• 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 Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• Korean journal of applied entomology
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• v.61 no.2
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• pp.369-376
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• 2022

Five different methanol extracts of plants were evaluated as a mosquito repellent against Aedes albopictus in nude mice model. The volatile components of plants were analyzed with GC-MS. The effective dose was the highest in Nepeta cataria with 72.9, 83.7, 86.4, and 97.3% efficiencies at 10, 50, 100, and 200 ㎍/mL, respectively. Nepeta cataria, Actinidia polygama, and Artemisia annua extracts were the most effective in duration time test. And there was no difference compared to negative control in the low mosquito repellent efficiency from 30 minutes. As a result of analyzing the volatile components of each plant, a total of 28 components in Mentha suaveolens, 19 components in Actinidia polygama, 27 components in Artemisia annua, 26 components in Nepeta cataria, and 19 components in Taraxacum platycarpum were detected. Especially, nepetalactone known as an effective ingredient of a mosquito repellent was identified (27.95 mg/Kg) in one of volatile components of Nepeta cataria. Overall, our results provide the possibility to develop mosquito repellents material using natural plants which contain various volatile components.

• Atmosphere
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• v.31 no.5
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• pp.625-635
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• 2021

South Korea established the 2050 Carbon Neutral Plan in response to the climate crisis, and to achieve this policy, it is very important to monitor domestic carbon emissions and atmospheric carbon concentration. Both CO₂ and CO are emitted from fossil fuel combustion processes, but the relative ratios depend on the combustion efficiency and the strength of local emission regulations. In this study, the relationship between CO₂ and CO was analyzed using ground observation data for the period of 2018~2020 at Anmyeon-do site and the CO/CO₂ ratio according to regional origin during high CO₂ cases was investigated based on the footprint simulated from Stochastic Time-Inverted Lagrangian Transport (STILT) model. CO₂ and CO showed a positive correlation with correlation coefficient of 0.66 (p < 0.01), and averaged footprints during high CO₂ cases confirmed that air particles mainly originated from eastern and north-eastern China, and inland of Korean Peninsula. In addition, it was revealed that among the cases of high CO₂ concentration, when the CO/CO₂ ratio is high, the industrial area of eastern China is greatly affected, and when the ratio is low, the contribution of the domestic region is relatively high. The ratio of CO₂ and CO in this study is significant in that it can be used as a useful factor in determining the possibility of domestic and foreign origins of climate pollutants.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.57-65
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• 2022

This study simulated the shock wave propagation through the tamping material between explosives and hole wall at blasting works and verified the effect of tamping materials. The Arbitrary Lagrangian-Eulerian(ALE) method was selected to model the mixture of solid (Lagrangian) and fluid (Eulerian). The time series analysis was carried out during blasting process time. Explosives and tamping materials (air or water) were modeled with finite element mesh and the hole wall was assumed as a rigid body that can determine the propagation velocity and shock force hitting the hole wall from starting point (explosives). The numerical simulation results show that the propagation velocity and shock force in case of water were larger than those in case of air. In addition, the real site at blasting work was modeled and simulated. The rock was treated as elasto-plastic material. The results demonstrate that the instantaneous shock force was larger and the demolished block size was smaller in water than in air. On the contrary, the impact in the back side of explosives hole was smaller in water, because considerable amount of shock energy was used to demolish the rock, but the propagation of compression through solid becomes smaller due to the damping effect by rock demolition. Therefore, It can be proven that the water as the tamping media was more profitable than air.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.1
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• pp.33-43
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• 2023

Pathological wear across the entire dentition causes problems such as collapsed occlusal plane, reduced vertical dimension, anterior premature contact, inadequate anterior guidance, and tooth migration, thereby induce symptoms such as temporomandibular joint disorder, reduced masticatory efficiency, and tooth hypersensitivity. For the treatment of patients with excessive wear, evaluation of vertical dimension should be preceded along with analysis of the cause. The patient in this case was a 45-year-old female with a history of orthognathic surgery. Through clinical examination, radiographic examination, and model analysis, overall tooth wear, interdental spacing in the anterior maxillary region, retruded condylar position, and insufficient interocclusal space for prosthetic restoration were confirmed. Full mouth rehabilitation with increased vertical dimension was planned, the patient's adaptation to the new vertical dimension was evaluated with a removable occlusal splint and temporary prosthesis, and cross-mounting was performed based on the temporary restoration to fabricate the definitive zirconia prosthesis, maintaining the adjusted vertical dimension. It showed satisfactory functional and esthetic results through stable restoration of the occlusal relationship.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.587-599
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• 2023

As climate change increasesthe frequency and risk of flooding in major cities around theworld, the importance ofsimulation technology that can quickly and accurately analyze high-resolution 2D flooding information in large-scale areasis emerging. The physically-based approaches based on the Shallow Water Equations (SWE) often requires huge computer resources hindering high-resolution flood prediction. This study investigated the theoretical background of Weighted Cellular Automata 2D (WCA2D), which simulates spatio-temporal changes offlooding using transition rules and weight-based system, and assessed feasibility to simulate pluvial flooding in the urbancatchment, theOncheon-cheon catchmentinBusan, SouthKorea.Inaddition,the computation performancewas compared by applying versions using OpenComputing Language (OpenCL) andOpenMulti-Processing (OpenMP) parallel computing techniques. Simulationresultsshowed that the maximuminundation depthmap by theWCA2Dmodel cansimilarly reproduce historical inundation maps. Also, it can precisely simulate spatio-temporal changes of flooding extent in the urban catchment with complex topographic characteristics. For computation efficiency, parallel computing schemes, theOpenCLandOpenMP, improved the computation by about 8~14 and 5~6 folds respectively, compared to the sequential computation.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.