• Information and Communications Magazine
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• v.30 no.1
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• pp.10-16
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• 2012

모바일 혁명으로 대변되는 제2의 인터넷혁명시대의 도래에 따라 방송통신산업에도 큰 변화가 나타나고 있다. 구글, 애플 등 플랫폼 사업자를 중심으로 생태계 경쟁이 심화되면서, SW 콘텐츠 개발자 등에게 새로운 성장기회가 형성되고 있고 정보의 생성 및 유통속도가 가속화되면서 클라우드, 빅데이터 등 신산업이 부상하고 있다. 또한, 인터넷 중심으로 방송통신망이 융합되면서 스마트TV 등 인터넷 동영상서비스가 확산되고, 방송시장 내에서도 유료방송서비스간 경쟁이 심화되고 있다. 스마트 생태계 구축경쟁과 인터넷 중심 융합으로 특징지워지는 새로운 방송통신 패러다임은 기존 방송통신산업구조와 규제체계의 전환을 촉구하고 있으며, 방송통신 융합을 촉진하고 방송통신산업을 육성하기 위해서는 C(콘텐츠)-P(플랫폼)-N(네트워크)-T(기기)를 아우르는 종합적인 접근이 요구되고 있다. 본 고에서는 우선 방송통신 분야의 정책환경 변화를 살펴본 후 네트워크 고도화, 방송통신 R&D, ICT 중소 벤처기업 육성, 방송통신콘텐츠산업 경쟁력 제고 등 방송통신 산업이 한 단계 더 도약하기 위해 필요한 주요 정책과제들을 살펴본다.

• Information and Communications Magazine
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• v.30 no.10
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• pp.18-24
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• 2013

협력 지능형 교통 시스템 (C-ITS: Cooperative Intelligent Transportation System)은 차량이 도로 인프라 또는 다른 차량과 서로 통신하면서 전방의 교통사고 및 장애물과 주변 차량 정보를 공유하여 위험상황을 피할 수 있도록 사전에 경고하는 미래형 교통체계이다. C-ITS는 보행자 및 차량의 안전을 향상시키고 배출탄소량 감소 및 교통물류의 효율성을 증가시킬 수 있는 미래사회의 핵심 인프라가 될 전망이다. C-ITS의 성공적인 실현을 위해서는 다중 센서 융 복합 기반 교통정보 수집, 교통정보를 쌍방향으로 유통하기 위한 통합 무선 통신망, 스마트 기기와 이동통신망을 활용한 실시간 교통정보 수집 및 빅 데이터 처리와 주문형 서비스 제공 등의 핵심 기술 개발이 필요하다. 본 고에서는 협력형 교통 환경에서의 C-ITS 구조 및 관련 핵심 요소 기술을 소개하고, 앞으로 해결할 과제를 소개 한다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.85-88
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• 2015

본 연구는 사회현안으로 대두되고 있는 사회재난의 선제예방 및 효과적 대응을 위해 개발되는 인명지킴이 시스템과 관련하여 인명지킴이 시스템 기반 사회재난 대응 실증방안을 제시한다. 실증 추진을 위한 빅데이터 분석, 시나리오 도출, 테스트베드 구축 및 시스템 연계, 실증 및 확산에 이르기까지 각 단계별 수행내용을 다룬다. 또한, 김포시에서 추진하고 있는 스마트안전도시와 연계하여 실증사업 수행 시 주요 고려사항 및 수행 내용을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.614-617
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• 2021

법률 문서는 일반 사용자가 이해하기 어려운 용어로 이루어져 있고 특히 장문의 문서가 많아 법률시스템에 종사하는 종사자들 또한 많은 양의 문서를 읽기가 어려운 현실이다. 이에 문서 요약 방법중 딥러닝 기반의 사전학습 모델을 적용한 추출요약기반, 생성요약 방법론과 딥러닝 이전의 핵심문장 추출 방법론을 비교하여 법률용어의 요약성능에 대한 비교 평가를 수행하고자 하며 추후 연구과제로 법률문서에 특화된 요약 모델을 만들어보고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.895-897
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• 2024

본 논문에서는 2024년 4월부터 과학기술정보통신부 재원으로 시작하는 "디지털트윈 융합 의료혁신선도" 사업 내용을 소개한다. 본 사업은 첨단 의료기기 클러스터를 운영 중인 강원도를 중심으로 국내 디지털 의료기기 개발 혁신을 위한 디지털트윈 활용 기반 구축을 목표로 하며, 이를 위해 ▲디지털트윈 통합 인프라 구축(디지털트윈 모델, 디지털트윈 연계 플랫폼), ▲시뮬레이션 검증 인프라 구축, ▲의료기기 디지털트윈 사업화를 세부 과제로 진행할 예정이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.95-101
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• 2023

Globally, the importance of utilization and management of marine spatial information is being maximized, and analyzing such data is emerging as a major driving force for R&D. In Korea, it is expected that collecting marine data from the past to the present and extracting its value will play an important role in the development of science in Korea in the future. In particular, marine static data constitutes a huge big database, and it is necessary to store and store the collected data without loss as high data collection costs and high-level observation techniques are required. In addition, the Disaster Safety Intelligence Convergence Center's "Marine Digital Twin Establishment and Utilization-Based Technology Research" task requires collection and analysis of marine data, so this paper conducts a current status survey of static marine data. And we present a series of algorithms that collect and store them in a database.

• Informatization Policy
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• v.25 no.4
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• pp.37-64
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• 2018

This study aims to forecast the future of the Korean society via a big data analysis. Based upon two sets of database - a collection of 46,000,000 news on 127 media in Naver Portal operated by Naver Corporation and a collection of 70,000 academic papers of social sciences registered in KCI (Korea Citation Index of National Research Foundation) between 2005-2017, 40 most frequently occurring keywords were selected. Next, their temporal variations were traced and compared in terms of number and pattern of frequencies. In addition, core issues of the future were identified through keyword network analysis. In the case of the media news database, such issues as economy, polity or technology turned out to be the top ranked ones. As to the academic paper database, however, top ranking issues are those of feeling, working or living. Referring to the system and life-world conceptual framework suggested by $J{\ddot{u}}rgen$ Habermas, public interest of the future inclines to the matter of 'system' while professional interest of the future leans to that of 'life-world.' Given the disparity of future interest, a 'mismatch paradigm' is proposed as an alternative to social forecasting, which can substitute the existing paradigms based on the ideas of deficiency or deprivation.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.