• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.117-125
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• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Analytical Science and Technology
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• v.22 no.3
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• pp.191-200
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• 2009

The aim of this study was to evaluate and establish of emission test method for liquid phase building materials such as paint, adhesive, sealant by emission cell. A small-scale emission chamber and emission cell were used to evaluate emission of TVOC from paint, adhesive, sealant. The quantity of TVOC emission were measured by a gas chromatography/mass spectrometry (GC/MS). Background concentration of TVOC was below $10{\mu}g/m^3$ in the emission chamber and cell. Air tightness and recovery in chamber and cell showed good results. The recovery of thermal desorber for toluene and n-dodecane were about 120%. The repeatability of response factor and retention time in GC/MS below 30%. The method detection limit of VOCs ranged 0.04~8.82 ng. The concentration of TVOC emission using emission cell was 1.35~1.41 times higher than emission chamber. The correlation of TVOC emission using chamber and cell method was significantly high (r=0.91~0.97).

• Membrane Journal
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• v.33 no.6
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• pp.362-368
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• 2023

This research article explores the application of Polymer of Intrinsic Microporosity (PIM-1) as a cutting-edge material for CO₂ gas separation membranes in response to the escalating global concern over climate change and the imperative to reduce greenhouse gas emissions. The study delves into the synthesis, molecular weight control, and fabrication of PIM-1 membranes, providing comprehensive insights through various characterization techniques. The intrinsic microporosity of PIM-1, arising from its unique crosslinked and rigid structure, is harnessed for selective gas permeation, particularly of carbon dioxide. The article emphasizes the tunable chemical properties of PIM-1, allowing for customization and optimization of gas separation membranes. By controlling the molecular weight, higher molecular weight (H-PIM-1) membranes are demonstrated to exhibit superior CO₂ permeability and selectivity compared to lower molecular weight counterparts (L-PIM-1). The study's findings highlight the critical role of molecular weight in tailoring PIM-1 membrane properties, contributing to the advancement of next-generation membrane technologies for efficient and selective CO₂ capture-an essential step in addressing the pressing global challenge of climate change.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.515-532
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• 2023

Bentonite, predominantly consists of expandable clay minerals, is considered to be the suitable buffering material in high-level radioactive waste disposal repository due to its large swelling property and low permeability. Additionally, the bentonite has large cation exchange capacity and specific surface area, and thus, it effectively retards the transport of leaked radionuclides to surrounding environments. This study aims to review the thermodynamic sorption models for four radionuclides (U, Am, Se, and Eu) and eight bentonites. Then, the thermodynamic sorption models and optimized sorption parameters were precisely analyzed by considering the experimental conditions in previous study. Here, the optimized sorption parameters showed that thermodynamic sorption models were related to experimental conditions such as types and concentrations of radionuclides, ionic strength, major competing cation, temperature, solid-to-liquid ratio, carbonate species, and mineralogical properties of bentonite. These results implied that the thermodynamic sorption models suggested by the optimization at specific experimental conditions had large uncertainty for application to various environmental conditions.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.576-583
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• 2023

This study aimed to investigate the impact of enhancing the electrode conductivity and mitigating the production of hydrogen peroxide - a by-product arising from lactate oxidation - on the performance of lactate electrodes. The electrical conductivity of the electrode was improved by modifying the surface of carbon paper with single-walled carbon nanotubes. Catalase was introduced to effectively eliminate the hydrogen peroxide produced during the lactate oxidation reaction. The carbon paper electrode, with simultaneous immobilization of both lactate oxidase and catalase, yielded a current 1.7 times greater than the electrode where only lactate oxidase was immobilized. The electrode in which lactate oxidase and catalase were co-immobilized on the surface of carbon paper modified with single-walled carbon nanotubes, produced a current of 171 µA, which was more than twice as much current as the carbon paper with only lactate oxidase immobilized. The optimized electrode showed a linear response up to lactate concentration of 20 mM, confirming that it can be used as a sensor electrode.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.541-554
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• 2023

The utilization of underground spaces in relation to tunnels and energy/waste storage is on the rise. To ensure the stability of underground spaces, it is crucial to reinforce rock fractures and discontinuities. Discontinuities, such as joints, can weaken the strength of the rock and lead to groundwater inflow into underground spaces. In order to enhance the strength and stability of the area around these discontinuities, rock grouting techniques are employed. However, during rock grouting, it is impossible to visually confirm whether the grouting material is being smoothly injected as intended. Without proper injection, the expected increases in strength, durability, and degree of consolidation may not be achieved. Therefore, it is necessary to predict in advance whether the grouting material is being injected as designed. In this study, we aimed to assess the injection performance based on injection variables such as the water/cement mixture ratio, injection pressure, and injection flow using UDEC (Universal Distinct Element Code) numerical program. Additionally, numerical results were validated by the lab experiment. The results of this study are expected to help optimize variables such as injection material properties, injection time, and pump pressure in the grouting design in the field.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.63-68
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• 2024

With the increasing use of transparent displays and flexible devices, polymer substrates offering excellent flexibility and strength are in demand. Since polymers are sensitive to heat, precise temperature control during the process is necessary. The study proposes a temperature measurement system for the laser processing area within the polymer base, aiming to address the drawbacks of using these polymer bases in laser-based selective processing technology. It presents the possibility of optimizing the process conditions of the polymer substrate through local temperature change measurements in the laser processing area. We developed and implemented the PDPT (Photodiode-based Planck Thermometry) to measure temperature in the laser-processing area. PDPT is a non-destructive, contact-free system capable of real-time measurement of local temperature increases. We monitored the temperature fluctuations during the laser processing of the polymer substrate. The study shows that the proposed laser-based temperature measurement technology can measure real-time temperature during laser processing, facilitating optimal production conditions. Furthermore, we anticipate the application of this technology in various laser-based processes, including essential micro-laser processing and 3D printing.