• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.161-166
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• 2010

EPC Information Services (EPCIS) is an EPCglobal standard designed to enable EPC-related data sharing within and across supply chain. The EPCIS standard defines standard interfaces to enable EPC-related data to be captured and subsequently to be queried using a set of service operations and an associated data model. There are two kinds of EPCIS data: event data and master data. Event data is created in the process of carrying out business processes. Traceability of goods across supply chain is based on event data. Therefore, each company must have an event data. This study compared he difference between MS-SQL(DRDBMS) and ALTIBASE(MMDBMS) for data storage. We compared the difference between two database management in many respects such as insert time and select time. We come to a conclusion that ALTIBASE is more efficient than MS-SQL.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.474-475
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• 2015

본 논문에서는 ATSC3.0 기반 UHD 급 고정/이동방송 융합형 3DTV 서비스 시나리오에 대한 내용을 기술한다. ATSC3.0 은 북미지상파 표준화 방송방식의 차세대 지상파서비스 표준으로 2016 년 까지 기술표준 논의완료를 목표로 물리계층, 전송프로토콜계층 및 4K-UHD 서비스를 포함한 응용서비스계층으로 분류하여 표준화 논의 중에 있다. 본 논문에서는 스테레오 스코픽 콘텐츠의 좌/우영상을 각각 고정 TV 채널 및 모바일 TV 채널로 나누어 전송하고 이를 융합형 3DTV 방식을 지원하는 수신기에서 동시에 좌/우영상 신호를 수신하여 3D 영상을 합성하는 방법으로 지상파 DTV 한 채널(6MHz 대역)내에서의 4K-UHD 고정 TV 서비스, 모바일 HD 서비스 및 UHD 3D 서비스를 동시에 제공할 수 있는 고정/이동방송 융합형 3DTV 서비스 방법을 제시한다. 따라서 본 논문에서 제안하는 융합형 3DTV 서비스는 추가적인 3D 부가정보를 전송하지 않고 4K-UHD, 모바일 HD 및 UHD 3D 서비스를 시청자에게 제공 가능하다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12B
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• pp.1060-1065
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• 2003

As the Internet continues to grow, network games are widely spreaded. For most network games, many users' meet on a server causes a heavy load to the sewer, which in turn brings inconvenience to the user. Moreover, it demands increased expense to the service provider for deploying additional servers. In this paper, we propose a hybrid distributed system for network games. In our proposed system, a client is independents of the server and exchanges information directly with other clients. The client depends on servers only for the update information. The proposed methodology classifies messages according to the characteristics of information that the message handles and applies either client-to-server and pear-to-pear communication for processing messages which increases the efficiency of systems.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.155-171
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• 2012

With the continuous and outstanding development of information technology(IT), human being is coming to the new computing era which is called cloud computing. This era brings lots of huge benefits also at the same time release the resources of IT infrastructure and data boom for man. In the future no longer, most of IT service providers, enterprises, organizations and systems will adopt this new computing model. There are three main deployment models in cloud computing including public cloud, private cloud and hybrid cloud; each one also has its own cons and pros. While implementing any kind of cloud services, customers have to choose one of three above deployment models. Thus, our paper aims to represent a practical framework to help the adopter select which one will be the best suitable deployment model for their requirements by evaluating each model comprehensively. The framework is built by applying the analytic hierarchy process(AHP), namely benefit-cost-opportunity-risk(BCOR) model as a powerful and effective tool to serve the problem. The gained results hope not only to provide useful information for the readers but also to contribute valuable knowledge to this new area. In addition, it might support the practitioners' effective decision making process in case they meet the same issue and have a positive influence on the increase of right decision for the organization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5654-5668
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• 2018

Driven by security and real-time demands of Internet of Things (IoT), the timing of fog computing and edge computing have gradually come into place. Gateways bear more nearby computing, storage, analysis and as an intelligent broker of the whole computing lifecycle in between local devices and the remote cloud. In fog computing, the edge broker requires X-aware capabilities that combines software programmability, stream processing, hardware optimization and various connectivity to deal with such as security, data abstraction, network latency, service classification and workload allocation strategy. The prosperous of Field Programmable Gate Array (FPGA) pushes the possibility of gateway capabilities further landed. In this paper, we propose a software-defined gateway (SDG) scheme for fog computing paradigm termed as Fog Computing Zero-Knowledge Gateway that strengthens data protection and resilience merits designed for industrial internet of things or highly privacy concerned hybrid cloud scenarios. It is a proxy for fog nodes and able to integrate with existing commodity gateways. The contribution is that it converts Privacy-Enhancing Technologies rules into provable statements without knowing original sensitive data and guarantees privacy rules applied to the sensitive data before being propagated while preventing potential leakage threats. Some logical functions can be offloaded to any programmable micro-controller embedded to achieve higher computing efficiency.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.55-65
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• 2021

The purpose of this paper is to present the Over-The-Top (OTT) platforms' strategies for two-sided markets. For this, we examine six strategic factors influencing OTT's success in Korea. The analysis reveals, among six OTTs, Netflix utilizes five strategic factors except the same-side network effects. OTTs from pay TV operators and channel providers tend to block the cross-side network effects on the opponent OTTs, because they think their giveaway to content providers is in vein, if the invested content by them would be consumed on opponent rival platforms. Interesting is that after experiencing a negative association between the market entry of Netflix and the subscription revenue growth rate of pay TV services, pay TV operators utilize the same-side network effects by offering hybrid services in partnership with global OTTs like Netflix, Disney+ which are considered as a complementary OTT. In conclusion, it is suggested to target a new connected TV based OTT service offering with collaboration with Korean TV device manufacturers for Korean OTTs' global strategy, because Netflix-like global market expansion is not easy for them to cover their content cost.

• Journal of Korean Public Health Nursing
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• v.36 no.1
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• pp.107-123
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• 2022

Purpose: This study was done to develop and test a scale to measure the homeboundness for low-income aged who live in the community. Methods: This was nursing methodology research. Homeboundness Scale development process was composed of construct identification based on concept analysis using the Hybrid model, 35 initial items. This number was reduced to 31 items through face validity tests by 7 experts. The preliminary Homeboundness Scale for low-income aged was administered to 240 aged who registered and received visiting health care service in the community health center located in S city. Data were analyzed using item analysis, factor analysis, Pearson correlation coefficients, and Cronbach's alpha. Results: Twenty-two items were selected for the final scale. Three factors evolved from the factor analysis, which explained 66.0% of the total variance. The internal consistency, Cronbach's alpha, was .945 and reliability of the subscales ranged from .890 to .934. Conclusion: Homeboundness Scale demonstrated acceptable validity and reliability. It can be used to assess the Homeboundness of the low-income aged in practice and research.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.