• The Journal of Advanced Prosthodontics
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• v.10 no.4
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• pp.265-270
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• 2018

PURPOSE. The purpose of this in vitro study was to evaluate the accuracy of a new implant impression technique using bite impression coping and a dual arch tray. MATERIALS AND METHODS. Two implant fixtures were placed on maxillary left second premolar and first molar area in dentoform model. The model with two fixtures was used as the reference. The impression was divided into 2 groups, n=10 each. In group 1, heavy/light body silicone impression was made with pick up impression copings and open tray. In group 2, putty/light body silicone impression was made with bite impression copings and dual arch tray. The reference model and the master casts with implant scan bodies were scanned by a laboratory scanner. Surface tessellation language (STL) datasets from test groups was superimposed with STL dataset of reference model using inspection software. The three-dimensional deviation between the reference model and impression models was calculated and illustrated as a color-map. Data was analyzed by independent samples T-test of variance at ${\alpha}=.05$. RESULTS. The mean 3D implant deviations of pick up impression group (group 1) and dual arch impression group (group 2) were 0.029 mm and 0.034 mm, respectively. The difference in 3D deviations between groups 1 and 2 was not statistically significant (P=.075). CONCLUSION. Within limitations of this study, the accuracy of implant impression using a bite impression coping and dual arch tray is comparable to that of conventional pick-up impression.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.843-852
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• 2019

In the present study, we established a comprehensive dataset of dose coefficients (DCs) of the new meshtype ICRP reference computational phantoms (MRCPs) for idealized external exposures of photons and electrons with the Geant4 code. Subsequently, the DCs for the nine organs/tissues, calculated for their thin radiosensitive target regions, were compared with the values calculated by averaging the absorbed doses over the entire organ/tissue regions to observe the influence of the thin sensitive regions on dose calculations. The result showed that the influences for both photons and electrons were generally insignificant for the majority of organs/tissues, but very large for the skin and eye lens, especially for electrons. Furthermore, the large influence for the skin eventually affected the effective dose calculations for electrons. The DCs of the MRCPs also were compared with the current ICRP-116 values produced with the current ICRP-110 reference phantoms. The result showed that the DCs for the majority of organs/ tissues and effective dose were generally similar to the ICRP-116 values for photons, except for very low energies; however, for electrons, significant differences from the ICRP-116 values were found in the DCs, particularly for superficial organs/tissues and skeletal tissues, and also for effective dose.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.1949-1958
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• 2023

The International Commission on Radiological Protection (ICRP) Publication 116 was released to provide a comprehensive dataset of the dose coefficients (DCs) for external exposures produced with the adult reference voxel phantoms of ICRP Publication 110. Although an advanced skeletal dosimetry method for photons and neutrons using fluence-to-dose response functions (DRFs) was introduced in ICRP Publication 116, the ICRP-116 skeletal DCs were calculated by using the simple method conventionally used (i.e., doses to red bone marrow and endosteum approximated by doses to spongiosa and/or medullary cavities). In the present study, the photon and neutron DRFs were used to produce skeletal DCs of the ICRP-110 reference phantoms, which were then compared with the ICRP-116 DCs. For photons, there were significant differences by up to ~2.8 times especially at energies <0.3 MeV. For neutrons, the differences were generally small over the entire energy region (mostly <20%). The general impact of the DRF-based skeletal DCs on the effective dose calculations was negligibly small, supporting the validity of the ICRP-116 effective DCs despite their skeletal DCs derived from the simple method. Meanwhile, we believe that the DRF-based skeletal DCs could be beneficial in better estimates of skeletal doses of individuals for risk assessments.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.22-35
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• 2011

Incheon Bay has a suitable condition for tidal power generation due to the high tidal range by topographical effect. Therefore a study on the technology development for tidal energy utilization has been promoted since 2006. It is needed to deduce optimal alternatives to determine the suitable location of facilities for tidal power generation and to reduce the environmental damage from development. In order to carry out efficiently this mission, spatial information system is essential to manage and use various spacial elements related to the development and conservation. In this study, for the development of tidal energy, spatial data could be defined as three kinds of dataset. Fundamental dataset is defined as spatial data such as tide, tidal current, wave, erosion and sedimentation. Framework dataset is composed of topographical map, facility map and bathymetry. The reference dataset is composed of marine ecology and environment having the characteristics of thematic map. This study is mainly aimed at establishing methodology of conceptual spatial data modeling classifying as essential data model and optional data model through the definition of the components of spatial data.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.23-35
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• 2023

Korea Augmentation Satellite System (KASS) is a satellite-based augmentation system (SBAS) that provides approach procedure with vertical guidance-I (APV-I) level corrections and integrity information to Korea territory. KASS is used to monitor navigation performance in real-time, and this paper introduces the design, implementation, and verification process of mission monitoring (MIMO) in KASS. MIMO was developed in compliance with the Minimum Operational Performance Standards of the Radio Technical Commission for Aeronautics for Global Positioning System (GPS)/SBAS airborne equipment. In this study, the MIMO system was verified by comparing and analyzing the outputs of reference tools. Additionally, the definition and derivation method of accuracy, integrity, continuity, and availability subject to MIMO were examined. The internal and external interfaces and functions were then designed and implemented. The GPS data pre-processing was minimized during the implementation to evaluate the navigation performance experienced by general users. Subsequently, tests and verification methods were used to compare the obtained results based on reference tools. The test was performed using the KASS dataset, which included GPS and SBAS observations. The decoding performance of the developed MIMO was identical to that of the reference tools. Additionally, the navigation performance was verified by confirming the similarity in trends. As MIMO is a component of KASS used for real-time monitoring of the navigation performance of SBAS, the KASS operator can identify whether an abnormality exists in the navigation performance in real-time. Moreover, the preliminary identification of the abnormal point during the post-processing of data can improve operational efficiency.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.39-47
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• 2003

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So, it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor by Berger and Pericchi (1996) and the fractional Bayes factor by O'Hagan (1995) have been used to overcome this problems. This paper suggests intrinsic priors for testing the equality of two lognormal means, whose Bayes factors are asymptotically equivalent to the corresponding fractional Bayes factors. Using proposed intrinsic priors, we demonstrate our results with a simulated dataset.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.661-671
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• 2003

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So, it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor by Berger and Pericchi (1996) and the fractional Bayes factor by O'Hagan (1995) have been used to overcome this problems. This paper suggests intrinsic priors for testing the equality of two lognormal means, whose Bayes factors are asymptotically equivalent to the corresponding fractional Bayes factors. Using proposed intrinsic priors, we demonstrate our results with real example and a simulated dataset.