• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.459-480
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• 2003

Purpose: This study was done to analyze the contents of nurses' ethical decision-making in four of hypothetical dilemma cases using the Cameron's Ethical Decision-Making Model of 'Value, Be, Do'. Method: Sixteen nurses who work at ICU at present or worked before, participated from April 10 to May 10 in 2002. The participants were interviewed three times each and for 40 minutes at once, with a structured questionnaire at their working places and locker rooms. The data was analyzed by a procedure of qualitative content analysis into three categories; what should I value, who should I be, what should I do. Result: 1) In consistency, most of subjects showed a unified voice in 'Value, Be, Do'. Exceptionally 8 subjects showed inconsistency such as 3 in active treatment to the incurable patients(case 1), 1 in treatment truth-telling to the terminally ill patients(case 2), 3 in conflict with uncooperative doctors(case 3), 3 in dying patients and euthanasia(case 4). Only one subject showed inconsistency in 3 dilemma cases. 2) Closing the interview procedure, the subjects evaluated Cameron's Model as it would help them build consistent value, carry right action, and cope to conflicts. Conclusion: On the basis of the results, it is recommended that nursing ethics should adopt the ethical decision-making model, and be applied to the curriculum of nursing colleges and continuing education program for clinical nurses.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.505-514
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• 2019

Purpose: This study aimed to introduce and elucidate changes in Strauss and Corbin's grounded theory and discuss its application to the field of nursing in South Korea. Methods: The changes in grounded theory by Strauss and Corbin were examined through a literature review of grounded theory from its inception. Results: Strauss and Corbin acknowledged their philosophical backgrounds of symbolic interactionism and pragmatism; however, their methodology based on positivism overwhelmed their epistemology and ontology. This inconsistency has been represented by the coding paradigm and the premise of "emergent from the data." In the revised version of Basics, Strauss and Corbin modified their theory to weaken the coding paradigm and strengthen the strategies for the development of substantive theory. Conclusion: Strauss and Corbin's revised grounded theory did not fully address the inconsistency of their epistemology and ontology between their acknowledgement and methodology. However, these changes constitute a meaningful step toward resolving inconsistencies and highlight the development of substantive theory. This has implications for Korean nursing researchers who have utilized methodologies in grounded theory with dogmatic approaches; grounded theory, with its evolving nature, is not a finalized method and calls for open approaches for the development of a grounded theory that fits Korean nursing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.49-60
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• 2001

Data warehouse(DW) manages all information in a Enterprise and also offers the specific information to users. However, it might be difficult to develope an effective DW system due to varieties in computing facilities, data base, and operating systems. The heterogeneous system environments make it harder to extract data and to provide proper information to usesr in real time. Also commonly occurred is data inconsistency of non-integrated legacy system, which requires an effective and efficient data extraction flow control as well as data cleansing. We design the integrated automatic ETT(Extraction Transformation Transportation) system to control data extraction flow and suggest implementation methodology. Detail analysis and design are given to specify the proposed ETT approach with a real implementation.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.199-213
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• 2022

The unsupervised domain adaptation can solve the impractical issue of repeatedly collecting high-quality training data every year for annual crop classification. This study evaluates the applicability of deep learning-based unsupervised domain adaptation models for crop classification. Three unsupervised domain adaptation models including a deep adaptation network (DAN), a deep reconstruction-classification network, and a domain adversarial neural network (DANN) are quantitatively compared via a crop classification experiment using unmanned aerial vehicle images in Hapcheon-gun and Changnyeong-gun, the major garlic and onion cultivation areas in Korea. As source baseline and target baseline models, convolutional neural networks (CNNs) are additionally applied to evaluate the classification performance of the unsupervised domain adaptation models. The three unsupervised domain adaptation models outperformed the source baseline CNN, but the different classification performances were observed depending on the degree of inconsistency between data distributions in source and target images. The classification accuracy of DAN was higher than that of the other two models when the inconsistency between source and target images was low, whereas DANN has the best classification performance when the inconsistency between source and target images was high. Therefore, the extent to which data distributions of the source and target images match should be considered to select the best unsupervised domain adaptation model to generate reliable classification results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.6
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• pp.593-599
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• 2011

With increasing user applicability of geospatial data, user demand for manifold and accurate information has increased. The usefulness of these services derives from their combination of the advantages of as-built geospatial data in making new content. There is a spatial inconsistency and shape disagreement in fusing heterogeneous data. Conflation, defined as the combining of information from diverse sources so as to reconcile spatial inconsistencies and shape disagreement, is possible solution to the problem. In this research, we developed the technique for removing shape disagreement between aerial image and road map removed spatial inconsistency in advanced research. The process includes four processes: producing of a road candidate image, extraction of vertices, and generation of a graph by connecting the vertices. We could remove the shape disagreement using the extracted road that was derived from finding the road possible path.

• Genomics & Informatics
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• v.9 no.3
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• pp.138-141
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• 2011

Here, we report a computer program written in Python, which phases SNP genotypes and infers inherited deletions based on the pattern of Mendelian inheritance within a trio pedigree. When tiered trio genotypes that encompass three generations are available, it narrows a recombination event down to a region between two consecutive heterozygous markers. In addition, the phase information that is inferred from the upper trio that is formed by one of the parents and grandparents can be propagated to phase the genotypes of the lower trio that is formed by the parents and an offspring.

• Clinical and Experimental Reproductive Medicine
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• v.3 no.1
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• pp.5-11
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• 1976

Considerable data has been reported on the outcome of pregnancies subsequent to induced abortion, but the findings contain a great deal of inconsistency and disagreement. Most studies strongly suggest that normal deliveries are less likely to occur in subsequent pregnancies following induced abortion, in terms of gestation length, birth weight, stillbirth, and miscarriage. Other work suggests that some of the demographic and health characteristics of women who experience induced abortion are different from those women who do not; and these factors may affect the outcome of subsequent pregnancies profoundly rather than the induced abortion itself.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.243-251
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• 2000

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause the inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.441-447
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• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.