• Journal of Wetlands Research
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• v.23 no.1
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• pp.35-43
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• 2021

The application of nature-based solutions, such as low impact development (LID) techniques and green infrastructures, for stormwater management continue to increase in urban areas. Plants are usually utilized in LID facilities to improve their pollutant removal efficiency through phytoremediation. Plants can also reduce maintenance costs and frequency by means of reducing the accumulation of pollutants inside the facility. Plants have long been used in different LID facilities; however, proper plant-selection should be considered since different species tend to exhibit varying pollutant uptake capabilities. This study was conducted to investigate the pollutant uptake capabilities of plants by comparing the dry matter and nutrient contents of different plant species in roadsides, LID facilities, and landscape areas. The dry matter content of the seven herbaceous plants, shrubs, and arboreal trees ranged from 60% to 90%. In terms of nutrient content, the total nitrogen (TN) concentration in the tissues of herbaceous plants continued to increase until the summer season, but gradually decreased in the succeeding periods. TN concentrations in shrubs and trees were observed to be high from early spring up to the late summer seasons. All plant samples collected from the LID facility exhibited high TP content, indicating that the vegetative components of LID systems are efficient in removing phosphorus. Overall, the nutrient content of different plant species was found to be highly influenced by the urban environment which affected the stormwater runoff quality. The results of this study can be beneficial for establishing plant selection criteria for LID facilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.600-605
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• 2022

In this study, nitrogen oxide (NO_x) removal experiments were performed using a graphene based ceramic filter coated with a V₂O₅-WO₃-TiO₂ catalyst. Graphene oxide (GO) was prepared by Hummer's method using graphite, and the reduced graphene oxide was produced by reducing with hydrazine (N₂H₄). Vanadium (V), Tungsten (W), and Titanium (Ti) were coated by the sol-gel method, and then a metal oxide-supported filter was prepared through a calcination process at 350 ℃. A NO_x removal efficiency test was performed for the catalytic ceramic filters with UV light in a humid condition. When graphene oxide (GO) and reduced graphene oxide (rGO) were present on the filter, the NOx removal efficiency was superior to that of the conventional ceramic filter. Most likely, this is due to an improvement in the adsorption properties of NOx molecules on graphene coated surfaces. As the concentration of graphene increased, higher NO_x removal efficiency was confirmed.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.649-660
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• 2022

In 1959, Satomura used spectral Doppler ultrasound to express the velocity of red blood cells according to time change, and Kato defined a zero-base line that could tell the direction of blood flow, making it possible to know the direction of blood flow. This became the basis for the widely used classifications of Triphasic, Biphasic, and Monophasic. However, the above classification has limitations that confuse users with the meaning and timing of use in a clinical environment. As a result, the American Society for Vascular Medicine (SVM) and the Society for Vascular Ultrasound (SVU) A consensus document on Doppler waveform analysis was declared by the joint committee. This study tried to review this consensus and to suggest nomenclature and modifiers that can be used in the domestic vascular ultrasound clinical field. The joint committee formed by SVM and SVU recommended that the use of the triphasic waveform and the biphasic waveform be used as a multiphasic waveform rather than being used due to the ambiguity of interpretation. In addition, it was agreed to name the hybrid-type waveform, which is a monophasic and high-resistance waveform, which has always been a problem of interpretation in a clinical environment, as an intermediate resistive waveform. In addition, in order to increase the communication efficiency between the interpreter and the sonographer, waveform analysis was classified into a main descriptor and a modifier, and it was recommended to use a single nomenclature by unifying various synonyms. It is expected that this literature review will provide accurate arterial spectral Doppler waveform interpretation and an agreed-upon nomenclature to radiologists performing vascular ultrasound examination in clinical practice, and will be utilized as basic data that can contribute to the improvement of public health.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.235-246
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• 2023

In this paper, we propose suggestions for developing a Metaverse platform for educational purpose utilizing a Delphi study method with experts on Metaverse and digital education. 17 experts participated in the 1st study and 16 took part in the 2nd study, and data was collected via emails from January 5th to 10th for the 1st study and from January 12th to 17th for the 2nd study in 2022. Collected data in the 1st study was analyzed by applying content analysis. The results for the 1st study indicated that there were 120 sub-factors were derived from 7 main questions(the necessity of a Metaverse platform for future education, how to use the Metaverse platform for education to improve the capacities needed for future human resources, problems that may arise during education using the Metaverse platform, the functions that the Metaverse platform for education should have, the infrastructure and environment required when using the Metaverse platform for education, how to use the Metaverse effectively as a learning space, subjects and educational contents that will be effective if conducted on the Metaverse platform for education). The results for the 2nd study were presented by being ranked with calculated means of sub-factors for each question. Finally, based on the results, suggestions for building a Metaverse platform for educational purpose are stated and limitations of the study and possible future study are discussed.

