• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.231-238
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• 2023

Since there is a limit to the physically visible horizon that sensors for autonomous driving can perceive, complementary utilization of digital map data such as a Local Dynamic Map (LDM) along the probable route of an Autonomous Vehicle (AV) is proposed for safe and efficient driving. Although the amount of digital map data may be insignificant compared to the amount of information collected from the sensors of an AV, efficient management of map data is inevitable for the efficient information processing of AVs. The objective of this study is to analyze the efficiency of information use and information processing time of AV according to the expansion of the active section of LDM-based static road and traffic information. To carry out this objective, a microscopic simulator model, VISSIM and VISSIM COM, was employed, and an area of about 9 km × 13 km was selected in the Busan Metropolitan Area, which includes heterogeneous traffic flows (i.e., uninterrupted and interrupted flows) as well as various road geometries. In addition, the LDM information used in AVs refers to the real high-definition map (HDM) built on the basis of ISO 22726-1. As a result of the analysis, as the electronic horizon area increases, while short links are intensively recognized on interrupted urban roads and the sum of link lengths increases as well, the number of recognized links is relatively small on uninterrupted traffic road but the sum of link lengths is large due to a small number of long links. Therefore, this study showed that an efficient range of electronic horizon for HDM data collection, processing, and management are set as 600 m on interrupted urban roads considering the 12 links corresponding to three downstream intersections and 700 m on uninterrupted traffic road associated with the 10 km sum of link lengths, respectively.

• Clean Technology
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• v.29 no.1
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• pp.31-38
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• 2023

Due to water shortages caused by water pollution and climate change, total organic carbon (TOC) standards have been implemented for wastewater discharged from public sewage treatment facilities. Furthermore, there is a growing interest and body of research pertaining to the reuse of sewage treatment water as a secure alternative water resource. The membrane bio-reactor (MBR) method is commonly used for advanced wastewater treatment because it can remove organic and inorganic ions and it does not require or emit any chemicals. However, the MBR process uses a separation membrane (MF), which requires frequent film cleaning due to fouling caused by a high concentration of mixed liquor suspended solid (MLSS). In this study, process improvement and microbubble cleaning efficiency were evaluated to improve the differential pressure, water flow, and MF fouling, which are the biggest disadvantages of operating the MF. The existing MBR method was improved by installing a precipitation tank between the air tank and the MBR tank in which raw water was introduced. Microbubbles were injected into a separation membrane tank into which the supernatant water from the precipitation tank was introduced. The microbubble generator was operated with a 15 day on, 15 day off cycle for 5 months to collect discharged water samples (4L) and measure TOC. As the supernatant water from the precipitation tank flowed into the separation membrane tank, about 95% of the supernatant water MLSS was removed so the MF fouling from biological contamination was prevented. Due to the application of microbubbles to supernatant water from the precipitation tank, the differential pressure of the separation membrane tank decreased by 1.6 to 2.3 times and the water flow increased by 1.4 times. Applying microbubbles increased the TOC removal rate by more than 58%. This study showed that separately operating the air tank and the separation membrane tank can reduce fouling, and suggested that applying additional microbubbles could improve the differential pressure, water flow, and fouling to provide a more efficient advanced treatment method.

• Journal of agricultural medicine and community health
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• v.48 no.1
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• pp.28-40
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• 2023

Objectives: This study aimed to identify the level of self-assessment of life satisfaction and various factors related to the life satisfaction of Korean physicians. Methods: This study is a secondary data analysis using the 2016 Korean physician survey Korean Physician Survey(KPS) data collected by the Research Institute for Healthcare Policy of the Korean Medical Association. The member database(DB) of the Korean Medical Association was used for sampling and the target population was formed and surveyed by using stratified quota sampling. A questionnaire was sent by E-mail as an online survey method and was conducted for a total of 7 weeks from November 21, 2016 to January 8, 2017. The final number of respondents was 8,564 (response rate 13.8%). In this study, a total of 7,228 physicians, excluding residents and public health doctors who are currently treating patients directly, were studied. Factors affecting the life satisfaction of physicians were analyzed using ordinal logistic regression analysis. Results: The physical factors positively related to the life satisfaction of physicians were those who were in their 60s, female, and thought they had good health status. As for psychological factors, stress was low. As for economic factors, satisfaction with income was high. As for social factors, the physicians lived with their families and were satisfied with the time they could spend with them. Also, the physicians were satisfied with the social respect they received as a doctors. Conclusions: Based on the results of this study, it is thought that a multifaceted approach is needed to increase the life satisfaction of physicians.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.195-210
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• 2023

