• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.31-36
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• 2020

This research study designed a location image collecting technology. It provides the exact location information of an image which is not given in the photo to the user. Deep learning technology analysis and collects the images. The purpose of this service system is to provide the exact place name, location and the various information of the place such as nearby recommended attractions when the user upload the image photo to the service system. Suggested system has a deep learning model that has a size of 25.3MB, and the model repeats the learning process 50 times with a total of 15,266 data, performing 93.75% of the final accuracy. This system can also be linked with various services potentially for further development.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.43-50
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• 2021

Recently, researches and projects of companies based on artificial intelligence have been actively carried out. Various services and systems are being grafted with artificial intelligence technology. They become more intelligent. Accordingly, interest in deep learning, one of the techniques of artificial intelligence, and people who want to learn it have increased. In order to learn deep learning, deep learning theory with a lot of knowledge such as computer programming and mathematics is required. That is a high barrier to entry to beginners. Therefore, in this study, we designed and implemented a web-based deep learning platform called DeepBlock, which enables beginners to implement basic models of deep learning such as DNN and CNN without considering programming and mathematics. The proposed DeepBlock can be used for the education of students or beginners interested in deep learning.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.715-718
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• 2007

We made a research on web service of famous Korean and foreign universities' CTLs(Center for Teaching and Learning). Foreign CTLs of famous universities surpass Korean CTLs in manpower. However, the web services of Korean famous universities' CTLs are never inferior to those of foreign famous universities' CTLs. Web services of Korean famous universities' CTLs, which provide application forms and participants' boards are more system oriented than those of foreign CTLs of famous universities.

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

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1349-1355
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• 2018

Recently, a variety of contents services over the internet are becoming popular, among which MCN(Multi Channel Network) platform services have become popular with the generalization of smart phones. The MCN platform is based on streaming, and various factors are added to improve the service. Among them, augmented reality sticker service using face recognition is widely used. In this paper, we implemented the MCN platform that masks the augmented reality sticker on the face through facial emotion recognition in order to further increase the interest factor. We analyzed seven facial emotions using deep learning technology for facial emotion recognition, and applied the emotional sticker to the face based on it. To implement the proposed MCN platform, emotional stickers were applied to the clients and various servers that can stream the servers were designed.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.267-279
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• 2011

A variety of Web Services are being existences based on the development of the Internet. Especially, e-Learning services in the Universities make the temporal and spatial constraints overcome, and e-Learning services have gained great popularity to the students who use because those provide various convenience and usefulness. e-Learning studies have been actively performed based on the spread of e-Learning in the various industries. A number of studies suggest the diffusion plan of e-Learning applying the Technology Acceptance Model studies. Those studies focused on the ease of use and usefulness of e-Learning. The explanation about educational contents perspectives, which is the key factor in e-Learning, is very weak. Therefore, this study suggested the strategy for spreading the e-Learning adoption through in terms of e-Learning educational contents and trust perspectives. This research results would provide the strategic implications to boost the e-Learning adoption in the various universities in terms of e-Learning educational contents and trust perspectives.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.739-748
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• 2016

This study quantitatively measured the impact of blended learning with smart devices for learning satisfaction. It is targeted in specialized domestic company with IT Service which build smart learning systems and utilize for employee training. Specifically, it empirically analyzed that learning attitude(Self-efficacy, Self-innovativeness, Perceived usefulness, Perceived ease of use) with smart devices affect acceptance of smart learning and offline face-to-face learning satisfaction. As a result, the learning attitude of the smart learning gave a positive effect on the acceptance of the smart learning and then acceptance of the smart learning gave a positive effect on offline face-to-face learning satisfaction. Additionally learning the attitude of the smart learning even gave a positive impact, as well as the acceptance of smart learning experience in offline training. It imply that this variables of smart-learning attitude affect the self-directed learning and positive learning experience.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.357-372
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• 2012

Existing e-learning systems not only require complex admission processes but also do not give stepwise education methods according to individual learners' characteristic. These circumstances cause learners to lose educational interest so that their educational efficiency decreases. In particular, the present e-learning systems do not provide educational approaches suitable for infant and elementary children. Under this system, the e-learning education for children does not proceed completely without guardians. To solve this problem, we design and implement an English e-learning system for elementary children based on friendly and comfortable user interfaces. For children, the proposed system reflects their age and individual interesting per each e-learning stage. This system supports both the Web application platform and smart phone application platform for various client requirements. The proposed system manages 3 classes as English learning content. Learners can experience their own English e-learning course in each class, which is compiled by current educational ability. In addition to the general functions in e-learning system, the proposed system develops content buffering algorithm to reduce data traffic in server.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.