• Journal of Korean Dental Science
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• v.13 no.2
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• pp.52-58
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• 2020

Purpose: Using two types of electronic apex locators, this study aimed to investigate the differences in accuracy according to the evaluator and equipment. Materials and Methods: Artificial teeth of the lower first premolars and two mandibular acrylic models (A and B) were used in this study. In the artificial teeth, the pulp chamber was opened and the access cavity was prepared. Using calibrated digital Vernier calipers, the distance from the top of the cavity and the root apex was measured to assess the actual distance between two artificial teeth. The evaluation was conducted by 20 dentists, and each evaluator repeated measurements for each electronic apex locator five times. The difference between the actual distance from the top of the cavity to the root apex and the distance measured using electronic measuring equipment was compared. For statistical analysis, the Friedman test the Mann-Whitney U-test were conducted and the differences between groups were analyzed (α=0.05). Result: As for the accuracy of measurement according to the two types of electronic apex locators, the value of the measurement error was 0.4753 mm in Dentaport ZX and 0.3321 mm in E-Cube Plus. Moreover, electronic apex locators Dentaport ZX and E-Cube Plus showed statistically significant differences (P<0.05). As for the difference in the accuracy of the two types of electronic apex locators according to the evaluator, the resulting values differed depending on the evaluator and showed a statistically significant difference (P<0.001). Conclusion: Electronic apex locator E-Cube Plus showed higher accuracy than did Dentaport ZX. Nevertheless, both types of electronic apex locators showed 100% accuracy in finding the region within root apex ±0.5 mm zone. Furthermore, according to the evaluator, the two electronic apex locators showed different resulting values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1700-1709
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• 2019

Recently, many researches have been under way to develop systems (services) to support the safety navigation of ships, and in these studies, common difficulties have been encountered in assessing the usefulness and effectiveness of the developed system. To solve these problems, we propose the DEVS-based ship navigation modeling and simulation technique. Following the preceding study, we analyze the COLREG rules and reflected to officer and helmsman agent models for decision making. Also we propose estimation and interpolation techniques to adopt the motion characteristics of the actual vessel to simulation. In addition, we implement the navigation simulation system to reflect the designed proposed methods, and we present five-scenarios to verify the developed simulation system. And we conduct simulations according to each scenario and the results were reconstructed. The simulation results confirm that the components modelled in each scenario enable to operate according to the navigation relationships.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Journal of The Korean Astronomical Society
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• v.54 no.1
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• pp.9-16
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• 2021

The Ozone Dynamics Investigation Nano-Satellite (ODIN) is a CubeSat design proposed by Chungnam National University as contribution to the CubeSat Competition 2019 sponsored by the Korean Aerospace Research Institute (KARI). The main objectives of ODIN are (1) to observe the polar ozone column density (latitude range of 60° to 80° in both hemispheres) and (2) to investigate the chemical dynamics between stratospheric ozone and ozone depleting substances (ODSs) through spectroscopy of the terrestrial atmosphere. For the operation of ODIN, a highly efficient power system designed for the specific orbit is required. We present the conceptual structural design of ODIN and an analysis of power generation in a sun synchronous orbit (SSO) using two different configurations of 3U solar panels (a deployed model and a non-deployed model). The deployed solar panel model generates 189.7 W through one day which consists of 14 orbit cycles, while the non-deployed solar panel model generates 152.6 W. Both models generate enough power for ODIN and the calculation suggests that the deployed solar panel model can generate slightly more power than the non-deployed solar panel model in a single orbit cycle. We eventually selected the non-deployed solar panel model for our design because of its robustness against vibration during the launch sequence and the capability of stable power generation through a whole day cycle.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.441-461
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• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.533-539
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• 2020

Real-time service (RTS) provided by IGS provides correction for GNSS orbit and clock via internet, so it is widely used in fields that require real-time precise positioning. However, the RTS signal may be lost due to an unstable Internet environment. When signal disconnection occurs, signal prediction can be performed using polynomial models. However, the RTS changes rapidly after the GNSS navigation message issue of data (IOD) changes, so it is difficult to predict when signal loss occurs at that point. In this study, we proposed an algorithm to generate continuous RTS correction information by applying the difference in navigation trajectory according to IOD change. The use of this algorithm can improve the accuracy of RTS prediction at IOD changes. After performing optimization studies to improve RTS prediction performance, the predicted RTS trajectory information was applied to precision positioning (PPP). Compared to the conventional method, the position error is significantly reduced, and the error increase along with the signal loss interval increase is reduced.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.352-367
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• 2021

