• The Korea Journal of Herbology
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• v.28 no.6
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• pp.47-51
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• 2013

Objectives : The Illicii Veri Fructus was not only traditional medicine but also food in Asia. The aim of this study was selection of optimum solvent in the fruit of Illicii Veri Fructus because an appropriate solvent affect a medicinal effect. Methods : Illicii Veri Fructus was carried out ultrasonic-assisted extraction as various solvents. Two main compounds, p-anisaldehyde and anethole, were successfully analyzed by high performance liquid chromatography-photodiode array detector (HPLC-PDA) and carried out method validation according to ICH guideline. The optimum solvent selected by comparing with yields of two main ingredients. Results : The p-anisaldehyde and anethole were detected at approximately 8.0 min and 19.8 min, respectively. It was all below 5.0% that RSD of retention time and peak area for two main peaks. Calibration curves of two compounds were good linearity as $R^2$ >0.9999. All of the precisions and accuracy were good intra-day and inter-day as below 5.0% RSD. Limited of detection (LOD) of p-anisaldehyde and anethole were analyzed as $0.134{\mu}g/mL$ and $4.286{\mu}g$, respectively. Limited of quantification (LOQ) of two compounds were $0.407{\mu}g$ and $12.989{\mu}g$, respectively. As a result of this study, p-anisladehyde was detected as 0.209 ~ 0.467%, however anethole was not detected in the distilled water. Conclusions : Anethole was main component as 5.329 ~ 6.815% except for water extraction. Methanol extraction among various solvents was detected the highest contents of p-anisaldehyde and anethole as 0.467(${\pm}0.008$)% and 6.815(${\pm}0.220$)%, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Wind and Structures
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• v.34 no.1
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• pp.127-136
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• 2022

In this work a multi-fidelity non-intrusive polynomial chaos (MF-NIPC) has been applied to a structural wind engineering problem in architectural design for the first time. In architectural design it is important to design structures that are safe in a range of wind directions and speeds. For this reason, the computational models used to design buildings and bridges must account for the uncertainties associated with the interaction between the structure and wind. In order to use the numerical simulations for the design, the numerical models must be validated by experi-mental data, and uncertainties contained in the experiments should also be taken into account. Uncertainty Quantifi-cation has been increasingly used for CFD simulations to consider such uncertainties. Typically, CFD simulations are computationally expensive, motivating the increased interest in multi-fidelity methods due to their ability to lev-erage limited data sets of high-fidelity data with evaluations of more computationally inexpensive models. Previous-ly, the multi-fidelity framework has been applied to CFD simulations for the purposes of optimization, rather than for the statistical assessment of candidate design. In this paper MF-NIPC method is applied to flow around a rectan-gular 5:1 cylinder, which has been thoroughly investigated for architectural design. The purpose of UQ is validation of numerical simulation results with experimental data, therefore the radius of curvature of the rectangular cylinder corners and the angle of attack are considered to be random variables, which are known to contain uncertainties when wind tunnel tests are carried out. Computational Fluid Dynamics (CFD) simulations are solved by a solver that employs the Finite Element Method (FEM) for two turbulence modeling approaches of the incompressible Navier-Stokes equations: Unsteady Reynolds Averaged Navier Stokes (URANS) and the Large Eddy simulation (LES). The results of the uncertainty analysis with CFD are compared to experimental data in terms of time-averaged pressure coefficients and bulk parameters. In addition, the accuracy and efficiency of the multi-fidelity framework is demonstrated through a comparison with the results of the high-fidelity model.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.889-898
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• 2022

In this paper, parallel computing method on the three-dimensional electromagnetic field is proposed. The present electromagnetic scattering analysis is conducted based on the time-harmonic vector wave equation and the finite element method. The edge-based element and 2^nd -order absorbing boundary condition are used. Parallelization of the elemental numerical integration and the matrix assemblage is accomplished by allocating the partitioned finite element subdomain for each processor. The graph partitioning library, METIS, is employed for the subdomain generation. The large sparse matrix computation is conducted by MUMPS, which is the parallel computing library based on the multi-frontal method. The accuracy of the present program is validated by the comparison against the Mie-series analytical solution and the results by ANSYS HFSS. In addition, the scalability is verified by measuring the speed-up in terms of the number of processors used. The present electromagnetic scattering analysis is performed for a perfect electric conductor sphere, isotropic/anisotropic dielectric sphere, and the missile configuration. The algorithm of the present program will be applied to the finite element and tearing method, aiming for the further extended parallel computing performance.

• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• Journal of The Korean Society of Clinical Toxicology
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• v.20 no.1
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• pp.1-7
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• 2022

Purpose: In patients with acute drug overdose, identification of drugs ingested is crucial to make a precise diagnosis. In most cases, the diagnoses are made on the medical history and physical examination findings. This study was undertaken to determine the concordance of diagnosis made on the basis of patient history by comparing it with urine toxicology analysis. Methods: This was a retrospective study of drug intoxicated patients over 18 years old who presented to the emergency center from 2017 to 2019. Specimens from urine were tested using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-TMS). The test results were compared with information obtained from patients. Diagnostic concordances for drug detection in intoxicated patients were calculated. Logistic regression analysis was used to examine the association between clinical characteristics and diagnostic discrepancy. Results: Totally, 370 patients were included in the analysis. Overall, 66 types of drugs were detected by UPLC-TMS. The drugs detected most frequently were zolpidem (104, 27.8%), citalopram (70, 18.7%), and paracetamol (66, 17.6%). The mean diagnostic concordance of patients was 52.7%. There were statistically significant diagnostic discrepancies in patients with underlying depression and patients intoxicated with multiple types of drugs. Conclusion: In ED patients with acute drug overdose, the diagnoses made on history alone were often inaccurate. It is essential to perform urine toxicology tests such as UPLC-TMS as a confirmatory instrument to improve accuracy in evaluating patients with drug intoxication.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Journal of Gastric Cancer
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• v.23 no.2
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• pp.264-274
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• 2023

Purpose: In this study, polymerase chain reaction (PCR)-based microsatellite instability (MSI) testing was comprehensively analyzed and compared with immunohistochemistry (IHC) for mismatch repair (MMR) protein expression in patients with gastric cancer (GC). Materials and Methods: In 5,676 GC cases, PCR-based MSI testing using five microsatellites (BAT-26, BAT-25, D5S346, D2S123, and D17S250) and IHC for MLH1 were performed. Reevaluation of MSI testing/MLH1 IHC and additional IHC for MSH2, MSH6, and PMS2 were performed in discordant/indeterminate cases. Results: Of the 5,676 cases, microsatellite stable (MSS)/MSI-low and intact MLH1 were observed in 5,082 cases (89.5%), whereas MSI-high (MSI-H) and loss of MLH1 expression were observed in 502 cases (8.8%). We re-evaluated the remaining 92 cases (1.6%) with a discordant/indeterminate status. Re-evaluation showed 1) 37 concordant cases (0.7%) (18 and 19 cases of MSI-H/MMR-deficient (dMMR) and MSS/MMR-proficient (pMMR), respectively), 2) 6 discordant cases (0.1%) (3 cases each of MSI-H/pMMR and MSS/dMMR), 3) 14 MSI indeterminate cases (0.2%) (1 case of dMMR and 13 cases of pMMR), and 4) 35 IHC indeterminate cases (0.6%) (22 and 13 cases of MSI-H and MSS, respectively). Finally, MSI-H or dMMR was observed in 549 cases (9.7%), of which 47 (0.8%) were additionally confirmed as MSI-H or dMMR by reevaluation. Sensitivity was 99.3% for MSI testing and 95.4% for MMR IHC. Conclusions: Considering the low incidence of MSI-H or dMMR, discordant/indeterminate results were occasionally identified in GCs, in which case complementary testing is required. These findings could help improve the accuracy of MSI/MMR testing in daily practice.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.65-85
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• 2022

In this study, in order to derive the priority of indicators and sustainability areas of smart ports, which means ports in the digital era, previous studies and ESG, which have recently been indispensably introduced in all industries worldwide, were studied together. A hierarchical structure was established with upper evaluation items and 20 lower evaluation items in four areas (operational, environmental, social, and governance), and a relative evaluation method of weighting items among the AHP techniques was applied. The pairwise comparison questionnaire consisted of a 9-point scale proposed by Satty (1980). A survey was conducted targeting working-level workers who perform sustainability or ESG(Environmental, Social, Governance)-related work at four representative port authorities in Korea (Busan, Incheon, Ulsan, Yeosu Gwangyang). In order to increase the accuracy of the analysis results, AHP analysis was conducted on 17 questionnaires with a consistency ratio of 0.1 or less. As a result of the analysis, it was confirmed that among the four areas representing the sustainability of domestic smart ports, the operation area had the highest priority, followed by the environment area. In addition, looking at the overall priorities for the 20 detailed indicators, indicators such as operational efficiency, operational planning, energy management, and pollution measurement and management system were found to have high priority. On the other hand, it was confirmed that the social and the governance areas had relatively low importance compared to other areas.