• Journal of the Society of Cosmetic Scientists of Korea
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• v.47 no.4
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• pp.317-331
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• 2021

Essential oil is a volatile substance obtained by physically obtaining fragrant plant materials made by one single plant and plant species, and is widely used for cosmetics, fragrances, and aroma therapy due to its excellent preservation, sterilization, and antibacterial effects. When essential oil would undergo the extraction and concentration processes, the agricultural chemicals thereof would be extracted and concentrated only to be harmful to the human body. This study analyzes 320 residual agricultural chemicals concentrated in the essential oil, and to this end, LC-MS/MS and GC-MS/MS are used, while the freezing process is applied instead of the conventional refining process hexane, to improve the preprocessing method. As a result of analyzing the essential oil, such ingredients as chlorpyrifos, piperonyl butoxide and silafluofen have been detected in Basil oil and Clove leaf oil. Hence, it is perceived that the residual agricultural chemicals should continue to be monitored for the essential oil.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• Applied Chemistry for Engineering
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• v.33 no.5
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• pp.496-501
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• 2022

In this study, an anode material for lithium secondary batteries was manufactured using petroleum-based residual oil, which is a petroleum refining by-product. Among petroleum-based residual oils, pyrolysis fuel oil (PFO), fluidized catalyst cracking-decant oil (FCC-DO), and vacuum residue (VR) were used as carbon precursors. The physicochemical characteristics of petroleum-based residual oil were confirmed through Matrix-assisted laser desorption/ionization Time-of-Flight (MALDI-TOF) and elemental analysis (EA), and the structural characteristics of anode materials manufactured from residual oil were evaluated using X-ray crystallography (XRD) and Raman spectroscopic techniques. VR was found to contain a wide range of molecular weight distributions and large amounts of impurities compared to PFO and FCC-DO, and PFO and FCC-DO exhibited almost similar physicochemical characteristics. From the XRD analysis results, carbonized PFO and FCC-DO showed similar d₀₀₂ values. However, it was confirmed that FCC-DO had a more developed layered structure than PFO in Lc (Length of a and c axes in the crystal system) and La values. In addition, FCC-DO showed the best cycle characteristics in electrochemical characteristics evaluation. According to the physicochemical and electrochemical results of the petroleum-based residual oil, FCC-DO is a better carbon precursor for a lithium secondary battery than PFO and VR.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.45-51
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• 2022

Technologies for collecting and treating pollutants from point sources are steadily being developed, but Non-point sources, it is difficult to develop emission treatment technologies and effective emission coefficients. However, since non-point sources make up about 60% of domestic emissions, and first of all, the method of calculating emissions should be reasonable, and the workplace should develop emission reduction technologies based on this. This study suggest the effectiveness and improvement of the emission coefficient currently used for the petrochemical industry with high emissions. The emission characteristics of non-point sources emission were confirmed by analyzing the LDAR (Leak Detection And Repair) data of OO company located in Yeosu, Jeollanam-do over the past five years. As a result, there was no difference in discharge characteristics according to fluid phase, but it was confirmed that there was a difference in the size of the device and the characteristics of each manufacturer. In addition, it was confirmed that the emission coefficient applied in the petrochemical industry was larger than that of the refining industry, and improvement measures were suggested. Through these studies, it is expected that emission coefficients specialized in the petrochemical industry can be applied and that the workplace itself will contribute to the development of technologies that can drastically reduce them.

• Environmental and Resource Economics Review
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• v.31 no.3
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• pp.419-449
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• 2022

This paper aims to quantify the potential economic burdens of EU's carbon border adjustment mechanisms faced by Korean domestic industries. In addition, this study tries to compare and analyzes changes in the burden of each industry resulted from the implementation of the domestic low-carbon policy. Based on the quantitative findings, we intend to suggest policy implications for establishing mid- to long-term strategies in response to climate change risks. Based on the environmentally extended input-output analysis, the total economic burdens of the domestic industries due to the EU's carbon border adjustment mechanisms are estimated to be approximately KRW 8,245.6 billion in 2030. Looking at the impacts by industry, it is found that major industries such as petrochemicals, petroleum refining, transportation equipment, steel, automobiles, and electric/electronic equipment industries are expected to account for 84.3% of the total potential burdens. In addition, in multiple policy scenarios assuming technological developments and energy transition following the implementation of domestic low-carbon policies, the total economic burden of carbon border adjustment is expected to decrease by about 11.7% to 15.0%. The main result of this study suggests that we should not view EU EU's carbon border adjustment mechanism as a trade regulation, but to use it as a momentum for more effective implementation of the low-carbon and energy transition strategies in the global carbon neural era.

