• Journal of the Korea Convergence Society
• /
• v.11 no.12
• /
• pp.161-176
• /
• 2020

This study was to examine the group difference of parental composition (parents, single parent group) in the relationship between parental attitude (supervision, affection, rational explanation) and children's life satisfaction through ego-resilience as a mediating factor. For this, a multi-group analysis was conducted using 310 student data from the 4th panel data of the KCYPS. The results were as follows: First, parents' affectionate parenting attitude on children's life satisfaction was significant in both groups. Second, affectionate and supervisory attitude had effects to improve ego-resilience only in the single-parent group. Third, the affectionate attitude showed a significant positive effect on children's life satisfaction by mediating ego-resilience only in single parent group. This study verified the structural relationship of factors affecting children's life satisfaction and different the pattern of that relationship depending on parental composition. Finally, limitations and implications for future research were presented.

• Information Systems Review
• /
• v.13 no.1
• /
• pp.1-11
• /
• 2011

This study empirically investigated the effect of firm-level data quality on economic performance in the Korean financial industry during 2008~2009. The data quality was measured by data quality management process index and data quality criteria by Korea Database Agency, and financial firm performance data was acquired from Financial Statistics Information System of the Financial Supervisory Service. The result showed that the data quality has statistically significant impacts on financial firm performance such as sales, operating profit, and value added. If the data quality management process index increases by one, the value added can increase by 2.3 percent. Moreover, the data quality criteria increase by one, the value added can increase by 72.6 percent.

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.532-533
• /
• 2008

본 논문에서는 하루에 수백만 명이 이용하는 지하철 역사내의 환경 관리를 유비쿼터스(Ubiquitous) 기술을 적용하여 실질적이고 효과적으로 실행하는 방안을 제안하고자 한다. 지하철 역사는 지하라는 특수한 환경이기 때문에 외부의 신선한 공기를 유입하여 역사내의 공기질을 쾌적하게 유지해야할 필요가 있다. 또한 실내 공기질을 오염시키는 환경가스나 미세먼지 등의 오염도에 대해서도 지속적으로 감시하여야 한다. 이를 위해 설치가 간편하고 소형의 측정 장치를 지하역사내의 주요 지점에 설치하여 오염물질을 측정하고 그 결과 데이터를 모으는 집중기(Collector)를 운영하여 최종적으로 측정 데이터를 중앙 서버까지 전송할 수 있다면 지하철 관리자에 의한 지하역사내의 오염물질을 효과적으로 관리 감독하는 종합적인 지하철 환경 관리시스템의 운영이 가능할 것으로 판단된다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10e
• /
• pp.61-63
• /
• 2002

멀티미디어 컨텐츠의 지능형 선택/검색 시스템(MISS: Multimedia Content Intelligent Selection/search) 는 콘텐츠를 공급하는 서버에 다량의 멀티미디어 컨텐츠들이 존재하며, 이 컨텐츠 중에서 원하는 것을 검색, 선택하는 시스템이다. 지능적 검색, 선택기능을 갖는 MISS 시스템은 인터넷 및 네트워크상에 연결된 시스템들간의 맞춤형 서비스 구현에 필요한 핵심이며, 모든 종류의 멀티미디어 콘텐츠에 적용 가능하다. 현재 WWW 서비스경우는 정보를 찾기 위하여 웹상에서 문서를 찾아주는 텍스트 기반 정보검색기술이 사용되고 있는데, 점점 우리가 접하는 정보의 형태는 텍스트와 함께 화상, 음성, 동영상 등의 멀티미디어화 및 디지털화하고 있다. 사용자들에게는 멀티미디어 데이터를 효과적으로 찾아야 하는 필요성이 증가하고 이에 따라 방대한 양의 분산된 멀티미디어 데이터를 처리할 수 있는 색인 및 검색 도구의 요구가 커지게 되었다. MISS 시스템은 WWW 서비스의 요구에도 적용될 수 있다. MISS 시스템은 다량의 동영상 콘텐츠 중에서 특정 배우, 감독등의 여러 가지 검색 조건으로 콘텐츠를 검색/선택할 수 있고, 하나의 동영상 콘텐츠 내에서 특정Video Segment를 검색할 수 있다. 본 MISS 시스템은 동영상에 대한 Search/Query를 위한DS 구조로써 MPEG-7의 User preference metadata를 이용하였다.

