Journal of Korean Tunnelling and Underground Space Association
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v.24
no.2
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pp.153-170
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2022
Waterjet has been comprehensively used in urban areas owing to a suitable technique for cutting concrete and rock, and low noise and vibration. Recently, the abrasive waterjet technique has been adopted and applied by the Korea Atomic Energy Research Institute to demolish concrete plugging without disturbing and damaging In-situ Demonstration of Engineered Barrier System in the disposal research tunnel. In this study, the use of abrasive waterjet in the tunnel was evaluated for practical applicability and the existing cutting model was compared with the experimental results. As a variable for waterjet cutting, multi-cutting, water flow rate, abrasive flow rate, and standoff distance were selected for the diversity of analysis. As regarding the practical application, the waterjet facilitated path selection for cutting the concrete plugging and prevented additional disturbances in the periphery. The pump's noise at idling was 64.9 dB which is satisfied with the noise regulatory standard, but it exceeded the standard at ejection to air and target concrete because the experiment was performed in the tunnel space. The experimental result showed that the error between the predicted and measured cutting volume was 12~13% for the first cut and 16% for second cut. The standoff distance had a significant influence on the cutting depth and width, and the error tended to decrease with decrement of standoff distance.
Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.
An, Sojung;Choi, Youn;Son, MyoungJae;Kim, Kwang-Ho;Jung, Sung-Hwa;Park, Young-Youn
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.
Shin, Sung-Kyun;Park, Young-San;Choi, Byoung-Choel;Lee, Kwonho;Shin, Dongho;Kim, Young J.;Noh, Youngmin
Korean Journal of Remote Sensing
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v.30
no.5
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pp.597-605
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2014
The continuous observations for atmospheric aerosols were conducted during 3 years (2009 to 2011) by using Gwangju Institute of Science and Technology (GIST) multi-wavelength Raman lidar at Gwangju, Korea ($35.10^{\circ}N$, $126.53^{\circ}E$). The aerosol depolarization ratios calculated from lidar data were used to identify the Asian dust layer. The optical properties of Asian dust layer were different according to its vertical distribution. In order to investigate the difference between the optical properties of each individual dust layers, the transport pathway and the transport altitude of Asian dust were analyzed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We consider that the variation of optical properties were influenced not only their transport pathway but also their transport height when it passed over anthropogenic pollution source regions in China. The lower particle depolarization ratio values of $0.12{\pm}0.01$, higher lidar ratio of $67{\pm}9sr$ and $68{\pm}9sr$ at 355 nm and 532 nm, respectively, and higher ${\AA}ngstr\ddot{o}m$ exponent of $1.05{\pm}0.57$ which are considered as the optical properties of pollution were found. In contrast with this, the higher particle depolarization ratio values of $0.21{\pm}0.09$, lower lidar ratio of $48{\pm}5sr$ and $46{\pm}4sr$ at 355 nm and 532 nm, respectively, and lower ${\AA}ngstr\ddot{o}m$ exponent of $0.57{\pm}0.24$ which are considered as the optical properties of dust were found. We found that the degree of mixing of anthropogenic pollutant aerosols in mixed Asian dust govern the variation of optical properties of Asian dust and it depends on their altitude when it passed over the polluted regions over China.
Kim, Hyeon-Jae;Kim, Dong-Hoon;Lim, Chaewook;Shin, Yongtak;Lee, Sang-Chul;Choi, Youngjin;Woo, Seung-Buhm
Journal of Korean Society of Coastal and Ocean Engineers
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v.33
no.6
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pp.265-274
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2021
Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.
The study aimed to evaluate cancer damage cost due to risk from benzene inhalation. We performed health risk assessment based on US EPA guideline to estimate annual population risk in Ulsan metropolitan city. Also, we estimated a willingness-to-pay amount for reducing a cancer mortality rate to evaluate a value of statistical life. We combined the annual population risk and the value of statistical life to calculate the cancer damage cost. In the health risk assessment, we applied the benzene unit risk ($2.2{\times}10^{-6}{\sim}7.8{\times}10^{-6}$) in the US EPA'S Integrated Risk Information System to assess the annual population risk. Average concentration of benzene in ambient air is $7.88{\mu}g/m^3$(min: 1.16~max: $23.32{\mu}g/m^3$). We targeted an exposure population of 516,641 persons who aged over 30 years old. Using a Monte-Carlo simulation for uncertainty analysis, we evaluated that the population risk of benzene during ten years in Ulsan city is 2.90 persons (5 percentile: 0.32~95 percentile: 9.11persons). And the monthly average WTP for 5/1,000 cancer mortality reduction during ten years is 14,852 Won(95% C.I: 13,135~16,794 Won) and the implied VSL is 36 million Won(95% C.I: 30~40 million Won). Cancer damage cost due to risk from benzene inhalation during 10 years in Ulsan city is about 104 million Won(5 percentile: 13~95 percentile: 328 million Won). Health benefit cost to reduce a cancer mortality risk of benzene is about 50 million Won is Ulsan metropolitann city. But, it is very important that this cost is not for all health damage cost of cancer mortality in some area. We just recommended a model for evaluating a cancer risk reduction, so we must re-evaluate an integrated application of total VOCs damage cost including benzene.