• Journal of Conservation Science
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• v.38 no.4
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• pp.289-300
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• 2022

In the 6^th and 7^th centuries, 5 iron artifacts excavated form the Baekje Stone Tomb in Guryong-ri site, Ungcheon, Boryeong, were studied. The sample were metal microscopic observation, SEM-EDS analysis and Raman micro-spectroscopy analysis were conducted to understand the metallurgical characteristics. The microstructure observation showed the presence of ferrite and pearlite throughout, and differences in carbon content existed depending on the direction. Non-metallic inclusions were in the form of long lines, and most of them were wüstite, fayalite. It is indicated that the artifacts were forge welded using hypoeutectoid steel, with signs of carburizing and decarburizing processes. Some crystals with high P₂O₅, TiO₂, CaO content were identified as sarcopside, ulvöspinel, and perovskite, respectively, through Raman spectroscopy. A comparison of the results with previous studies on the sites of Bujang-ri site in Seosan and Bongseon-ri site in Seocheon, which are adjacent sites in the coastal area, revealed that, while heat treatment technology was available, the artifacts were not heat-treated considering the purpose for use for these artifacts. The chemical composition of the non-metallic inclusions P₂O₅, TiO₂, CaO were plotted in proportions to SiO₂ and compared with adjacent sites. Considering that the P₂O₅/SiO₂ ratio was widely distributed, the refining technology was not uniform. In addition, the TiO₂/SiO₂ ratio was found to be higher than that of other sites, meaning that a titanium-containing ore was used to manufacture the artifacts, unlike in surrounding sites, but it is not detected in all artifacts, so it may have been affected by various factors such as furnace walls in addition to raw materials. Although slag formers were used, considering the CaO/SiO₂ ratio and the (Al₂O₃/SiO₂)/(CaO/SiO₂) ratio, which appear to be similar to the surrounding sites, but it is possible that CaO containing raw ore was used because it is also affected by the components of raw ore. As a result of the study, it is highly likely that ore different from that of the surrounding sites was used for production, but a more comprehensive comparative study with the surrounding sites is needed in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Service Research and Studies
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• v.11 no.1
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• pp.59-79
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• 2021

Service-related research in accordance with the transition to the service economy era has been conducted in a wide variety of ways, but the development of a service-related scale suitable for the present time is still insignificant. The purpose of this study is to define the nature of services and to develop measurement items for them. First, four categories of service nature were adopted in the previous study. The four categories are 'relationship', 'interactivity', 'horizontality', and 'harmony'. In this study, sub-factors and specific items of each of these four service essences were extracted and developed as measurable items. As a qualitative study, the four categories of sub-factors were extracted, and a mixed study was adopted to prove the reliability and validity of the extracted factors through quantitative studies. The scale items were identified through literature study, free response method, and Delphi technique, and the measurement items were refined through a second questionnaire of 30 Delphi panels composed of experts. As a result of the study, 15 out of 52 questions for relationship, 11 out of 45 questions for bilateral direction, 9 out of 33 questions for horizontality, and 17 out of 61 questions for harmonization were derived after secondary refining. Through this study, it was possible to uncover new essential items suitable for the service economy era. SNS, network, synergy, platform, system, real name, and breakthrough are concepts that have not been obtained in previous studies, and can be seen as contributions of this study. However, due to various limitations, this study did not cover all aspects of the service, but mainly dealt with people-centered services, which are part of the service. In the future, it is necessary to study the development of service essence measurement items for the overall aspect of services developed according to the evolution of the service economy era.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.3
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• pp.161-168
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• 2023

Converter slag is a by-product generated by refining the pig iron produced into molten steel in the blast furnace, occupying about 15 % of the weight of steel production. It has a high free-CaO content that can generate expansion cracks when used for concrete aggregate. This is the main reason to make it difficult to recycle. To solve this problem, government guideline requires that converter slag has to be aged in an open yard for 90 days. However, aging can not be perfectly performed because it entails time and cost. In this study, we tried to investigate the applicability of converter slag as a cementitious material rather than an aggregate by mixing converter slag with mortar formulations. According to the EDS results of the converter slag in the experiment, we found that screening in the aggregate phase was more effective than that in the powder phase. When the particles separated by a magnet in the aggregate state were pulverized and used for concrete up to a 15 % replacement ratio, various engineering characteristics, such as flow, length change, and compressive strength, showed engineering characteristics similar to those of the control mix.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.4
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• pp.133-142
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• 2023

In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of "deterministic non-periodic flow", and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.