• The Research Journal of the Costume Culture
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• v.13 no.6 s.59
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• pp.999-1010
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• 2005

The purposes of this study were to investigate used patterns in Hanbok and to find out a classification and a characteristic of motifs. The data made use of 111 pictures constituted Gegory(a Korean jacket) and Chima(a skirt) in photographs collected in wedding magazine(Wedding 21'). The data was analyzed by frequency, contents analysis. Pattern's use or not in Hanbok and a sort, a arrangement, a way of expression of patterns using frequency Classified and characteristics of motifs were analyzed contents analysis. The results of this study were as follows. First, a sort of patterns was lots of flower motifs of the plant pattern. An arrangement of and expression of patterns used mainly a partial arrangement and embroidery expression. Second, the subjects classified using criterion of classification of a external feature, forms of expression, and simplicity/complexity of line. Third, the motifs classified into plants, an animal, geometry, abstraction, and a natural object. The plant motifs were perceived the focus of flower, a combination of a flower and a stem in the plant motifs. The subjects perceived as a simplicity/complexity of flower and a drawing line of a flower and a stem.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.340-347
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• 2019

Due to advances in machine intelligence and increased demands for autonomous machines, the complexity of the underlying software platform is increasing at a rapid pace, overwhelming the developers with implementation details. We attempt to ease the burden that falls onto the developers by creating a graphical programming framework we named Splash. Splash is designed to provide an effective programming abstraction for autonomous machines that require stream processing. It also enables programmers to specify genuine, end-to-end timing constraints, which the Splash framework automatically monitors for violation. By utilizing the timing constraints, Splash provides three key language semantics: timing semantics, in-order delivery semantics, and rate-controlled data-driven stream processing semantics. These three semantics together collectively serve as a conceptual tool that can hide low-level details from programmers, allowing developers to focus on the main logic of their applications. In this paper, we introduce the three-language semantics in detail and explain their function in association with Splash's language constructs. Furthermore, we present the internal workings of the Splash programming framework and validate its effectiveness via a lane keeping assist system.

• Women's Health Nursing
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• v.25 no.3
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• pp.258-272
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• 2019

Purpose: The purpose of this study was to identify nursing interventions for the postpartum breast care of mothers and determine the effectiveness of interventions for breast pain and engorgement by systematic review. Methods: Eight national and international databases were reviewed to retrieve and collect randomized controlled trial and controlled clinical trial literature published up to March 2015. Two reviewers independently selected the studies and performed data abstraction and validation. The risk of bias was assessed using Cochrane criteria. A meta-analysis of the studies was performed to analyze the data. Results: The meta-analysis showed that breast massage, along with routine breast care, resulted in a 3.52-point reduction in pain on a 10-point visual analogue scale. Meta-analysis of therapy with cold cabbage leaves and routine breast care showed a pain reduction of 0.54 points. Meta-analysis of cold cabbage leaf application in the experimental group versus cold compress therapy in the comparison group showed a pain reduction of 0.44 points. Meta-analysis of cold cabbage leaf application and routine breast care showed an engorgement reduction of 0.67 points. Conclusion: The results of the analysis of 12 articles showed that hot and cold compresses, breast massage, and cabbage application were effective for postpartum breast pain and engorgement.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.29-37
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• 2023

Purpose: This study is to explore how to enhance an organizational citizenship behavior (OCB) for private companies, boosting their employees; performance. With OCB in place, businesses won't have to worry about employees engaging in harmful or counterproductive actions. Better coordination will benefit employees' skill sets and the company's overall performance. Research design, data and methodology: We used a data extraction form and present it as an appendix. These forms could demonstrate to the reader what the present authors looked for and how they found it. We also investigated to obtain text datasets whether any extractions were carried out in duplicate, and, if so, whether duplicate abstraction was carried out independently. Results: There are four solutions to boost employees' OCB for HR practitioners: 'Creating an Environment that Supports Constructive OCB', 'Encouraging Productive Behavior in the Workplace and Reward properly, 'Integrating Corporate Citizenship into Performance Evaluations', and 'Training to Use OCB and Educating on its Benefits'. Conclusions: Based on the research findings of the current study, this study strongly concludes that OCB should be encouraged, and employers and employees should collaborate on efforts to boost morale and increase productivity. As a direct result of their efforts, their firms enjoy improved earnings while experiencing reduced overhead costs.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1075-1083
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• 2009

The scientific research method went through construction of hypothesis and collection of data by experiment or observation and abstracting the hypothesis based on the experience which uses the data. The statistical methodology plays an important role in this process. The method which acquires a data becomes an initial process of abstraction and a survey research using structured questionnaires is a basic tool. After the data is acquired, the high-class statistical techniques such as the regression analysis and the linear structural equation model are used to abstract a hypothesis. By the way, from time to time the concepts which have become abstractive do not help us to understand an actual phenomena, rather it is need to extract some knowledge from questions themselves. In this article, we review the well known statistical methods providing the ways of finding core questions which possibly answer a researcher wants to know. We deal with course evaluation data as an example and try to set up the strategy for improving course evaluation.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.193-199
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• 2014

In the application development process is an important task to improve the quality of the data is the principal means for achieving the functional requirements of the wireless sensor network applications because it is the data obtained in the real world. It should be possible to meet the requirements for the quality improvement to those detailed for improving the quality in a short period of time for developing a prototype of an application and low cost. However, development of the existing methods can not be satisfied simultaneously in order to use a consistent abstraction because antinomical relation between ability of description and ease of description. In this paper, we propose a meta-data designed to support the improvement of data quality through abstract modeling language is also used in combination to a multiple of the other.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.388-400
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• 2018

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in $21^{st}$ century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included 'generalization' in CTPF of science education instead of 'parallelization' which was not found. The implication about teacher education was made based on the CTPF in terms of science education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.144-150
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• 2017

Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.83-98
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• 2005

Exploring distributional patterns of multivariate data is very essential in understanding the characteristics of given data set, as well as in building plausible models for the data. For that purpose, low-dimensional visualization methods have been developed by many researchers along various directions. As one of methods, Kohonen's SOM (Self-Organizing Map) is prominent. SOM compresses the volume of the data, yields abstraction from the data and offers visual display on low-dimensional grids. Although it is proven quite effective, it has one undesirable property: SOM's display is discrete. In this study, we propose two techniques for enhancing quality of SOM's display, so that SOM's display becomes continuous. The proposed methods are demonstrated in two numerical examples.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.565-573
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• 2017

The ex-post evaluation of governmental crisis management is an important issues since it is necessary to prepare for the future disasters and becomes the cornerstone of our success as well. In this paper, we propose a data cube model with data mining techniques for the analysis of governmental crisis management strategies and ripple effects of foot-and-mouth(FMD) disease using the online news articles. Based on the construction of the data cube model, a multidimensional FMD analysis is performed using on line analytical processing operations (OLAP) to assess the temporal perspectives of the spread of the disease with varying levels of abstraction. Furthermore, the proposed analysis model provides useful information that generates the causal relationship between crisis response actions and its social ripple effects of FMD outbreaks by applying association rule mining. We confirmed the feasibility and applicability of the proposed FMD analysis model by implementing and applying an analysis system to FMD outbreaks from July 2010 to December 2011 in South Korea.