Journal of Korean Society of Disaster and Security
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v.12
no.1
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pp.11-21
/
2019
This study assessed the risk of disaster by using QRE(Quick Risk Estimation - UNISDR Roll Model City of Basic Evaluation Tool) tools for three natural disasters and sixteen social disasters managed by the Seoul Metropolitan Government. The criteria for selecting 19 disaster types in Seoul are limited to disasters that occur frequently in the past and cause a lot of damage to people and property if they occur. We also considered disasters that are likely to occur in the future. According to the results of the QRE tools for disaster type in Seoul, the most dangerous type of disaster among the Seoul city disasters was "suicide accident" and "deterioration of air quality". Suicide risk is high and it is not easy to take measures against the economic and psychological problems of suicide. This corresponds to the Risk ratings(Likelihood ranking score & Severity rating) "M6". In contrast, disaster types with low risk during the disaster managed by the city of Seoul were analyzed as flooding, water leakage, and water pollution accidents. In the case of floods, there is a high likelihood of disaster such as localized heavy rains and typhoons. However, the city of Seoul has established a comprehensive plan to reduce floods and water every five years. This aspect is considered to be appropriate for disaster prevention preparedness and relatively low disaster risk was analyzed. This corresponds to the disaster Risk ratings(Likelihood ranking score & Severity rating) "VL1". Finally, the QRE tool provides the city's leaders and disaster managers with a quick reference to the risk of a disaster so that decisions can be made faster. In addition, the risk assessment using the QRE tool has helped many aspects such as systematic evaluation of resilience against the city's safety risks, basic data on future investment plans, and disaster response.
Journal of the Korea Organic Resources Recycling Association
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v.30
no.3
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pp.13-22
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2022
In order to improve water quality of the water system contaminated with dyes, biochars prepared using discarded waste resources were applied in this study. Biochars with a large specific surface area were manufactured using street tree pruning products or waste wood, and were applied to remove an organic dye in synthetic water. Biochars were made by pyrolysis of typical street tree porch products (Platanas, Ginkgo, Aak) and waste wood under air-controlled conditions. Methylene blue (MB), which is widely used in phosphofibers, paper, leather, and cotton media, was selected in this study. The adsorption capacity of Platanas for MB was the highest and the qmax value obtained using the Langmuir model equation was 78.47 mg/g. In addition, the adsorption energy (E) (kJ/mol) of MB using the Dubinin-Radushkevich (D-R) model equation was 4.891 kJ/mol which was less than 8 kJ/mol (a criteria distinguishing physical adsorption from chemical adsorption). This result suggests a physical adsorption with weak interactions such as van der Waals force between the biochar and MB. In addition, the physical adsorption may resulted from that Platanas-based biohar has the largest specific surface area and pore volume. The ∆G value obtained through the adsorption experiment according to temperature variation was -3.67 to -7.68, which also suggests a physical adsorption. Considering these adsorption results, the adsorption of MB onto Platanas-based biochar seems to occur through physical adsorption. Overall, it was possible to suggest that adsorption capacity of the biochr prepared from this study was equal to or greater than that of commercial activated carbon reported in other studies.
KSCE Journal of Civil and Environmental Engineering Research
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v.42
no.6
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pp.815-824
/
2022
Among the main facilities of the power plant, the circulating water used for cooling the power generation system is supplied through the Circulation Water Intake Basin (CWIB). The vortexes of various types generated in the Pump Sump (PS) of CWIB adversely affect the Circulation Water Pump (CWP) and pipelines. In particular, the free surface vortex accompanied by air intake brings about vibration, noise, cavitation etc. and these are the causes of degradation of CWP performance, damage to pipelines. Then power generation is interrupted by the causes. Therefore, it is necessary to investigate the hydraulic characteristics of CWIB through the hydraulic model experiment and apply an appropriate Anti Vortex Device (AVD) that can control the vortex to enable smooth operation of the power plant. In general, free surface vortex is controlled by Curtain Wall (CW) and the submerged vortex is by the anti vortex device of the curtain wall. The detailed specifications are described in the American National Standard for Pump Intake Design. In this study, the circulating water intake part of the Tripoli West 4×350 MW power plant in Libya was targeted, the actual operating conditions were applied, and the vortex reduction effect of the anti vortex device generated in the suction tank among the circulating water intake part was analyzed through a hydraulic model experiment. In addition, a floor splitter was basically applied to control the submerged vortex, and a new type of column curtain wall was additionally applied to control the vortex generated on the free surface to confirm the effect. As a result of analyzing the hydraulic characteristics by additionally applying the newly developed Column Curtain Wall (CCW) to the existing curtain wall, we have found that the vortex was controlled by forming a uniform flow. In addition, the vortex angle generated in the circulating water pump pipeline was 5° or less, which is the design standard of ANSI/HI 9.8, confirming the stability of the flow.
Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R2=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.
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