Additional deposition and erosion in large rivers in South Korea have continued to occur toward morphological stabilization after massive dredging through the four major river restoration project, subsequently requiring precise bathymetry monitoring. Hyperspectral bathymetry method has increasingly been highlighted as an alternative way to estimate bathymetry with high spatial resolution in shallow depth for replacing classical intrusive direct measurement techniques. This study introduced the conventional Optimal Band Ratio Analysis (OBRA) of hyperspectral bathymetry method, and evaluated the performance in a domestic large river in normal turbid and flow condition. Maximum measurable depth was estimated by applying correlation coefficient and root mean square error (RMSE) produced during OBRA with cascadedly applying cut-off depth, where the consequent hyperspectral bathymetry map excluded the region over the derived maximum measurable depth. Also non-linearity was considered in building relation between optimal band and depth. We applied the method to the Nakdong and Hwang River confluence as a large river case and obtained the following features. First, the hyperspectal method showed acceptable performance in morphological mapping for shallow regions, where the maximum measurable depth was 2.5 m and 1.25 m in the Nakdong and Hwang river, respectively. Second, RMSE was more feasible to derive the maximum measurable depth rather than the conventional correlation coefficient whereby considering various scenario of excluding range of in situ depths for OBRA. Third, highly turbid region in Hwang River did not allow hyperspectral bathymetry mapping compared with the case of adjacent Nakdong River, where maximum measurable depth was down to half in Hwang River.

• Journal of Life Science
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• v.33 no.2
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• pp.158-168
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• 2023

Natural products have been used to mitigate the effects of cancer and infectious diseases, as they feature diverse bioactivities, such as antioxidant, antibacterial, anti-inflammatory, and immunomodulatory effects. Here, we chose 10 natural products that are well-known as pulmonary enhancers and investigated their bactericidal effects on Streptococcus pneumoniae. In the disk diffusion assay, the growth of S. pneumoniae was significantly regulated by G. fructus treatment regardless of extraction method used. We first adopted spraying as a novel delivery method for G. fructus. Interestingly, mice exposed to G. fructus three times a day for 2 weeks were resistant to S. pneumoniae intranasal infection (shown both through body weight loss and survival rates compared to the control group). Moreover, we confirmed that exposure to G. fructus regulated the colonization of the bacteria despite the sustained inflammation in the lung after exposure to S. pneumoniae, indicating that migrated inflammatory immune cells may involve a host defense mechanism against pulmonary infectious diseases. While a similar number of granulocytes (CD11b⁺Ly6C⁺Ly6G⁺), neutrophils (CD11b⁺Ly6C^intLy6G⁺), and monocytes (CD11b⁺Ly6C^intLy6G^-) were found between groups, a significantly increased number of alveolar macrophages (CD11b⁺CD11c^hiF4/80⁺) was detected in BAL fluids of mice pre-exposed to G. fructus at 5 days after S. pneumonia infection. Taken together, our data suggest that this usage of G. fructus can induce protective immunity against bacterial infection, indicating that facial spray may be helpful in enhancing the defense mechanism against pulmonary inflammation and in evaluating the efficacy of natural products as immune enhancers against respiratory diseases.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of IKEEE
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• v.27 no.1
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• pp.122-126
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• 2023