• Journal of the Korean Electrochemical Society
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• v.24 no.4
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• pp.77-92
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• 2021

The energy density of lithium-ion batteries (LIBs) determines the mileage of electric vehicles. For increasing the energy density of LIBs, it is necessary to develop high-capacity active materials that can store more lithium ions within constrained weight. The rapid progress made in cathode technology has realized the utilization of the near-theoretical capacity of cathode materials. In contrast, commercial LIBs have still exploited graphite as active material in anodes since the 1990s. The most promising way to increase anodes' capacity is to mix high-capacity and long-cycle-life silicon oxides (SiOx) with graphite. However, the low initial Coulombic efficiency (ICE) of SiOx limits its content below 15 wt%, impeding the capacity increase in anodes. To address this issue, various prelithiation techniques have been proposed, which can improve the ICE of high-capacity anode materials. In this review paper, we introduce the principles and expected effects of prelithiation techniques reported so far. According to the reaction mechanisms, the strategies are categorized. Mainly, we focus on the recent progress of solution-based chemical prelithiation methods with commercial viability, of which lithiation reaction occurs homogeneously at liquid-solid interfaces. We believe that developing a cost-effective and mass-scalable prelithiation process holds the key to dominating the anode market for next-generation LIBs.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.77-89
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• 2022

It has been proven through many previous researches that the pretrained language model with a large corpus helps improve performance in various natural language processing tasks. However, there is a limit to building a large-capacity corpus for training in a language environment where resources are scarce. Using the Cross-lingual Post-Training (XPT) method, we analyze the method's efficiency in Korean, which is a low resource language. XPT selectively reuses the English pretrained language model parameters, which is a high resource and uses an adaptation layer to learn the relationship between the two languages. This confirmed that only a small amount of the target language dataset in the relationship extraction shows better performance than the target pretrained language model. In addition, we analyze the characteristics of each model on the Korean language model and the Korean multilingual model disclosed by domestic and foreign researchers and companies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.276-290
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• 2023

Several efforts in Korea are currently underway with the goal of commercializing autonomous vehicles. Hence, various studies are emerging on autonomous vehicles that drive safely and quickly according to operating guidelines. The current study examines the path search of an autonomous vehicle from a microscopic viewpoint and tries to prove the efficiency required by learning the lane change of an autonomous vehicle through Deep Q-Learning. A SUMO was used to achieve this purpose. The scenario was set to start with a random lane at the starting point and make a right turn through a lane change to the third lane at the destination. As a result of the study, the analysis was divided into simulation-based lane change and simulation-based lane change applied with Deep Q-Learning. The average traffic speed was improved by about 40% in the case of simulation with Deep Q-Learning applied, compared to the case without application, and the average waiting time was reduced by about 2 seconds and the average queue length by about 2.3 vehicles.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.1
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• pp.51-57
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• 2023

Because of the global pollution caused by plastic disposal, demand for eco-friendly transformation in the packaging industry is increased. As part of that, the utilization of polylactic acid (PLA) as a food packaging material is increased. However, it is necessary to improve the crystallinity of PLA by adding nucleating agents or to improve the modulus by adding fillers because of the excessive brittleness of the PLA matrix. Thus, the cellulose nanofiber (CNF) was fabricated and dried to obtain a powder form and applied to the CNF/PLA nanocomposite. The effect of CNF on the morphological, thermal, rheological, and dynamic mechanical properties of the composite was analyzed. We can confirm the impregnated CNF particle in the PLA matrix through the field emission scanning electron microscope (FE-SEM). Differential scanning calorimetry (DSC) analysis showed that the crystallinity of not annealed CNF/PLA nanocomposite was increased approximately 2 and 4 times in the 1st and 2nd cycle, respectively, with the shift to lower temperature of cold crystallization temperature (T_cc) in the 2nd cycle. Moreover, the crystallinity of annealed CNF/PLA nanocomposite increased by 13.4%, and shifted T_cc was confirmed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.