• 한국노년학
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• v.30 no.3
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• pp.1005-1020
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• 2010

This study was conducted to explore and understand the meaning of stigma of families having old people admitted to nursing home within the Korean culture. Data collection was performed through in-depth interviews and participant observations which were recorded and transcribed verbatim with the consent of the participants. The key informants were 12 people having the aged family member in nursing home. The data was collected from October 2008 to February 2009 until completed. Data were analyzed utilizing the taxonomic analysis method developed by Spradley. As a result, 24 themes, 8 categories and 4 cultural domains are founded from the cases. The cultural domains resulted from the analysis are: 『Incompetence of Oneself: 'Adaptation to Inevitable Realities', 'Difficulty of Economic Independence', 'Difficulty of the Subjective Self-assertion'』, 『Contradictoriness of Decision Making: 'Decision Making Different from Own Mind', 'Conflicts between Neighboring'』, 『Self-rationalization of Decision Making: 'Self-comfort of Decision Making'』, 『Shifting Responsibility: 'Services Different from that of Family', 'Laking in Sincerity of Responsible Institution'』. Theoretical model about stigma of the family having old people admitted to nursing home by the research result in the above was able to be confirmed that it was expressed with the original form of thought of recursive system which continuously showing the inconsistency of decision making, rationalizing decision making, and shifting one's own responsibility in the process of accomplishing the duty of supporting old people. Based on the results, I discussed the meaning of stigma of families having old people admitted to nursing home and provided recommendations for future research.

• Korean Journal of Optics and Photonics
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• v.16 no.3
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• pp.182-190
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• 2005

The fiber Raman amplifier(FRA) is a distinctly advantageous technology. Due to its wider, flexible gain bandwidth, and intrinsically lower noise characteristics, FRA has become an indispensable technology of today. Various FRA modeling methods, with different levels of convergence speed and accuracy, have been proposed in order to gain valuable insights for the FRA dynamics and optimum design before real implementation. Still, all these approaches share the common platform of coupled ordinary differential equations(ODE) for the Raman equation set that must be solved along the long length of fiber propagation axis. The ODE platform has classically set the bar for achievable convergence speed, resulting exhaustive calculation efforts. In this work, we propose an alternative, highly efficient framework for FRA analysis. In treating the Raman gain as the perturbation factor in an adiabatic process, we achieved implementation of the algorithm by deriving a recursive relation for the integrals of power inside fiber with the effective length and by constructing a matrix formalism for the solution of the given FRA problem. Finally, by adiabatically turning on the Raman process in the fiber as increasing the order of iterations, the FRA solution can be obtained along the iteration axis for the whole length of fiber rather than along the fiber propagation axis, enabling faster convergence speed, at the equivalent accuracy achievable with the methods based on coupled ODEs. Performance comparison in all co-, counter-, bi-directionally pumped multi-channel FRA shows more than 102 times faster with the convergence speed of the Average power method at the same level of accuracy(relative deviation < 0.03dB).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.