• Journal of Digital Convergence
• /
• v.17 no.10
• /
• pp.259-264
• /
• 2019

This paper proposes an image prediction system architecture for deep running in enterprise environment. Easily transform into an artificial intelligence platform for an enterprise environment, and allow sufficient deep-running services to be developed and modified even in Java-centric architectures to improve the shortcomings of Java-centric enterprise development because artificial intelligence platforms are concentrated in the pipeline. In addition, based on the proposed environment, we propose a more accurate prediction system in the deep running architecture environment that has been previously learned through image forecasting experiments. Experiments show 95.23% accuracy in the image example provided for deep running to be performed, and the proposed model shows 96.54% accuracy compared to other similar models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.583-585
• /
• 2011

이북과 이러닝을 결합하는 블레디드 학습인 디지털교과서 서비스의 핵심은 교사의 현장 수업을 지원하는 것이다. 본 논문은 디지털교과서 수업설계 저작도구는 디지털교과서 서비스의 창의적 학습 설계를 지원하기 위한 저작도구의 기능설계 방안을 제시한다.

• Proceedings of the Korea Contents Association Conference
• /
• 2013.05a
• /
• pp.271-272
• /
• 2013

오늘날, 컴퓨터와 통신기술의 급속한 발전은 교육의 질을 증진하는데 새로운 기회를 제공하고 있다. 그러나, 현존하는 코스 중심의 학습환경이 개인적인 학습을 적절한 방법으로 지원하지 못하고 있다. 캠퍼스 밖에서도 선도적인 기술로 지원하고 도움을 제공하는 개인별 학습지원이 필요하다. 이러한 새로운 환경에 부응하기 위하여 클라우드에 기반한 CPLE모델을 제안하고 있다.

• The Journal of Society for e-Business Studies
• /
• v.26 no.4
• /
• pp.15-36
• /
• 2021

The airline industry faces with significant competition due to the rise of technology innovation and diversified customer needs. Therefore, continuous quality management is essential to gain competitive advantages. For this reason, there have been various studies to measure and manage service quality using customer reviews. However, previous studies have focused on measuring customer satisfaction only, neglecting systematic management between customer expectations and perception based on customer reviews. In response, this study suggests a framework to identify relevant criteria for service quality management, measure the importance, and assess the customer perception based on customer reviews. Machine learning techniques, topic models, and sentiment analysis are used for this study. This study can be used as an important strategic tool for evaluating service quality by identifying important factors for airline customer satisfaction while presenting a framework for identifying each airline's current service level.

• The Journal of the Korea Contents Association
• /
• v.8 no.10
• /
• pp.391-400
• /
• 2008

The web services of the CTLs(Center for Teaching and Learning) of 30 well known Korean and foreign universities have been evaluated in the present study. The CTLs of renowned foreign universities surpass Korean CTLs in terms of manpower. Nevertheless, the web services of the renowned Korean universities' are in no way inferior to those of the foreign CTLs surveyed in the present study. We found some web services to be desired and suggested a more system oriented web service function model.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.12
• /
• pp.291-306
• /
• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Journal of the Korea Contents Association
• /
• v.14 no.6
• /
• pp.477-490
• /
• 2014

The purpose of this study is to investigate factors influencing continuous usage intention of mobile learning and suggest practical strategies to enhance learners' continuous usage intention of mobile learning. In this study, we hypothesized that system quality, information quality, service quality and personal innovativeness have a positive effect on effort expectancy and performance expectancy, which ultimately have a positive effect on continuous usage intention. In order to examine structural relationship among variables, we surveyed 279 students who took courses at W Cyber University in 2013 fall semester. After collecting data, we examined causal relationship among variables using Structural Equation Modeling. The results of this study are as follows: First, system quality and personal innovativeness significantly affect effort expectancy. Second, information quality, service quality and personal innovativeness significantly affect performance expectancy. Last of all, effort expectancy and performance expectancy significantly affect continuous usage intention of mobile learning.

• Journal of Industrial Convergence
• /
• v.17 no.3
• /
• pp.23-29
• /
• 2019

Object recognition distinguish objects which are different from each other. But Face recognition distinguishes Identity of Faces with Similar Patterns. Feature extraction algorithm such as LBP, HOG, Gabor is being replaced with Deep Learning. As the technology that identify individual face with machine learning using Deep Learning Technology is developing, The Face Recognition Technology is being used in various field. In particular, the technology can provide individual and detailed service by being used in various offline environments requiring user identification, such as Smart Mirror. Face Recognition Technology can be developed as the technology that authenticate user easily by device like Smart Mirror and provide service authenticated user. In this paper, we present investigation about Face Recognition among various techniques for user authentication and analysis of Python source case of Face recognition and possibility of various service using Face Recognition Technology.

• 한국정보교육학회:학술대회논문집
• /
• 2010.01a
• /
• pp.253-258
• /
• 2010

지식정보화 시대에서 정보는 사회적 부를 창출하는 수단이며 그 자체로서 가치를 지니기도 한다. 하지만 사회적 약자인 장애인들은 정보접근과 활용에서 소외되고 있다. 이를 해결하기 위해 웹접근성을 향상시키는 것이 중요한 문제이다. 이에 본 논문에서는 웹접근성의 의미와 u-러닝의 의미, 그리고 u-러닝이 주는 학습적 효과를 알아보고 u-러닝 콘텐츠에 웹접근성을 향상시켰을 때 얻을 수 있는 이점에 대해 알아보고자 하며, 장애인의 웹접근성을 향상시키는 u-러닝 콘텐츠를 개발하는 데 목적이 있다. 본 연구에서는 다음과 같은 제안을 한다. 첫째, 시작장애인을 위한 보조기능이 존재해야 한다. 둘째, u-러닝 콘텐츠의 색채구성에 유의해야 한다. 셋째, 청각장애인을 위한 서비스를 제공해야 한다. 넷째, 장애인에게 적절한 모바일 기기를 선택해야 한다.

• Journal of Digital Contents Society
• /
• v.18 no.5
• /
• pp.869-879
• /
• 2017

This study identifies the effects of HTML5 service quality on the use intention of smart learning and present the policy implications through empirical studies. This study select assurance, reliability, tangibles, responsiveness, empathy as independent variables of HTML5 service quality and also select perceived usefulness, degree of perceived ease of use as parameters and select use intention of smart learning as dependent variables. The control variables such as learning devices, service, learning place, use age, use times are adapted. As a result of analysis by applying the structural equation model, it was estimated that the reliability of HTML5 service quality, tangibles affect negatively on perceived ease of use, but reliability, assurance, tangibles, empathy, responsiveness of HTML5 service affect positive impacts on perceived usefulness, and also certainty, empathy, responsiveness was identified as positive impacts on the perceived ease of use. It was proven that perceived ease of use effect positive on the perceived usefulness and also usefulness or ease to use have positive effects on the usage intention of users.