• Korean Journal of Adult Nursing
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• v.29 no.1
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• pp.63-75
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• 2017

Purpose: This study was to explore and describe the reported experiences of elderly spouses who care their bedridden spouse in the home. Methods: The participants of this study were 14 male and female elderly spouses who live in B metropolitan city and have provided care for more than six months. Data were collected from July 3 to November 6, 2014. Data analysis was done simultaneously with data collection, using the analytical methods of Strauss and Corbin for Grounded theory. Results: The core category was identified as 'going together bearing a heavy burden of care in old age.' In this study, the caring process of elderly spouses can be explained in terms of three stages such as 'a period of trial and error,' 'a period of mastering a role,' and 'a period of role transcendence'. Conclusion: The results of this study can provide an intervention framework to reduce the heavy burden of caring for an elderly spouse.

• Journal of Distribution Science
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• v.12 no.5
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• pp.93-107
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• 2014

Purpose - The pool of entrepreneurs with progressive qualities such as creativity and innovation was considered concurrently with such factors as work and capital that stimulate economic development and growth. This study aims to present a model to support the development of a strategic approach for achieving an overall academic entrepreneurship system in Iran. Research design, data, and methodology - The research design of this study is based on applied research because of its objectives, using principles and techniques formulated for basic research to solve operational and real organizational issues. This design also drives the method used, describing and interpreting the findings. Secondary data (library research) was used for this study's data collection. Because of this research's essential characteristics, no hypothesis is launched, and no research setting, questionnaire design, population or population sampling, validity or reliability tests, or statistical analysis are needed. Results and Conclusions - The model is created using a strategic approach acting in an octal setting comprising social, cultural, legal, economic, political, technological, competitive, and natural environments to present a conceptual framework for future studies.

• Corrosion Science and Technology
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• v.11 no.4
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• pp.103-107
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• 2012

In the framework of an International cooperation program in Australia-Asia, the atmospheric corrosion of metals in five nations located in this tropical zone: Australia, Vietnam, Thailand, Philippines and Indonesia was investigated. In this program, mild steel, zinc and copper were tested on a set of sites, representative for different climatic conditions: severe marine, marine, industrial, urban and rural, simultaneously with the collection of climatic parameters and pollutants. Based on the data obtained in the Program and referring to the bank of data collected in the Vietnam National Projects, modeling was used to construct a corrosion map of steel for Vietnam. The correlation of the data derived from the map compared with those from National Projects is very high, in most cases, differing by less than 2-3%.

• Journal of Cardiovascular Imaging
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• v.31 no.1
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• pp.1-17
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• 2023

The Coronary Artery Calcium Data and Reporting System (CAC-DRS) is a standardized reporting method for calcium scoring on computed tomography. CAC-DRS is applied on a per-patient basis and represents the total calcium score with the number of vessels involved. There are 4 risk categories ranging from CAC-DRS 0 to CAC-DRS 3. CAC-DRS also provides risk prediction and treatment recommendations for each category. The main strengths of CAC-DRS include a detailed and meaningful representation of CAC, improved communication between physicians, risk stratification, appropriate treatment recommendations, and uniform data collection, which provides a framework for education and research. The major limitations of CAC-DRS include a few missing components, an overly simple visual approach without any standard reference, and treatment recommendations lacking a basis in clinical trials. This consistent yet straightforward method has the potential to systemize CAC scoring in both gated and non-gated scans.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.9-20
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• 2011

Future warfare paradigm is changing to network-centric warfare and effects-based operations. In order to find first and strike the enemy in the battlefield, friendly unit requires real-time target acquisition, intelligence collection, accurate situation assessment, and timely decision. The rapid development in advanced sensor technology and wireless networks requires a significant change in operational concepts of the battlefield surveillance. In particular, the introduction of a battlefield surveillance sensor network system is a big challenge to the ground forces which have lack of automated information collection assets. Therefore this paper proposes an ontology-based context-aware framework for the battlefield surveillance sensor network system which is needed for early finding the enemy and visualizing the battlefield in the ground force operations. Compared with the performance of existing systems, the one of the proposed framework has shown highly positive results by applying the context systems evaluation method. The framework has also proven to be satisfactory by the structured evaluation method using device collaboration. Since the proposed ontology-based context-aware framework has a lot of advantages in terms of scalability and reusability, the ground force's reconnaissance and surveillance system can be widely applied to expand in the future. And, ontology-based model has some weak points such as ontology data size, processing time, and limitation of network bandwidth. However, these problems can be resolved by customizing properly to fit the mission and characteristics of the unit. Moreover, development of the next-generation communication infrastructure can expedite the intelligent surveillance and reconnaissance service and may be expected to contribute greatly to expanding the information capacity.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Korean Journal of Construction Engineering and Management
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• v.9 no.5
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• pp.86-94
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• 2008

Productivity is one of key management indexes for evaluating soundness of a manufacturing organization and its efficiency. In many aspects of productivity management in the construction industry, however, intuition of an experienced field manager still plays a greater role while productivity data is not utilized efficiently for the construction management purposes, because the collection and analysis of the productivity-related information are not systematic. Lack of systematic method in collecting and analyzing the productivity data results in such problems. The existing WBS should therefore be improved to solve them. The authors developed a new WBS for productivity data collection and analysis by following the research direction that was determined by literature reviews, overseas cases, and interviews with field engineers. The new breakdown structure was then evaluated for its feasibility as a productivity analysis framework. It is expected that the productivity data collected by the WBS will be used for OLAP and mining for future productivity forecast.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.324-332
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• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of Engineering Education Research
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• v.22 no.5
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• pp.37-48
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• 2019

The government has tried to develop a platform for systematically collecting and managing engineering education data for policy proposals. However, there have been few cases of big data analysis platform for policy proposals in engineering education, and it is difficult to determine the major function of the platform, the purpose of using big data, and the method of data collection. This study aims to collect the cases of big data analysis systems for the development of a big data system for educational policy proposals, and to conduct a study to analyze cases using the analysis frame of key elements to consider in developing a big data analysis platform. In order to analyze the case of big data system for engineering education policy proposals, 24 systems collecting and managing big data were selected. The analysis framework was developed based on literature reviews and the results of the case analysis were presented. The results of this study are expected to provide from macro-level such as what functions the platform should perform in developing a big data system and how to collect data, what analysis techniques should be adopted, and how to visualize the data analysis results.