• Journal of the Korean Electrochemical Society
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• v.26 no.4
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• pp.71-79
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• 2023

In this study, we employed anionic, cationic, and nonionic surfactants for the hydrophilization of porous substrates used in the fabrication of pore-filling membranes. We investigated the extent of hydrophilization based on the type of surfactant, its concentration, and immersion time. Furthermore, we used the hydrophilized substrates to produce pore-filling anion exchange membranes and compared their ion conductivity to determine the optimal hydrophilization conditions. For the ionic surfactants used in this study, we observed that hydrophilization progressed rapidly from the beginning of immersion when the applied concentration was 3.0 wt%, compared to lower concentrations (0.05, 0.5, and 1.0 wt%). In contrast, for the relatively larger molecular weight non-ionic surfactants, smooth hydrophilization was not observed. There was no apparent correlation between the degree of hydrophilization and the ion conductivity of the anion exchange membrane. This discrepancy suggests that an excessive hydrophilization process during the treatment of porous substrates leads to excessive adsorption of the surfactant on the sparse surfaces of the porous substrate, resulting in a significant reduction in porosity and subsequently decreasing the content of polymer electrolyte capable of ion exchange, thereby greatly increasing the electrical resistance of the membrane.

• Journal of the Korean Geosynthetics Society
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• v.22 no.4
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• pp.17-26
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• 2023

To consider the risk of damage and fracture of P.C strands, the existing post-maintenance system alone has the limitations, hence it is necessary to quantitatively evaluate and predict the deterioration, durability and safety of facilities and establish a reasonable maintenance system considering the asset value of facilities. Therefore, it is worth considering a preventive maintenance plan that allows proactive measures to be taken before a major defect occurs in the temporary anchor. This study devised a preventive over tension method, reviewed its effectiveness through design and field tests, by calculating the resistance factors by performing a reliability-based optimization design. At this time, the over tension anchor method was evaluated using the ratio of the residual tension force after the fracture of P.C strands to the effective tension force before the fracture of P.C strand, followed by the resistance factor calculated by the optimal solution for each random variables using Excel solver and applying it to the limit state equations. As a result of the study, if the over tension ratio is 125% to 130%, the remaining strands showed a high resistance effect even after the fracture of P.C strand. As a result of the optimization design, it was found that it is appropriate to apply the load factor (γ) of 1.25, and the resistance factors of Φ₁, Φ₂, Φ₃ as 0.7, 0.5, 0.6.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Journal of Marine Bioscience and Biotechnology
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• v.15 no.2
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• pp.41-48
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• 2023

The objective of the present study was to improve the biomass productivity of newly isolated freshwater green microalga Parachlorella sp. This was accomplished by culture conditions optimization, including CO₂ concentration, superficial gas velocity, and light intensity, in 0.5 L bubble column photobioreactors. The supplied CO₂ concentration and gas velocity varied from 0.032% (air) to 10% and 0.02 m/s - 0.11 m/s, respectively, to evaluate their effects on growth kinetics. Next, to maximize the production rate of Parachlorella sp., a lumostatic operation based on a specific light uptake rate (q_e) was applied. From these results, the optimal CO₂ concentration in the supplied gas and the gas velocity were determined to be 5% and 0.064 m/s, respectively. For the lumostatic operation at 10.2 µmol/g/s, biomass productivity and photon yield showed significant increases of 83% and 66%, respectively, relative to cultures under constant light intensity. These results indicate that the biomass productivity of Parachlorella sp. can be improved by optimizing gas properties and light control as cell concentrations vary over time.

• Journal of Marine Bioscience and Biotechnology
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• v.15 no.2
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• pp.82-89
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• 2023

Microalgae, as photosynthetic organisms, possess the ability to produce a diverse array of bioactive compounds. This study focused on the transformant Chlamydomonas reinhardtii dZL and subjected it to cultivation under varying light intensities (60, 120, 180, and 240 µmol/m²/s). Our aim was to assess the impact of light intensity on both microalgal biomass and carotenoid production. The cultivation took place in 80 mL bubble column photobioreactors, specifically the Multi-cultivator. Notably, the culture exposed to 240 µmol/m²/s exhibited the most rapid cell growth, surpassing even the cell concentration achieved at 180 µmol/m²/s by day 8. A detailed analysis of the specific irradiance rate over time unequivocally revealed a sharp decline in growth rates when the rate fell below 2 × 10^-10 µmol/cell/s. Although the culture with 60 µmol/m²/s yielded the highest carotenoid content (1.2% of dry weight), the culture exposed to 240 µmol/m²/s recorded the highest carotenoid concentration at 8.9 mg/L owing to its higher biomass. Our findings reveal the critical importance of maintaining a specific irradiance rate above 2 × 10^-10 µmol/cell/s to enhance biomass and carotenoid productivity. This study lays the groundwork for defining optimal light intensity conditions applicable to mass culture systems, with the objective of augmenting C. reinhardtii biomass and optimizing carotenoid productivity.

• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.34-39
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• 2024

Energy harvesting technology that converts the wasted energy resources into electrical energy is emerging as a semipermanent power source for self-powered electronics and wireless low-power sensor systems. Among the various energy conversion techniques, flexible piezoelectric energy harvesters (f-PEHs), using materials with piezoelectric effects, have attracted significant interest because they can harvest a small mechanical energy into electrical signals without constraints of time and space in various environments. In this study, we used a flexible piezoelectric composite film fabricated by dispersing BaHf_xTi_(1-x)O₃ (x = 0, 0.01, 0.05, 0.1) piezoelectric powders inside a polymeric matrix to facilitate f-PEHs. The fabricated f-PEH with optimal Hf contents (x = 0.05) generated a maximum output voltage of 0.95 V and current signal of 130 nA with stable electrical/mechanical disabilities under periodically bending deformations. In addition, we demonstrated a cantilever-type f-PEH and investigated its potential as a sensor by characterizing the output performance under mechanical vibrations at various frequencies. This study provides the breakthrough for realizing self-powered energy harvesting and sensing systems by adopting the lead-free piezoelectric composites under vibrational environments.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.315-323
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• 2023

Marine accidents due to loss of stability of small ships have continued to increase over the past decade. In particular, since sudden winds have been pointed out as main causes of most small ship accidents, safety measures have been established to prevent them. In this regard, to prevent accidents caused by sudden winds, a systematic analysis technique is required. The aim of the present study was to develop a probabilistic approach to estimate extreme value and evaluate effects of wind on motion characteristics of ships. The present study included studies of motion analysis, extraction of extreme values, and motion characteristics. A series analysis was conducted for three conditions: wave only, wave with uniform wind speed, and wave with the NPD wind model. Hysteresis filtering and Peak-Valley filtering techniques were applied to time-domain motion analysis results for extreme value extraction. Using extracted extreme values, the goodness of fit test was performed on four distribution functions to select the optimal distribution-function that best expressed extreme values. Motion characteristics of a fishing boat were evaluated for three periodic motion conditions (Heave, Roll, and Pitch) and results were compared. Numerical analysis was performed using a commercial solver, ANSYS-AQWA.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.531-550
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• 2023

Purpose: The unique nature of life-and-death healthcare services sets them apart from other service industries. While many studies exist on the relationship between healthcare services and customer satisfaction, most of them focus on mildly ill patients, ignoring the differences between critically ill and non-seriously ill patients. This study discusses the actual quality of healthcare services for patients who are facing life-threatening illnesses and are on life support, as well as their right to protection and dignity. Methods: The survey conducted to 149 patients with the four major illnesses: cancer, heart disease, brain disease and rare and incurable disease, those who have experiences with senior general hospitals. Results: The basic statistics of this study are adequate to represent the four major critical illnesses, and the reliability and validity of this study's hypotheses, which were measured by multiple items, were analyzed, and the internal consistency was judged to be high. In addition, it was found that the convergent validity was good and the discriminant validity was also secured. When examining the goodness of fit of the hypotheses, the SRMR, which is the standardized root mean square of residuals that measures the difference between the covariance matrix of the data variables and the theoretical covariance matrix structure of the model, met the optimal criteria. Conclusion: The academic implications of this study are differentiated from other studies by moving away from evaluating the quality of healthcare services for mildly ill patients and focusing on the rights and dignity of patients with life-threatening illnesses in four senior general hospitals. In terms of academic implications, this study enriches the depth of related studies by demonstrating the right to protection and dignity as a factor of patient-centeredness based on physical environment quality, interaction quality, and outcome quality, which are presented as sub-factors of healthcare quality. We found that the three quality factors classified by Brady and Cronin (2001) are optimized for healthcare quality assessment and management, and that the results of patients' interaction quality assessment can be used to provide a comprehensive quality rating for hospitals. Health and human rights are inextricably linked, so assessing the degree to which rights and dignity are protected can be a superior and more comprehensive measurement tool than traditional health level measures for healthcare organizations. Practical implications: Improving the quality of the physical environment and the quality of outcomes is an important challenge for hospital managers who attract patients with life and death conditions, but given the scale and economics of time, money, and human inputs, improving the quality of interactions and defining them as performance indicators in hospital quality management is an efficient way to create maximum value in the short term.