Purpose: Minimally invasive gastrectomy is a promising surgical method with well-known benefits, including reduced postoperative complications. However, for total gastrectomy of gastric cancers, this approach does not significantly reduce the risk of complications. Therefore, we aimed to evaluate the incidence and risk factors for the severity of complications associated with minimally invasive total gastrectomy for gastric cancer. Materials and Methods: The study included 392 consecutive patients with gastric cancer who underwent either laparoscopic or robotic total gastrectomy between 2011 and 2019. Clinicopathological and operative characteristics were assessed to determine the features related to postoperative complications after minimally invasive total gastrectomy. Binomial and multinomial logistic regression models were used to identify the risk factors for overall complications and mild and severe complications, respectively. Results: Of 103 (26.3%) patients experiencing complications, 66 (16.8%) and 37 (9.4%) developed mild and severe complications, respectively. On multivariate multinomial regression analysis, independent predictors of severe complications included obesity (OR, 2.56; 95% CI, 1.02-6.43; P=0.046), advanced stage (OR, 2.90; 95% CI, 1.13-7.43; P=0.026), and more intraoperative bleeding (OR, 1.04; 95% CI, 1.02-1.06; P=0.001). Operation time was the only independent risk factor for mild complications (OR, 1.06; 95% CI, 1.001-1.13; P=0.047). Conclusions: The risk factors for mild and severe complications were associated with surgery, indicating surgical difficulty. Surgeons should be aware of these potential risks that are related to the severity of complications so as to reduce surgery-related complications after minimally invasive total gastrectomy for gastric cancer.

• Journal of Ginseng Research
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• v.46 no.3
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• pp.367-375
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• 2022

Background: Short-term hydroponic-cultured ginseng (sHCG), which is 1-year-old ginseng seedlings cultivated for 4 weeks in a hydroponic system, is a functional food item with several biological effects. However, the optimal extraction conditions for sHCG, and the bioactivity of its extracts, have not been evaluated. Methods: Chlorogenic acid (CGA) and ginsenoside contents were evaluated in sHCG, white ginseng (WG), and red ginseng (RG) using high-performance liquid chromatography. Response surface methodology (RSM) was used to optimize the extraction conditions (temperature and ethanol concentration) to maximize the yield of dry matter, CGA, and four ginsenosides (Re, Rg₁, Rb₁, and Rd) from sHCG. The optimal extraction conditions were applied to pilot-scale production of sHCG extracts. The expression levels of tumor necrosis factor (TNF)-α/interferon (IFN)-γ-induced thymic and activation-regulated chemokines (TARC/CCL17) were measured after treatment with sHCG, WG, and RG extracts, and the effects of their bioactive compounds (CGA and four ginsenosides) on human skin keratinocytes (HaCaTs) were evaluated. Results: CGA and four ginsenosides, which are bioactive compounds of sHCG, significantly inhibited TNF-α/IFN-γ-induced TARC/CCL17 expression. The optimal sHCG extraction conditions predicted by the RSM models were 80 ℃ and 60% ethanol (v/v). The sHCG extracts produced at the pilot scale under optimal conditions greatly alleviated TNF-α/IFN-γ-induced TARC/CCL17 production compared with WG and RG extracts. Conclusions: Pesticide-free sHCG extracts, which contain high levels of CGA and the ginsenosides Re, Rg₁, Rb₁, and Rd as bioactive compounds, may have therapeutic potential for atopic diseases.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• The Journal of Advanced Prosthodontics
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• v.14 no.5
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• pp.294-304
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• 2022

PURPOSE. The purpose of this study was to evaluate the influence of different palatal vault configurations on the accuracy and scan speed of intraoral scans (IO) of completely edentulous arches. MATERIALS AND METHODS. Three different virtual models of a completely edentulous maxillary arch with different palatal vault heights- Cl I moderate (U-shaped), Cl II deep (steep) and Cl III shallow (flat)-were digitally designed using CAD software (Meshmixer; Autodesk, USA) and 3D-printed using SLA-based 3D-printer (XFAB; DWS, Italy) (n = 30; 10 specimens per group). Each model was scanned using intraoral scanner (Trios 3; 3Shape^TM, Denmark). Scanning time was recorded for all samples. Scanning accuracy (trueness and precision) were evaluated using digital subtraction technique using Geomagic Control X v2020 (Geomagic; 3DSystems, USA). One-way analysis of variance (ANOVA) test was used to detect differences in scanning time, trueness and precision among the test groups. Statistical significance was set at α = .05. RESULTS. The scan process could not be completed for Class II group and manufacturer's recommended technique had to be modified. ANOVA revealed no statistically significant difference in trueness and precision values among the test groups (P=.959 and P=.658, respectively). Deep palatal vault (Cl II) showed significantly longer scan time compared to Cl I and III. CONCLUSION. The selection of scan protocol in complex cases such as deep palatal vault is of utmost importance. The modified, adopted longer path scan protocol of deep vault cases resulted in increased scan time when compared to the other two groups.