• Information and Communications Magazine
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• v.25 no.9
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• pp.3-8
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• 2008

본고에서는 1998년 말부터 시작된 3GPP의 표준인 WCDMA, HSPA 및 LTE로의 진화 로드맵을 알아보고, 특히 최근의 중점 이슈인 LTE에 대한 추진 배경, 일반적 요구사항 및 기술적 요구사항의 개요에 대해 알아본다. LTE는 2004년 말에 필요성이 제기되어 2005년부터 본격 추진되었고 2008년 말이면 무선접속 분야는 표준이 거의 완료될 전망이다. 과거 WCDMA 에서 HSPA로 진화 시 호환성을 고려해서 조심스럽게 접근했던 것과는 달리 LTE에서는 호환성의 제약을 받지 않는 완전히 새로운 표준을 지향하여, 단순하면서도 효율적이고 유연한 기술표준을 목표로 작업이 수행되어 왔다. 무선통신 분야의 주류 기술이 되고 있는 OFDM, MIMO기술을 바탕으로 LTE기술은 Vodafone, Verizon, NTT DoCoMo등 많은 대형사업자로부터 차세대 기술로 선택되고 있으며 2010년경부터 상용화가 예상되고 있다.

• Agribusiness and Information Management
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• v.1 no.2
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• pp.97-118
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• 2009

지식경제부는 2009년부터 RFID(Radio-Frequency IDentification)와 USN(Ubiquitous Sensor Network) 기술의 중요성을 파악하고 이를 집중적으로 육성하여 2017년까지 세계 RFID/USN 산업에서 3강에 진입하겠다는 로드맵을 발표하였다. RFID와 USN은 전 세계적으로 매년 큰 폭으로 성장하고 있는 산업이며, 정부에서도 의욕적으로 지원을 발표하였다. 또한 RFID/USN 산업에 종사하고 있는 기업의 80%가 중소기업이기 때문에 이 분야에 대한 정부의 지원은 중소기업의 활성화라는 측면에서도 매우 중요하다. 본 논문에서는 RFID/USN 최신 기술에 대해 간략하게 정리하였으며, 농림수산업 분야에서 RFID/USN 기술을 적용한 연구 사례를 충실히 소개하고 있다. 또한 해외 사례를 토대로 RFID 및 USN 산업이 나아가야할 시사점을 제공함으로써, 정부가 유비쿼터스 분야의 커다란 로드맵을 구성하는데 있어 중요한 지표가 될 수 있을 것이다.

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

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.17-27
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• 2024

Video frame interpolation is an important technique used in the field of video and media, as it increases the continuity of motion and enables smooth playback of videos. In the study of video frame interpolation using deep learning, Kernel Based Method captures local changes well, but has limitations in handling global changes. In this paper, we propose a new U-Net structure that applies feature map differentiation and two directions to focus on capturing major changes to generate intermediate frames more accurately while reducing the number of parameters. Experimental results show that the proposed structure outperforms the existing model by up to 0.3 in PSNR with about 61% fewer parameters on common datasets such as Vimeo, Middle-burry, and a new YouTube dataset. Code is available at https://github.com/Go-MinSeong/SF-AdaCoF.

• Journal of Information Technology Services
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• v.8 no.2
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• pp.89-115
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• 2009

A Technology Roadmap (TRM) is extensively used as a tool to manage activities of researching and developing technologies and products that can help a business achieve its goals. However, most of past studies on TRMs have limited themselves to developing in a single product or a single industry level. Few research studies have examined how TRM are used for convergence products and services for future development. The aim of this chapter is to introduce a TRM development methodology for u-City technologies and to consider its possible application at the R&D program level in Korea. This research suggests ways to develop a TRM in u-City development by segmenting u-City technologies, projecting TRM for each technology and designing TRM templates for evaluating both current and future technologies. In addition, the study further highlights to prioritize which technology is favorable for the implementation based on its economic feasibility and technological maturity.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.165-168
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• 2009

현재 IT서비스 업체에서 새로운 성장동력으로 떠오르고 있는 분야가 융복합사업(IT+IT, IT+장비, IT+장비+시공)이라고 불리우는 컨버전스 프로젝트이다. 기존의 IT분야의 프로젝트와는 전혀 다른 특성을 갖고 있는 컨버전스 프로젝트에 대한 IT서비스 업체의 준비는 아직 미흡한 것이 현실이다. LG CNS에서는 이런 현실에서 컨버전스 프로젝트의 특성에 맞추어 기존 프로젝트 관리자(Project Manager)의 역할을 재정의하고 이를 현장에 적용시키기 위해 그동안의 컨버전스 프로젝트를 수행하면서 얻은 이슈에 HPT(Human Performance Technology; 수행공학)를 적용하여 조직적인 차원에서 프로젝트 관리자의 육성 로드맵을 만들고 이를 확산시키는 활동을 진행하고 있다. 이에 본 발표에서는 HPT측면에서 컨버전스 프로젝트 이행역량을 강화하기 위한 사례를 소개하고자 한다.

• Journal of Korean Society of Disaster and Security
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• v.6 no.3
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• pp.49-57
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• 2013

This paper is to show the long-term roadmap by analyzing the development trend for the safety technology of facility disaster in the country and abroad, and it is designed to plan the long term and roadmap in response to change the disaster environment. Recently in the country, it is increasing the needs of the long term roadmap design of the facility disaster research development in the facility disaster, by the repidly of the social and the living and the related governments response's changing. The U.S. is going to develop the disaster responding research by planning the its master plans, including the NRF (National Responing Framwork), the NIMS (National Incident Management System), and its sinarios etc.. Japan is going to develop the research planning in the annual report of the disaster prevention, and we going to do the study projects about the facility disaster area with the NEMA (National Emergency Management Agency) and NDMI (National Disaster Management Institute). This paper is showed to design the long term roadmap of the facility disaster's study development, and to minimize the damage of the man and his property, and to set the study development system of the national facility disaster, and furthering to make the resilient planning in changing of the facility disaster's environment.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• TTA Journal
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• s.165
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• pp.104-105
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• 2016

현재 무선통신 분야의 큰 흐름은 5G와 IoT(lnternet of Things)로 대별할 수 있는데, 5G는 표준 로드맵이나 장기 사업화 계획 등이 일부 나오고 있어서 어느 정도 가시권에 들어온 반면, IoT는 아직은 정해지지 않은 면이 많이 남아 있다. 그러나 미래에 일반인들이 생활 속에서 경험하게 될 변화는 IoT 분야가 더 광범위하고 실감이 될 것으로 보이므로 업계, 학계, 그리고 연구계에서는 더 많은 준비가 필요해 보인다. 본고에서는 IoT 시대를 준비하기 위해서는 무엇을 고려해야 하는지를 제기해 보고자 한다.