• Journal of the Korean GEO-environmental Society
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• v.23 no.3
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• pp.15-22
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• 2022

This study aims to observe the swelling representative rocks in Korea and to suggest improvements in the use of test methods and prior analysis in relation to the weathering of rocks. The swelling test and analysis were performed on the drilling cores obtained for the ground investigation at the domestic highway construction site. For the method of determining the absorption expansion index of rocks, the method proposed in "Standard Methods for Sample Collection and Specimen Preparation" of ISRM and Korean Rock Engineers Standard Rock Test Method was used. The specimen for the measurement of the expansion displacement was cylindrical with a height of 10 cm and a diameter of 5 cm. The existing swelling analysis method evaluates the sensitivity to weathering by using the maximum expansion displacement, but since the classification by bedrock grade is unclear, it is reasonable to use the rate of change of the expansion displacement according to the immersion time. It is necessary to conduct an experiment to distinguish between weathering and fault deterioration. In addition, long-term weathering prediction technology for each cancer type is needed through the expansion displacement analysis of the chemical weathering stage.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2A
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• pp.93-102
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• 2010

The numerical analysis and safety assessment by construction stages were considered the essential examination particular in order to solving the unstability of long-span bridges in the middle a construction. When estimating structural response characteristics by the construction stage analysis of long-span bridges, the influence of the near-field ground motion (NFGM) would be evaluated as a critical factor for the seismic design because it indicates clearly different aspects from the existing input earthquake motion data. Therefore, this study re-examined the response aspect of long-span bridges considering NFGM characteristics based on the response spectrum result, and advanced the presented numerical analysis program by the related research for conducting the construction stage analysis and reliability assessment of long-span bridges efficiently. The excellency of various construction schemes was assessed using the time history analysis result of critical member considering NFGM characteristics. For evaluating quantitative safety level, the reliability analysis was conducted considering the influence of external uncertainties included in random variables, and presented the safety index and failure probability of the critical construction stage by NFGM characteristics. In addition, the reliability result was examined the influence of internal uncertainties using monte carlo simulation (MCS), and assessed the distribution aspect of the essential analysis result. It is expected that this study will provide the basic information for the construction safety improvement when performing seismic design of long-span bridges considering NFGM characteristics.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.488-502
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• 2023

Ecosystem service accounting must measure ecosystem supply functions, demand, and the actual service flows that occur between them. In order to measure flows, supply and demand relationships must be defined, and a methodology that can objectify complex connections is needed. Although various studies on ecosystem services have been conducted in Korea, but researches on accounting for ecosystem services are not enough. The purpose of this study is to evaluate flood control ecosystem services by applying the EU methodology studied in the Experimental Ecosystem Account (EEA) of System of Environmental Economy Account (SEEA) and explore ways to introduce ecosystem account. To conduct the study, the ecosystem's runoff retention potential, social and economic demand for flood control, and actual service benefit flows formed from the relationships between them were modeled and quantified on a spatial basis. As a result of calculating the actual flow of flood control ecosystem services, the total domestic service amount was calculated to be 165,595 (ha), and it was confirmed that much of it was concentrated in agricultural land. In order to account for domestic flood control services in the future, key spatial data such as land cover maps must be continuously established and managed, and researches on input data and methodologies applicable to various spatial scopes such as national, regional, and unit watersheds are expected to be necessary.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.27-35
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• 2024

Fuel gases such as methane and propane are used in explosion hazardous area of domestic plants and can form non-uniform mixtures with the influence of process conditions due to leakage. The fire-explosion risk assessment using literature data measured under uniform mixtures, damage prediction can be obtained the different results from actual explosion accidents by gas leaks. An explosion characteristics such as explosion pressure and flame velocity of non-uniform gas mixtures with concentration change similar to that of facility leak were examined. The experiments were conducted in a closed 0.82 m long stainless steel duct with observation recorded by color high speed camera and piezo pressure sensor. Also we proposed the quantification method of non-uniform mixtures from a regression analysis model on the change of concentration difference with time in explosion duct. For the non-uniform condition of this study, the area of flame surface enlarged with increasing the concentration non-uniform in the flame propagation of methane and was similar to the wrinkled flame structure existing in a turbulent flame. The time to peak pressure of methane decreased as the non-uniform increased and the explosion pressure increased with increasing the non-uniform. The ranges of K_G (Deflagration index) of methane with the concentration non-uniform were 1.30 to 1.58 [MPa·m/s] and the increase rate of K_G was 17.7% in methane with changing from uniform to non-uniform.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.97-108
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• 2024

