• The Journal of Information Systems
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• v.21 no.1
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• pp.117-152
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• 2012

The purpose of this study was to identify the negative factors affecting personnel u-Learning acceptance and to analyze the interrelation among the factors in this research model. The two independent variables avoidable convenience and reliant convenience, based on pilot test results, and learning performance and perceived interactivity, based on the relevant literature, are used to examine the research model. The research problem was tested with data collected from 577 respondents in 23 universities. This study developed and empirically analyzed a model representing the relationship by using the Structural Equation Model. The major findings of this study are, firstly, that the higher reliant convenience is negatively affecting the degree of system use and learner’s satisfaction, whereas avoidable convenience is only affecting the learner’s satisfaction. Secondly, the higher learning performance and stronger perceived interactivity affects the degree of system use as well as learner’s satisfaction. Finally, the degree of system use affects the learner’s satisfaction.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.661-667
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• 2010

A lot of existence e-learning are connected SCORM and LMS. And u-learning was researching as one of the new trend. But there are few research paper to connect the existing SCORM and LMS. In this paper, we proposed u-learning framework with connect the SCORM and LMS. And we used the mobile equipment transform module and learning object reconstruction module to apply each different characteristics of mobile equipment. Especially, information of the mobile equipment was stored and managed using the meta-data of the equipment.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.271-279
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• 2011

With advanced technologies of information and communication technologies, ubiquitous computing becomes popular. U-learning(Ubiquitous Learning) is a new paradigm that was started with ubiquitous computing environment. U-learning has characteristics such as anytime, anywhere, any network and any device, The U-learning support system(ULSS) is the system for supporting the u-Learning. Also, with recent fashion of smart phones, their use in education becomes interested. In this paper, The ULSS is defined in smart phone environments.

• Journal of The Korean Association of Information Education
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• v.9 no.3
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• pp.511-522
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• 2005

The rapid development of information & communication technology has changed the paradigm of education. Recently the area of education is introducing u-Learning, in which learning is possible at any time and in any place through personal information devices such as PDA, tablet PC and mobile phone terminals. Taking advantage of the mobility and individuality of personal information devices, u-Learning can provide learning customized to the characteristics of individual learners without the limitations of time and space and can be effective in situational learning and experiential learning. In order to identify the uses of PDA in teaching.learning and to develop a basic teachinglearning model using PDA, the present study applied PDA directly to classes and examined the effects. According to the result, most students were satisfied with classes utilizing PDA but problems were also found in connection, insufficient contents for PDA, the quality of screen, etc.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.123-126
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• 1995

Characteristics of control system design using Universal Learning Network (U.L.N.) are that a system to be controlled and a controller are both constructed by U.L.N. and that the controller is best tuned through learning. U.L.N has the same generalization ability as N.N.. So the controller constructed by U.L.N. is able to control the system in a favorable way under the condition different from the condition of the control system in learning stage. But stability can not be realized sufficiently. In this paper, we propose a robust control method using U.L.N. and second order derivatives of U.L.N.. The proposed method can realize better performance and robustness than the commonly used Neural Network. Robust control considered here is defined as follows. Even though initial values of node outputs change from those in learning, the control system is able to reduce its influence to other node outputs and can control the system in a preferable way as in the case of no variation. In order to realize such robust control, a new term concerning the variation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivatives of criterion function with respect to the parameters. Finally it is shown that the controller constricted by the proposed method works in an effective way through a simulation study of a nonlinear crane system.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.65-78
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• 2006

Recently, there has been much research to develope and apply RFID technology, which has a kcy role in the upcoming ubiquitous society, in many fields such as physical distribution, traffic control, medical service, and so on. However, there has been little research on a ubiquitous education or learning including 'u-Campuses' and 'u-Libraries'. Based on the characteristics of RFlD, this paper proposes a system for English learning required in globalization age. RFID tags and sensors utilize wireless communications to track the location and status information of the user to deliver English situated learning services. The current RFID-based system should use quite a different rniddleware, compared with a general-purpose middleware on server or desktop. The RFID system is used on a mobile PDA and consists of essential APIs such as reader and tag control, queue, and filter management.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.854-860
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• 2009

Ubiquitous is a new computing environment with IT technology and information communication, and appling various equipments likes PDA and application parts. Recently, user's using environment is changing to smart phone and is expanded learning tools to learner without educational environment. Thus, in this paper, we designed SCORM based meta-data to use smart phone. For this purpose, we made U-learning server and smart phone process server that is to handling with existence LMS and SCORM. To apply smart phones characteristics that have different ones each other, meta-data was able to have some resource information as like CPU, screen size and memory. The meta-data adapter could be process the characteristics.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.281-289
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• 2011

This paper provides the emerging flow of new communication technology using ubiquitous learning (u-Learning). In the intelligent Ubiquitous environment, humans and devices with computing abilities become interoperable. u-Learning will lead students to open their minds to the world and motivate self-learning, which may lead them to learn and communicate more efficiently, and save time, cost and energy. Through case research, regarding education, learning attitude, custom and, personal relations, one must solve the fundamental issues of misuse and outflow problems regarding personal information that will be widely collected in detail than the present condition, and in order for this not to happen, further support of the law and system, plus ethical perspectives must be considered in order to progress.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.