In this paper, a control system for a complex microbial incubator was proposed. The proposed control system consists of a control unit, a communication unit, a power supply unit, and a control system of the complex microbial incubator. The controller of the complex microbial incubator is designed and manufactured to convert analog signals and digital signals, and control signals of sensors such as displays using LCD panels, water level sensors, temperature sensors, and pH concentration sensors. The water level sensor used is designed and manufactured to enable accurate water level measurement by using the IR laser method with excellent linearity in order to solve the problem that existing water level sensors are difficult to measure due to foreign substances such as bubbles. The temperature sensor is designed and used so that it has high accuracy and no cumulative resistance error by measuring using the thermal resistance principle. The communication unit consists of two LAN ports and one RS-232 port, and is designed and manufactured to transmit signals such as LCD panel, PCT panel, and load cell controller used in the complex microbial incubator to the control unit. The power supply unit is designed and manufactured to supply power by configuring it with three voltage supply terminals such as 24V, 12V and 5V so that the control unit and communication unit can operate smoothly. The control system of the complex microbial incubator uses PLC to control sensor values such as pH concentration sensor, temperature sensor, and water level sensor, and the operation of circulation pump, circulation valve, rotary pump, and inverter load cell used for cultivation. In order to evaluate the performance of the control system of the proposed complex microbial incubator, the result of the experiment conducted by the accredited certification body showed that the range of water level measurement sensitivity was -0.41mm~1.59mm, and the range of change in water temperature was ±0.41℃, which is currently commercially available. It was confirmed that the product operates with better performance than the performance of the products. Therefore, the effectiveness of the control system of the complex microbial incubator proposed in this paper was demonstrated.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.57-70
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• 2023

In recent years, the frequency of warnings about particulate matter (PM) has gradually increased in Korea, along with an increase in its intensity. Because of their vast surface area, reactivity to external particles, and characteristics of their leaves, urban trees can act as biofilters, reducing PM pollution. However, the air pollutant PM can cause various types of damage not only to human health but also to vegetation. Studies performed to date on the responses of trees to PM are still insufficient. Here, we analyzed the correlation between PM adsorption and physiological and biochemical responses of four major street tree species, namely, Abies holophylla, Acer buergerianum, Pinus densiflora, and Quercus variabilis, under conditions of approximately 300 ㎍ m^-3 of fly ash emissions using a phytotron. The results showed that the physiological and biochemical responses and PM adsorption differed depending on the tree species. In correlation analysis, it was confirmed that there were positive correlations between physiological factors, and PM adsorption on adaxial leaf surfaces negatively impacted the physiological characteristics. This study provides fundamental information for selecting tree species to reduce PM pollution and develop sustainable urban forests.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.473-481
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• 2022

Environment-friendly harvesting is practiced to maintain ecosystem, landscape, and forest protection functions. The present study was conducted at Simgok-ri, Sinbuk-myeon, Pocheon, Gyeonngi-do, where a 41-50-year-old Japanese larch forest was harvested in an environment-friendly manner from 2017 to 2019. The dynamics of organic matter in this forest were investigated at three years after the harvest. Specifically, organic matter content was measured on the forest floor and in overstory biomass, litterfall, and soil up to 30 cm in depth from June 2020 to January 2021. Owing to the harvest, the amount of overstory biomass of the Japanese larch stands decreased from 142.22 to 44.20 t ha^-1. On the forest floor, the amount of organic matter was 32.87 t ha^-1 in the control plots and 23.34 t ha^-1 in the harvest plots. Annual litterfall was 4.43 t ha^-1 yr^-1 in the control plots and 1.16 t ha^-1 yr^-1 in the harvest plots. Soil bulk density in the B horizon was 0.97 g cm^-3 in the control plots and 1.06 g cm^-3 i n the harvest plots. Soil organic matter content was 11.5% in the control plots and 12.8% in the harvest plots. The total amount of soil organic matter did not differ significantly between the control plots (245.21 t ha^-1) and harvest plots (263.92 t ha^-1), although the amount of soil organic matter tended to be higher in the harvest plots. The total amount of organic matter in the forest was estimated to be 406.48 t ha^-1 in the control plots and 338.21 t ha^-1 in the harvest plots. In the harvest plots, the ratio of aboveground organic matter decreased to 13.1% and soil organic matter increased to 78.0%, indicating that the distribution of organic matter changed significantly in these plots. Overall, the carbon accumulated in aboveground biomass was substantially reduced by environment-friendly harvesting, whereas the soil carbon level increased, which played a role in mitigating the reduction of system carbon in the forest. These results highlight one possible resolution for forest management in terms of coping with climate change. However, given that only three years of environment-friendly harvesting data were analyzed, further research on the dynamics of organic matter and tree growth is needed.