This research delves into the escalating concerns of accidents and fatalities in the construction industry over the recent five-year period, focusing on the development of a Safety Perception Model to augment safety measures. Given the rising percentage of elderly workers and the concurrent drop in productivity within the sector, there is a pronounced need for leveraging Fourth Industrial Revolution technologies to bolster safety protocols. The study comprises an in-depth analysis of statistical data regarding construction-related fatalities, aiming to shed light on prevailing safety challenges. Central to this investigation is the formulation of a Safety Perception Model tailored for small-scale construction projects. This model facilitates the quantification of safety risks by evaluating safety grades across construction sites. Utilizing the DWM1000 module, among an array of wireless communication technologies, the model enables the real-time tracking of worker locations and the assessment of safety levels on-site. Furthermore, the deployment of a safety management system allows for the evaluation of risk levels associated with individual workers. Aggregating these data points, the Safety Climate Index(SCLI) is calculated to depict the daily, weekly, and monthly safety climate of the site, thereby offering insights into the effectiveness of implemented safety measures and identifying areas for continuous improvement. This study is anticipated to significantly contribute to the systematic enhancement of safety and the prevention of accidents on construction sites, fostering an environment of improved productivity and strengthened safety culture through the application of the Safety Perception Model.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1286-1297
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• 2022

Purpose To assess the usefulness of various metal artifact reduction (MAR) methods in patients with hip prostheses. Materials and Methods This retrospective study included 47 consecutive patients who underwent hip arthroplasty and dual-energy CT. Conventional polyenergetic image (CI), orthopedic-MAR (OMAR), and virtual monoenergetic image (VMI, 50-200 keV) were tested for MAR. Quantitative analysis was performed in seven regions around the prostheses. Qualitative assessments included evaluation of the degree of artifacts and the presence of secondary artifacts. Results The lowest amount of image noise was observed in the O-MAR, followed by the VMI. O-MAR also showed the lowest artifact index, followed by high-keV VMI in the range of 120-200 keV (soft tissue) or 200 keV (bone). O-MAR had the highest contrast-to-noise ratio (CNR) in regions with severe hypodense artifacts, while VMI had the highest CNR in other regions, including the periprosthetic bone. On assessment of the CI of pelvic soft tissues, VMI showed a higher structural similarity than O-MAR. Upon qualitative analysis, metal artifacts were significantly reduced in O-MAR, followed by that in VMI, while secondary artifacts were the most frequently found in the O-MAR (p < 0.001). Conclusion O-MAR is the best technique for severe MAR, but it can generate secondary artifacts. VMI at high keV can be advantageous for evaluating periprosthetic bone.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.3
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• pp.79-85
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• 2024

We investigated the crystal structural property and chemical bonding nature of cellulose nanocrystal extracted directly from cotton cellulose using high-pressure homogenizer. The nanowire-like cellulose nanocrystals were randomly distributed in the form of a dense mesh. Based on calculating the interplanar distance of the Bragg-diffracted crystal plane observed through X-ray diffraction (XRD) analysis, it was found that the cellulose nanocrystals formed by high-pressure homogenizer had a monoclinc crystal structure, corresponding to the cellulose Iβ sub-polymorph. Solid-state nuclear magnetic resonance (NMR) analysis for the quantitatively evaluation of the amorphous region in cellulose nanocrystals revealed that the crystallinity index of cellulose nanocrystals was calculated to be 53.06 %. The O/C ratio of the surface of cellulose nanocrystal was estimated to be 0.82. Further analysis showed that chemical bonds of C-C bond or C-H bond, C-O bond, O-C-O bond or C=O bond, and O-C=O bond were the main chemical bonding states of the cellulose nanocrystal surface.