• IE interfaces
• /
• v.19 no.4
• /
• pp.306-315
• /
• 2006

In this paper we analyze whether we can use the mobile phone having been highly distributed into young generation as a device for the u-learning in Korean public education. For this purpose we deal with the technical maturity in three axes. Firstly, we examine the authoring nature of mobile internet-based contents such as both text and motion picture for the contents developers in the public education. As a research result the authoring of text has almost no difficulty, but that of the motion picture shows some problems. Secondly, we deal with whether u-learners can easily get and use u-contents on both mobile phone and PC respectively. After analysing this factor, we found that the downloading of motion picture contents into mobile phone is very limited. Therfore we talk about the usability and problem of various PC Sync tools and propose their standardization. Finally, the needs of the introduction of the ubiquitous SCORM which could enable to reuse u-contents among different Korean telco’s mobile phones are discussed. Here we describe some functionality of both ubiquitous SCORM and u-LMS. Our study looks like almost the first work examining the technological maturity for the introduction of u-learning with mobile phone in Korean public education and it could be used as a reference for the study of any other wireless telecommunication-based u-learning other than mobile telecommunication.

• Journal of Advanced Navigation Technology
• /
• v.16 no.1
• /
• pp.76-81
• /
• 2012

With the spread of the various mobile devices, the studies on the learning management system based on the u-learning are actively proceeding. The u-learning-based learning management system is very convenient in that there are no restrictions on the various access devices as well as the access time and place. However, the judgments on the authentication for the user and whether learning is focused on are difficult. In this paper, the voice and user face capture interface rather than the common user event oriented interface was applied to the learning management system. When a user is accessing the learning management system, user's registered password is input and login as voice, and the user's learning attitude is judged through the response utterance of simple words during the process of learning through contents. As a result of evaluating the proposed learning management system, the user's learning achievement and concentration were improved, thus enabling the manager to monitor the user's abnormal learning attitude.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.95-98
• /
• 1996

Universal Learning Network(U.L.N.), which can model and control the large scale complicated systems naturally, consists of nonlinearly operated nodes and multi-branches that may have arbitrary time delays including zero or minus ones. Therefore, U.L.N. can be applied to many kinds of systems which are difficult to be expressed by ordinary first order difference equations with one sampling time delay. It has been already reported that learning algorithm of parameter variables in U.L.N. by forward and backward propagation is useful for modeling, managing and controlling of the large scale complicated systems such as industrial plants, economic, social and life phenomena. But, in the previous learning algorithm of U.L.N., time delays between the nodes were fixed, in other words, criterion function of U.L.N. was improved by adjusting only parameter variables. In this paper, a new learning algorithm is proposed, where not only parameter variables but also time delays between the nodes can be adjusted. Because time delays are integral numbers, adjustment of time delays can be carried out by a kind of random search procedure which executes intensified and diversified search in a single framework.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.12
• /
• pp.1252-1256
• /
• 2007

U-learning (ubiquitous learning) systems, which deliver learning materials anytime and anywhere, allow learners to watch live lectures on PDAs, tablet PCs and notebook computers via broadband and wireless Internet. These systems have various problems; first, terminal devices are expensive, and it is difficult to maintain their efficiencies. Secondly, Internet does not guarantee quality of service (QoS), and in general it does not provide real-time services. Finally, the security of these systems is weaker in a local network than in an external network. The USB-based terminal system based on the OSGi service platform was designed as a ubiquitous system, in order to solve those problems. The USB terminals, used in this system, are inexpensive, and it is easy to maintain their performances. Also, this system solves the problems of security in a local network and provides guaranteed QoS. To accomplish this, the number of USB terminals connected to the system has to be limited according to the formula proposed in our paper. This system uses the OSGi specification as a middleware. It supports the discovery mechanism of the USB terminals, maintenance and administration of the system. Finally, this paper shows a driver's license testing system as an example u-learning application1.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.71-76
• /
• 2023

In this paper, we propose a fuzzy U-Net, a fuzzy deep learning model that applies fuzzy logic to improve performance in image segmentation using deep learning. Fuzzy modules using fuzzy logic were combined with U-Net, a deep learning model that showed excellent performance in image segmentation, and various types of fuzzy modules were simulated. The fuzzy module of the proposed deep learning model learns intrinsic and complex rules between feature maps of images and corresponding segmentation results. To this end, the superiority of the proposed method was demonstrated by applying it to dental CBCT data. As a result of the simulation, it can be seen that the performance of the ADD-RELU fuzzy module structure of the model using the addition skip connection in the proposed fuzzy U-Net is 0.7928 for the test dataset and the best.

