institution, research institution, government and robot companies.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2299-2308
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• 1997

While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.2
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• pp.268-275
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• 2012

Purpose: The purpose of this study was to implement and evaluate the learning contracts based self-directed learning in a final clinical placement for senior nursing students. Methods: This study was a case study and 82 senior nursing students at a university participated in a learning contract based practice placement. Data were collected from written learning contracts and questionnaires after a clinical practice. Results: The students' learning needs were knowledge, clinical skills, and attitudes frequently encountered in a ward in which clinical skills were most common. The students' formulated learning contracts were varied but most of them were basic and simple. A self-directed clinical course was beneficial and a satisfactory experience to senior students. There was an increase in the students' motivation in learning, confidence in own capability, and satisfaction with the use of the learning contract. Conclusion: Self-directed clinical practicum would result in a degree of attitude change in the students. This study suggests that learning contract based self-directed clinical practice is effective to improve learning satisfaction, confidence in own capability, and competency.

• International Journal of Contents
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• v.5 no.1
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• pp.15-20
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• 2009

In this paper, we propose u-Learning management systems which are designed and implemented based on learning activities oriented components. The proposed systems are composed of components which can process the functionalities for coming into actions of learning activities. Specially, each component is broken into class units by which learning activities of users can be performed on various devices. When users by to connect the proposed learning management system, the system explores devices of users and the corresponding connection program, and then selects components that are fitted to the activities and combines them in a real-time. Our system provides u-Learning environment so that users can use the learning activity services taking no influence on time, place, various devices and programs. That is different from traditional e-Learning system which cannot support various devices of users directly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.1-5
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• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.