• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.4
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• pp.85-104
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• 2004

This study is supposed to the way to add and enlarge the elements related to educational domain in metadata of school library information system (DLS) by using the concept of learning object which the education information service agencies have adapted. This study is to propose the methods which can be accessed according to the units of learning content in order that they can be applied to the teaching and learning situations, and describe and index the total traits of interior data elements included in the information resources. Thus, the metadata of the existing DLS through the additional elements : , , and was made to access the information resources according to the teaching and learning situations and to accept the concept of interior learning units by means of the element.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• The Journal of Information Systems
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• v.17 no.4
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• pp.233-260
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• 2008

Universities have recognized e-Learning Systems as the critical IT resources which contribute to improving the competitiveness of the universities as well as the quality of the traditional class-based lectures. Instructors deliver the main contents in the class. Other supplementary activities like online discussions, sharing of teaching-learning materials, submission of homeworks, communication among the learners and between the instructors and the learners, and so on can be efficiently facilitated using e-Learning Systems. In other words, e-Learning Systems enable a blended learning combined class-based lectures and e-learning in a variety of ways. Nonetheless, compared to the level of implementation of e-Learning Systems, the usage of both the instructors and the learners is not high. Accordingly, this study examines the determinants to affect on the usage of e-Learning Systems from the learners perspective. To draw the key determinants, we review the IS literatures related to adoption or use of the IS like Media Richness Theory (MRT), Technology Acceptance Model (TAM), Social Influence Model (SIM), and Self-efficacy Model. The variables are drawn out to be expected on the usage of e-Learning like Media Richness, Ease of Use from MRT, TAM and Instructor's Influence, Co-learner's Influence from SIM, and Self-efficacy. To test our model and hypotheses, we have collected data in the class-based lectures using e-Learning System complementary. The results of the test with 192 data are as follows: Firstly, it shows that the Instructor's Influence and the Media Richness are the influential determinants to affect on the Perception of Usefulness of e-Learning Systems. Additionally, the Co-learner's Influence and Ease of Use in order is significant to the Perception of Usefulness. Secondly, as to the degree of use of the e-Learning Systems, the Co-leaner's Influence, the Media Richness, and the Ease of Use are, in that order, the significant determinants. The Perception of Usefulness, also, founded a key factor on increasing the use of e-Learning Systems. On the other hand, the Instructor's Influence is not significant to the use of e-Learning Systems. Finally, it has been found that Self-efficacy is significant to the Perception of Media Richness, Ease of Use, but not significant to the Perception of Usefulness.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.192-200
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• 2022

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.81-89
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• 2012

This paper presents a set of controlled simulated statical and engineering mechanical experiments accessible via the virtual world environment (VWE) and virtual physics lab S/W. Online courses of the university offering courses and/or programs online are growing and the number of students want education in ways which fit their personal places, e-learning is becoming more important and ubiquitous each year. In this study, first of all, question is rather 'How do we execute the learning effectiveness of e-learning courses?' than 'Why does they need e-learnig or VW-learning?'. In particular, is it possible to effectively teach mechanical engineering courses online? The answer was 'No'. So, there is little research on many of these questions. And another important question is 'Is e-learning cost effective?'. For the answer, This research provided that an instructional design model is used to 'How to think and apply the Newtonian forces' in the virtual physics lab S/W. Collected data from student are administered in the spring semester when students studied 'Introduction to Bio-resources and Systems Engineering'. Results show that a cadre of students can take highly interactively physical properties of mechanical engineering in the virtual laboratory environment. Those show that VWE is greater than that of a similar real world presentation or experimental lab, since most of students are delighted to modify and retry modeling works in the VWE.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.509-515
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• 2019

The purpose of this study is to analyze the learning needs for lifelong education of the baby boom generation, the role of higher education and to reorganization plan the lifelong learning system at higher education levels to realize the lifelong learning system. In order to do this, this study analyzes the present condition of lifelong learning for each age group in South Korea, especially the participation and learning needs of the baby boom generation. Based on this, present lifelong learning reorganization plans in universities examine for the realization of a lifelong learning system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.960-963
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• 2013

By the rapid development ICTs, education is entering into a smart learning. Learners are put down the paper book, are learning by using a various smart devices, and at this moment emerged that learning system each of which contains numerous platforms. Platform-specific systems require a lot of time and money to applicable to heterogeneous platforms, and This phenomenon is exacerbated if system has a lot of learning contents. Learning system that supports cross-platform is needed for reduce the unnecessary waste of resources and provide effective learning content. In order to achieve this, the research must be preceded by a database model that numerous of existing learning content can be integrated without any unnecessary redundant. In this paper, suggests Ontology-based metadata model that can be integrate existing learning content as an alternative to database, and through this, Design and Implementation of smart learning system that support Heterogeneous platforms.