• Convergence Security Journal
• /
• v.10 no.3
• /
• pp.43-49
• /
• 2010

With the development of information and communications technology(ICT), the concept of ubiquitous that we can communicate regardless of time and place appears. Due to the development of the technology delivering information, current society is called intellectualization society developed from informatization society. The intellectualization society is based on knowledge accumulated by processing information. The education methods are also developed into a concept of u-Learning applying the concept of ubiquitous from the concept of e-Learning using a computer. The military also points out education as a key policy. The aspect of war is changing to NCW(Network Centric Warfare) from platform centric warfare. Therefore, collecting and managing the war situations in real time is a key to controlling command. To this end, it needs to maximize individuals and groups' ability to cultivate the military with cutting-edge knowledge. Therefore, this study aims to look into methods to apply u-learning system in training and military actions according to changes in war environments and ICT.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.179-184
• /
• 2005

Computer Education form is rapidly growing and has been developed in a variety of forms such as off-line. on-line, multimedia, ICT education . These forms are changed to E-Learning, Blended-Learning and Ubiquitous-Learning. Ubiquitous-Learning model schools have already been running on a national scale according to the policy of the Ministry of Education. In this study, to understand the necessity for the Ubiquitous-Learning and Ubiquitous-Environment, the awareness of the Ubiquitous environment and U-learning in model schools and general schools is analyzed and compared. Through this study, a desirable model about Ubiquitous-Learning and Environment will be explored.

• International Commerce and Information Review
• /
• v.9 no.2
• /
• pp.103-120
• /
• 2007

The knowledge based economy requires more and more people to learn new knowledge and skills in a timely and effective manner. These needs and new technology such as computer and Internet are fueling a transition in e-learning. According to specialist's opinion, imagination experience studying is generalized, and learning environment that language barrier by studying, multi-language studying Machine that experience past things that disappear through simulation, and travel area, and experience future changed state disappears is forecasting to come. This is previewing finally that it may become future education that education and IT, element of entertainment is combined. Already, became story that argument for party satellite of e-Learning existence passes one season already. e-Learning is utilized already in all educations that we touch by effectiveness by corporation's competitive power improvement and implement of lifelong education in educational institutions through present e-Learning. It is obvious that when see from our viewpoint which is defining e-Learning by one industry and rear by application to education as well as one new growth power about these, e-Learning industry becomes very important means that can solve dilemma of growth real form. Only, special quality of digital industry that e-Learning is being same with other digital industry and repeat putting out a fire rapidly, and is repeating sudden change that these evolution is not gradual growth of accumulation and improvement of technology that is appearing consider need to. In the meantime, we need to observe about evolution of Information Technology. Because there is some scholars who e-Learning's concept foresees to evolve by u-Learning.(although, a person who see that these concept is not more in marketing terminology by some scholars' opinion is). This u-Learning's concept means e-Learning that take advantage of ubiquitous technology as Ubiquitous-Learning's curtailment speech. Ubiquitous, user means Information-Communication surrounding that can connect to network freely regardless of place without feeling network or computer. There is controversy about introduction time regarding these direction, but e-Learning is judged to evolve by u-Learning necessarily. Because keep in step and age that study all contents that learner wants under environment of 3A (any time, any whrer, any device) by individual order thoroughly is foreseen to come in ubiquitous learning environment that approach more festinately.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.737-738
• /
• 2006

This paper present a method of education configuration for U-learning. The proposed U-Learning configuration is more upgrade than earlier method. For the future, it is demanded that more advanced U-Learning systems and configuration toward to next generation education system, namely blended teaching and learning system.

• Korean Journal of Remote Sensing
• /
• v.39 no.3
• /
• pp.269-282
• /
• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.