• Smart Media Journal
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• v.12 no.9
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• pp.95-102
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• 2023

As the utilization value of personal information increases, discussions on how to provide personal information are active, but information required by institutions to utilize personal information is being exposed more than necessary. Therefore, personal privacy protection is essential to overcome the problems and limitations of personal information protection. In this study, a decentralized identity information management model that overcomes the problems and limitations of the centralized identity management method of personal information and manages and selectively provides personal information by the information owner himself and demonstrates the excellence of personal information by implementing the Smart Personal Information Provision System (SPIPS) in the PBFT consensus algorithm through experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.47-52
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• 2024

Through the coronavirus pandemic, research on the use and advancement of blockchain-based decentralized identity authentication (Decentralized ID) technology is being actively conducted in various fields, centered on the central government, local governments, and private businesses. In this paper, we introduce the results of development based on Hyperledger Fabric to change the existing central server-based identity authentication to a decentralized one. These development results can strengthen the security and transparency of identity authentication systems for commercial purposes and provide stable services for user ID issuance, inquiry, and disposal. In addition, the decentralized identity authentication system verified performance results of DID creation of 262,000 rps and DID inquiry of 1,850 rps, DID VP creation of 200 rps, and DID VP inquiry of 220 rps or less through public authentication.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.439-453
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• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2492-2498
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• 2013

In this paper, A lane tracking algoritm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Using clustering and lane similarity function with noise suppression features are extracted. Finally, lane model is calculated using RANSAC and lane model is tracked using Kalman Filter. Experimental results show that the proposed algorithm can be processed within 20ms and its detection rate approximately 90% on the highway in a variety of environments.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.20 no.1
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• pp.34-40
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• 2017

Purpose: This study was performed to review the outcomes of gastrostomy insertion in children at our institute during 10 years. Methods: A retrospective chart review was performed on 236 patients who underwent gastrostomy insertion from October 2005 to March 2015. We used our algorithm to select the least invasive method for gastrostomy insertion for each patient. Long-term follow-up was performed to analyze complications related to the method of gastrostomy insertion. Results: Out of 236 patients, 120 underwent endoscopic gastrostomy, 79 had laparoscopic gastrostomy, and 37 had open gastrostomy procedures. The total major complication rates for endoscopic gastrostomy insertion, laparoscopic gastrostomy insertion, and open gastrostomy were 9.2%, 8.9%, and 8.1%, respectively. The most common major complication was gastroesophageal reflux requiring Nissen fundoplication (3.8%), and other complications included peritonitis (1.3%), hiatal hernia (1.3%), and bowel perforation (0.8%). Gastrostomy removal was successful in 8.6% and 5.0% of patients in the endoscopic and surgical gastrostomy groups, respectively. Gastrocutaneous fistula occurred in 60% of surgically inserted cases, requiring a second operation. Conclusion: This retrospective study was performed to review the outcome of gastrostomy insertion, as well as to introduce an algorithm that can be used for future cases. Further studies should be conducted to make a consensus on choosing the most appropriate method for gastrostomy insertion.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.345-353
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• 2010

A general solution in global self-position location of robot is to generate multiple hypothesis in self-position of robot, which is to look for the most positive self-position by evaluating each hypothesis based on features of observed landmark. Markov Localization(ML) or Monte Carlo Localization(MCL) to be the existing typical method is to evaluate all pairs of landmark features and generated hypotheses, it can be said to be an optimal method in sufficiently calculating resources. But calculating quantities was proportional to the number of pairs to evaluate in general, so calculating quantities was piled up in wide environments in the presence of multiple pairs if using these methods. First of all, the positive and promising pairs is located and evaluated to solve this problem in this paper, and the newly locating method to make effective use of calculating time is proposed. As the basic method, it is used both RANSAC(RANdom SAmple Consensus) algorithm and preemption scheme to be efficiency method of RANSAC algorithm. The calculating quantity on each observation of robot can be suppressed below a certain values in the proposed method, and the high location performance can be determined by an experimental on verification.

• Korean Journal of Clinical Pharmacy
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• v.33 no.3
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• pp.153-167
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• 2023

Objective: Bipolar disorder displays a spectrum of manifestations, including manic, hypomanic, depressive, mixed, psychotic, and atypical episodes, contributing to its chronic nature and association with heightened suicide risk. Creating effective pharmacotherapy guidelines is crucial for managing bipolar disorder and reducing its prevalence. Treatment algorithms grounded in science have improved symptom management, but variations in recommended medications arise from research differences, healthcare policies, and cultural nuances globally. Methods: This study compares Korea's bipolar disorder treatment algorithm with guidelines from the UK, Australia, and an international association. The aim is to uncover disparities in key recommended medications and their underlying factors. Differences in CYP450 genotypes affecting drug metabolism contribute to distinct recommended medications. Variances also stem from diverse guideline development approaches-expert consensus versus metaanalysis results-forming the primary differences between Korea and other countries. Results: Discrepancies remain in international guidelines relying on meta-analyses due to timing and utilized studies. Drug approval speeds further impact medication selection. However, limited high-quality research results are the main cause of guideline variations, hampering consistent treatment conclusions. Conclusion: Korea's unique Delphi-based treatment algorithm stands out. To improve evidence-based recommendations, large-scale studies assessing bipolar disorder treatments for the Korean population are necessary. This foundation will ensure future recommendations are rooted in scientific evidence.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1279-1288
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• 2021

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3-80.0% and a specificity of 78.9-89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7-79.0% and a specificity of 93.3-100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66-0.96). Conclusion: The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.