• Women's Health Nursing
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• v.12 no.2
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• pp.87-96
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• 2006

Purpose: This study was conducted to provide fundamental information for a system establishment of advanced practice nursing for gynecological cancer patients (APN-GCP). Method: Data was collected by focus group and individual interviews and analyzed in the framework of the Grounded theory method mapped by Strauss and Corbin (1990). There were 13 subjects in this study (nurses, doctors, patient and her family). Result: We identified 87 concepts, 22 sub-categories, and 10 categories. Categories for role expectation were arrangement of diagnosis and treatment process, giving information of treatment course, support of treatment process, patients' right toward making a decision of treatment, counseling and teaching after discharge from hospital, medical insurance and financial problems, counseling about sexual problems and use of family and community resources. All subjects perceived the necessity of an APN-GCP. An APN-GCP requires over 2$\sim$7 years clinical experience and a master's degree. Services would be performed from initial registration to termination of treatment or death, and accomplished on an outpatient clinic basis. Conclusion: The nursing delivery system and curriculum should be developed for a women's health nurse practitioner including APN-GCP. As a further step, cost-effectiveness and projected estimation of manpower of APN-GCP should be studied in the future.

• Journal of the Korean Geotechnical Society
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• v.32 no.9
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• pp.37-50
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• 2016

Gravel compaction pile (GCP) is widely used as it increases the bearing capacity of soft ground and reduces the consolidation settlement. Stress concentration ratio for GCP design is dependent on the area replacement, surcharge pressure and depth. However, a range of stress concentration ratio obtained through field, laboratory experiments and numerical analysis is large. Little study has been done on the stress concentration ratio for the mixing ratio of gravel and sand. The main objective of the study is to evaluate the stress concentration ratio for both area replacement ratio and mixing ratio through literature review and numerical analysis. Numerical analysis using the finite element program ABAQUS 6.12-4 has been performed for the composite ground with GCP. The excess pore water pressure and stress concentration ratio of composite ground have been analyzed for both the area replacement ratio and the mixing ratio. Based on the previous research results, a range of stress concentration ratio obtained from the field tests, laboratory tests, numerical analysis on the GCP studies is found to be 1.7-3.2, 2.0-7.5 and 2.0-6.5, respectively. Based on the numerical analysis results, as the area replacement ratio increases, the stress concentration ratio increases up to 30% and then decreases at 40%. Also, the stress concentration ratio tends to increase up to 70:30 and then to decrease after 60:40.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.770-772
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• 2003

In this paper, to correct the error, we propose a method to estimate a correction data based on observation data obtained from MUBEX campaign. Many heat spots are correspond to industrial area including steel plant, power plant and so on. Heat spot transmits some kinds of thin cloud because it emits large radiance, so that it is possible to extract GCP from the area under the thin cloud.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.220-223
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• 2004

Automatic image registration is an essential element of remote sensing because remote sensing system generates enormous amount of data, which are multiple observations of the same features at different times and by different sensor. The general process of automatic image registration includes three steps: 1) The extraction of features to be used in the matching process, 2) the feature matching strategy and accurate matching process, 3) the resampling of the data based on the correspondence computed from matched feature. For step 2) and 3), we have developed an algorithms for automated registration of satellite images with RANSAC(Random Sample Consensus) in success. However, for step 1), There still remains human operation to generate GCP Chips, which is time consuming, laborious and expensive process. The main idea of this research is that we are able to automatically generate GCP chips with comer detection algorithms without GPS survey and human interventions if we have systematic corrected satellite image within adaptable positional accuracy. In this research, we use SUSAN(Smallest Univalue Segment Assimilating Nucleus) algorithm in order to detect the comer. SUSAN algorithm is known as the best robust algorithms for comer detection in the field of compute vision. However, there are so many comers in high-resolution images so that we need to reduce the comer points from SUSAN algorithms to overcome redundancy. In experiment, we automatically generate GCP chips from IKONOS images with geo level using SUSAN algorithms. Then we extract reference coordinate from IKONOS images and DEM data and filter the comer points using texture analysis. At last, we apply automatically collected GCP chips by proposed method and the GCP by operator to in-house automatic precision correction algorithms. The compared result will be presented to show the GCP quality.