• Annual Conference on Human and Language Technology
• /
• 2011.10a
• /
• pp.85-89
• /
• 2011

수동 어노테이션을 통한 말뭉치 구축 작업은 많은 시간과 노력이 필요한 작업이지만, 자동화된 정보 추출 도구의 훈련 및 실험, 평가를 위해서는 꼭 필요한 작업이기도 하다. 본 논문에서는, 수동 시맨틱 어노테이션을 통한 말뭉치 구축 작업을 지원하는 수동 시맨틱 어노테이션 지원 도구 COAT를 소개한다. COAT는 각 어노테이터의 작업 효율을 높이기 위하여 GUI 기반 인터페이스를 제공하고, 작업의 대부분을 단축키만 이용하여 수행 가능하도록 설계되었다. 또한 최종 결과로 얻어지는 데이터의 신뢰성을 높이기 위하여, 최소 두 명 이상의 어노테이터가 같은 문서에 대하여 작업하면 고참 어노테이터가 각 결과물들을 통합하는 컨쥬게이션 도구를 구축하였으며, 각 어노테이터들의 작업 및 데이터들을 관리 감독하기 위한 관리자 도구를 개발하였다. 본 도구를 직접 사용하여 어노테이션 작업을 수행한 결과, 본 도구를 사용하지 않고 작업을 수행할 때와 비교하여 약 87%의 비용 절감 효과를 얻을 수 있었다.

• Journal of Digital Convergence
• /
• v.18 no.6
• /
• pp.17-32
• /
• 2020

This study analyzes the regulatory issues of stakeholders, the firm, the government, and the individual, in the data industry using the principal-agent theory. While the importance of data driven economy is increasing rapidly, policy regulations and restrictions to use data impede the growth of data industry. We applied descriptive case analysis methodology using principal-agent theory. From our analysis, we found several meaningful results. First, key policy actors in data industry are data firms and the government among stakeholders. Second, two major concerns are that firms frequently invade personal privacy and the global companies obtain monopolistic power in data industry. This paper finally suggests policy and strategy in response to regulatory issues. The government should activate the domestic agent system for the supervision of global companies and increase data protection. Companies need to address discriminatory regulatory environments and expand legal data usage standards. Finally, individuals must embody an active behavior of consent.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.5
• /
• pp.823-834
• /
• 2020

In the past, when the personal information leakage incident of the three card companies, the computer program development was followed by the same strict electronic financial supervision regulations as the operating environment. However, when developing a computerized program, the application data is being verified with the integrity of the test data being compromised because the identification of the scope of conversion of the test data associated with the application is unclear. Therefore, in this paper, we proved by presenting a process and algorithm for selecting a range of sufficient test data conversion targets associated with a specific application.

• Journal of Advanced Navigation Technology
• /
• v.26 no.6
• /
• pp.528-535
• /
• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.24 no.1
• /
• pp.139-145
• /
• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.4
• /
• pp.145-153
• /
• 2020

In the rapidly changing era of the fourth industry, big data is being used in various fields. In recent years, the use of big data has been rapidly applied to overall cultural and artistic contents, and among them, the use of big data is essential as a film genre with a lot of capital. This research method is analyzed as the film , which won the Palme d'Or Prize of the 72nd Cannes Film Festival in 2019 and the works and directors' award at the Academy Awards. The analyzed value predicts the film's performance through opinion mining, which gives the value of the change and sensitivity of each data cycle, and extracts the utilization index and topic keywords of SNS such as Facebook and Twitter to reflect the audience's interest. Identify the factors. As such, if model performance and model development can be predicted through model analysis of film performance using big data, the efficiency of the film production process will be maximized while the risk of production cost and the risk of film failure will be minimized.