• 한국도서관정보학회지
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• 제51권2호
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• pp.103-131
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• 2020

본 연구는 임산공학 연구자들의 연구데이터 관리 현황 파악을 목적으로 하였다. 연구 목적 달성을 위해 DAF(Data Asset Framework)라는 도구를 이용하여 설문조사를 수행하였다. DAF는 기관이 연구데이터를 어떻게 관리하고 있는지 식별, 위치, 설명 및 평가할 수 있는 수단을 제공하는 조사 도구이다. 이러한 DAF를 활용하여 국립산림과학원 내 임산공학 분야 연구자들을 대상으로 연구데이터 관리 현황을 분석한 결과, 연구데이터 생성방법 및 유형, 공유, 저장, 보존 그리고 재사용 등과 같은 5가지의 카테고리의 현황 및 문제점이 도출되었으며, 또한 문제점 관련하여 해결책을 제시하였다. 이번 연구는 DAF와 같은 체계적인 도구를 활용한 기초 조사로서 특정 분야의 RDM 시스템 설계 시 연구데이터의 현황 및 문제점을 분석하는데 참고자료가 될 수 있을 것이다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.2473-2478
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• 2011

The asset management of railway facilities is a total framework for finally supporting a safe and comfortable train service, which includes functions of supporting evaluation of condition and performance of infrastructures, making the decision method of repair or rehabilitation of deteriorated facilities, and lengthening the life cycle of structure through the decision of adequate cost and time of repair or reinforcement. In the range of the asset management, organization, human, the target, and information & data of company are included. Therefore, in this paper, appling the method of asset management analysis to the railway structures, the process of the risk assesment using BRE(Business Risk Exposure) and the basis of consisting optimized renewal decision-making are expressed.

• 정보관리학회지
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• 제37권3호
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• pp.77-106
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• 2020

본 연구에서는 학제 간 연구가 가장 활발하고 응용가능성이 가장 높은 응집물질물리분야의 연구데이터를 체계적으로 관리하기 위한 개선방안을 제안하였다. 이를 위해 연구데이터 관리 도구인 Data Asset Framework (DAF)와 데이터 공유 및 재사용을 위한 FAIR원칙을 바탕으로 설문 내용을 구성하여 14명의 연구자를 대상으로 응집물질물리분야의 연구데이터 관리 현황을 수집하였다. 수집된 데이터는 설문에 응답한 연구자의 특성 및 기초정보, 데이터 보존 및 관리, 데이터 공유 및 접근에 관한 데이터로 구성되었다. 수집된 설문결과를 분석하여 응집물질물리분야의 연구데이터 특징과 데이터 수집과 생산, 데이터 보존과 관리, 데이터 공유 및 접근에 대한 9가지 문제점을 도출하였으며, 각 측면에서 도출된 문제점에 대한 개선방안을 제언하였다.

• 지식경영연구
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• 제19권1호
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• pp.133-146
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• 2018

This study develops an Education framework for users who need public open data for workplace etiquette education in a timely manner by mobile application. It facilitates utilizing efficiently Workplace etiquette contents that scattered in various platforms such as blogs, Youtube and web-sites run by private education agencies. Furthermore, it makes Public open data for workplace etiquette through gathering 'metadata', which is a comprehensive source of workplace etiquette. Accordingly, framework changes recognition about necessity of workplace etiquette education positively and suggests method that can promote effective workplace etiquette education. If the system in the study can provide public open data of workplace etiquette education, many young job applicants and workers will have a proper perception on it and sound workplace etiquette culture will be settled in the companies. Public data has been rising as a vital national strategic asset these days. Hopefully the public data will pave a way to discover the blue ocean in the market and open up a new type of businesses.

• 스마트미디어저널
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• 제8권2호
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• pp.94-98
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• 2019

본 논문에서는 관리자가 자신이 소유하고 있는 IoT 시스템을 관리할 수 있도록 하는 도구를 개발한다. 장비 에이전트는 오픈소스 블록체인 프레임워크 기반으로 구성해 데이터의 불변성을 보장하고, 장비에 연결된 AP에 대한 추적성을 확보하여 자산에 대한 위치를 파악할 수 있다. 관리자는 블록체인 장부에서 장비의 연결 내역을 추적할 수 있다. 추가로 네트워크 형성 과정 중 발생하는 ARP 프로토콜의 ARP 추가 요청에 대한 신뢰를 없애 ARP poisoning 공격을 방지할 수 있는 가능성에 대해 연구한다.

• Journal of Construction Engineering and Project Management
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• 제7권1호
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• pp.30-36
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• 2017

Most local agencies such as counties and small cities continuously express difficulties in making technically and financially defensible decisions on their pavement infrastructure maintenance and rehabilitation. Unlike pavement systems managed by state highway agencies, the total lane-miles of many local pavements are significantly short and they are managed by a limited number of staff who typically have multiple responsibilities. Most local agencies also do not have historical pavement performance data and the lack of a systematic decision making framework exacerbates the problem. A structured framework and an easily accessible decision support tool that reflects their local requirements, practices and operational conditions would greatly assist them in making consistent and defensible decisions. This study fills this gap by developing a systematic pavement treatment selection framework and a spreadsheet based tool for local agencies. It is expected that the proposed framework will significantly help local agencies to improve their pavement asset management practices at the project level.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• ETRI Journal
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• 제33권3호
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• pp.443-453
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• 2011

This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

• Journal of Information Technology Applications and Management
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• 제22권3호
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• pp.1-18
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• 2015

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

• ETRI Journal
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• 제42권5호
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.