• Korean Journal of Remote Sensing
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• v.5 no.2
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• pp.133-145
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• 1989

This paper describes an implementation of linear feature extraction algorithms for satellite images and a method of automatic GCP(Ground Control Point) filing using the extracted linear feature. We propose a new linear feature extraction algorithm which uses magnitude and direction information of edges. The result of applying the proposed algorithm to satellite images are presented and compared with those of the other algorithms. By using the proposed algorithm, automatic GCP filing was successfully performed.

• Journal of the Korean Geosynthetics Society
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• v.15 no.3
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• pp.27-37
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• 2016

Although many studies on the Granular Compaction Pile (GCP) have been done by many researchers, the GCP design has not been systematically done due to the absence of the rational design methodology. As the GCP design has been mostly done by engineers' own experiences, some failure cases have been reported to occur. For this reason, it is very difficult to confirm definite causes of the failure and establish the prevention plans for the failure. Therefore, this study aims to investigate the optimal mixing ratio of gravel and sand, the effects of the internal friction angle of the GCP on the stress concentration ratio and the vertical and horizontal settlements. In order to analyze the behavior of the soft ground reinforced with the GCP depending on the different design parameters such as the stress concentration ratio and the internal friction angle, a number of finite element (FE) analyses were performed. From the direct shear test, the optimal mixing ratio of gravel to sand was found to be 70:30. Based on the numerical analyses, as the internal friction angle increased, the stress concentration ratio increased and it converged to a constant value. In addition, the larger the internal friction angle, the smaller the settlements. Consequently, the use of the optimal mixing ratio of gravel and sand can lead to reducing both the lateral flow and the heaving phenomenon.

• Journal of the Korean Geosynthetics Society
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• v.16 no.2
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• pp.35-45
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• 2017

Gravel compaction pile (GCP) is widely used as it increases the bearing capacity of soft ground and reduces the consolidation settlement. Stress concentration ratio for design is dependent on the area replacement, surcharge pressure, depth and penetration rate. However, a range of stress concentration ratio obtained through field, laboratory experiments and numerical analysis is large. But since the main objective of the study is to evaluate the stress concentration ratio and settlement for both area replacement ratio and penetration rate through numerical analysis. Numerical analysis using the finite element program ABAQUS 6.12-4 has been performed for the composite ground with GCP. As a result, the stress concentration ratio at the points except for the point of top is in the range of 1.21-5.36, 1.19-5.45, 2.16-5.60 for 60%, 80% and 100% penetration, respectively. In general, as the penetration rate and area replacement ratio increases, the stress concentration ratio tends to increase.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.61-69
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• 2003

There is various kinds cause that influence to created DEM and orthoimage using stereo satellite images. Specialty, research about effect that GCP number gives to accuracy of DEM, orthoimage and modeling may have to be gone ahead. Therefore, this research increases GCP number by 5 to 30 and created each modeling, DEM and orthoimage using SPOT panchromatic images that resolution is 10m by digital image processing method. Accuracy assessment did by orthoimage using 20 check point. As a result, GCP number between 10∼30 modeling RMSE is 1 pixel low appear. Horizontal·vertical error that use orthoimage looked tendency that decrease GCP number increases, and confirmed by the most economical in GCP number 10∼15. Also, analyze correlation of GCP number and orthoimage position accuracy and presented improvement plan and research task hereafter.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.6
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• pp.611-616
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• 2008

When extracting geo-referenced 3D data from cameras mounted on Mobile Mapping Systems, one of important properties for accuracy of extracted data is the alignment of the relative translation(lever-arm) and rotation(bore-sight) between the coordinate systems of Inertial Measurement Unit(IMU)/Ground Positioning System(GPS) and cameras. Since the conventional method calculates absolute camera orientation using ground control points (GCP), the alignment is determined in one Coordinated System (GPS Coordinated System). It basically require GCP. We proposed a mathematical model for the alignment using the initially uncoupled data of cameras and IMU/GPS without GCPs.