• Information and Communications Magazine
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• v.34 no.1
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• pp.58-69
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• 2016

글로벌 식품 시장의 확대와 변화하는 소비자의 식품 소비 패턴은 기존의 농식품 서비스 산업에 큰 변화를 불러오고 있다. 다가오는 식품시장에 대응하기 위해서는 다각화된 유통구조에서 오는 식품의 안전성 문제를 해결함과 동시에, 급변하는 시장의 변화를 수용할 수 있는 비즈니스 플랫폼 구축이 필수적이다. 본 논문에서는 이러한 변화에 대응하고 스마트 팜 에서부터 유통물류, 그리고 식품안전, O2O 등 식품 응용서비스의 기반이 될 수 있는 GS1 표준 글로벌 식품 생태계를 소개한다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.719-729
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• 2017

Recently, the field of agriculture has been gaining a new leap with the integration of ICT technology in agriculture. In particular, smart farms, which incorporate the Internet of Things (IoT) technology in agriculture, are in the spotlight. Smart farm technology collects and analyzes information such as temperature and humidity of the environment where crops are cultivated in real time using sensors to automatically control the devices necessary for harvesting crops in the control device, Environment. Although smart farm technology is paying attention as if it can solve everything, most of the research focuses only on increasing crop yields. This paper focuses on the development of a system architecture that can harvest high quality crops at the optimum stage rather than increase crop yields. In this paper, we have developed an architecture using apple trees as a sample and used the color information and weight information to predict the harvest time of apple trees. The simple board that collects color information and weight information and transmits it to the server side uses Arduino and adopts model-driven development (MDD) as development methodology. We have developed an architecture to provide services to PC users in the form of Web and to provide Smart Phone users with services in the form of hybrid apps. We also developed an architecture that uses beacon technology to provide orchestration information to users in real time.

• Korea Information Processing Society Review
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• v.15 no.2
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• pp.90-95
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• 2008

e-Science를 기반으로 한 고에너지물리 연구 환경 구축은, 실제로 실험 데이터가 생산되는 외국의 가속기 연구소에 가지 않고서도, 언제어디서나 실제 가속기 연구소에서 고에너지물리 연구를 수행하는 것과 같은 연구 환경을 제공한다. 그 구성 요소로서는 1) 데이터 생산(production), 2) 데이터 프로세싱(processing), 3) 데이터 분석(analysis)이 있다. 데이터 생산은 원격 제어시설(remote control room)을 구축하여 원격으로 데이터 생산에 참여하는 것이며, 데이터 프로세싱은 그리드 팜을 활용한 데이터 처리를 뜻하며 데이터 분석은 EVO(Enabling Virtual Organization) 시스템을 활용하여 공동 협업 환경으로 연구 결과물을 얻는 것이다. 이러한 개념을 고에너지물리 실험의 하나인 CDF 실험에 구현하여 활용한 사례를 보여 준다.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.29-37
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• 2023

With the increasing demand for seafood and technological advancement in aquaculture, the industry has continuously grown. On the other hand, digital twins have been actively applied to various industries. Aquaculture deals with live aquatic animals that are sensitive to growth environment management. Hence, applying a digital twin to smart aquaculture may lead to a substantial economic benefit because it enables the optimization of different variables. We analyze the status of digital twin development in agriculture. The services of the aquafarm digital twin are divided into 1) data management, 2) optimization, and 3) intelligence. Standardization related to the aquafarm digital twin is also discussed. Based on the analyses, the development stage of aquafarm digital twin is defined, and directions of technology development are suggested.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.10
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• pp.1145-1156
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• 2015

There is a shortage of mobile application systems readily applicable to the field of crop cultivation in relation to diseases and insect pests directly connected to the quality of crops. Most of system have been devoted to diseases and insect pests that would offer full predictions and basic information about diseases and insect pests currently. But for lack of the instant diagnostic functions seriously and the field of crop cultivation, we design and implement a crop cultivation field-oriented smart diseases and insect pests information retrieval system based on mobile for u-Farm. The proposed system had such advantages as providing information about diseases and insect pests in the field of crop cultivation and allowing the users to check the information with their smart-phones real-time based on the Lucene, a search library useful for the specialized analysis of images, and JSON data structure. And it was designed based on object-oriented modeling to increase its expandability and reusability. It was capable of search based on such image characteristic information as colors as well as the meta-information of crops and meta-information-based texts. The system was full of great merits including the implementation of u-Farm, the real-time check, and management of crop yields and diseases and insect pests by both the farmers and cultivation field managers.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.