• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.864-881
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• 2021

Purpose: This study aims is to provide a total care solution preventing disaster based on Big Data and AI technology and to service safety considered by individual situations and various risk characteristics. The purpose is to suggest a method that customized comprehensive index services to prevent and respond to safety accidents for calculating the living safety index that quantitatively represent individual safety levels in relation to daily life safety. Method: In this study, we use method of mixing AHP(Analysis Hierarchy Process) and Likert Scale that extracted from consensus formation model of the expert group. We organize evaluation items that can evaluate life safety prevention services into risk indicators, vulnerability indicators, and prevention indicators. And We made up AHP hierarchical structure according to the AHP decision methodology and proposed a method to calculate relative weights between evaluation criteria through pairwise comparison of each level item. In addition, in consideration of the expansion of life safety prevention services in the future, the Likert scale is used instead of the AHP pair comparison and the weights between individual services are calculated. Result: We obtain result that is weights for life safety prevention services and reflected them in the individual risk index calculated through the artificial intelligence prediction model of life safety prevention services, so the comprehensive index was calculated. Conclusion: In order to apply the implemented model, a test environment consisting of a life safety prevention service app and platform was built, and the efficacy of the function was evaluated based on the user scenario. Through this, the life safety index presented in this study was confirmed to support the golden time for diagnosis, response and prevention of safety risks by comprehensively indication the user's current safety level.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.67-76
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• 2021

Biogasification is a technology that produces environmentally friendly fuel using methane gas generated in the process of stably decomposing and processing organic waste. Biogasification is the most used method for energy conversion of organic waste with high moisture content, and is a useful method for organic waste treatment following the prohibition of direct landfill (2005) and marine dumping (2013). Due to African Swine Fever (ASF), which recently occurred in Korea, recycling of wet feed is prohibited, and consumers such as dry feed and compost are negatively recognized, making it difficult to treat food waste. Accordingly, biogasification is attracting more attention for the treatment and recycling of food waste. Korea's energy consumption amounted to 268.41 106toe, ranking 9th in the world. However, it is an energy-poor country that depends on foreign imports for about 95.8% of its energy supply. Therefore, in Korea, the Renewable Energy Portfolio Standard (RPS) is being introduced. The domestic RPS system sets the weight of the new and renewable energy certificate (REC, Renewable energy certificate) of waste energy lower than that of other renewable energy. Therefore, an additional incentive system is required for the activation of waste-to-energy. In this study, the operation of an anaerobic digester that treats food waste, food waste Leachate and various organic wastes was confirmed. It was intended to be used as basic data for preparing the waste-to-energy incentive system through precise monitoring for a certain period of time. Four sites that produce biogas from organic waste and use them for power generation and heavy gas were selected as target facilities, and field surveys and sampling were conducted. Basic properties analysis was performed on the influent sample of organic waste and the effluent sample according to the treatment process. As a result of the analysis of the properties, the total solids of the digester influent was an average of 12.11%, and the volatile solids of the total solids were confirmed to be 85.86%. BOD and CODcr removal rates were 60.8% and 64.8%. The volatile fatty acids in the influent averaged 55,716 mg/L. It can be confirmed that most of the volatile fatty acids were decomposed and removed with an average reduction rate of 92.3% after anaerobic digestion.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.4
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• pp.194-210
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• 2022

The monthly inventory of dissolved inorganic carbon (C_T) and its fluxes were simulated using a box-model for the southeastern Yellow Sea, bordering the northern East China Sea. The monthly C_T data was constructed by combining the observed data representing four seasons with the data adopted from the recent publications. A 2-box-model of the surface and deep layers was used, assuming that the annual C_T inventory was at the steady state and its fluctuations due to the advection in the surface box were negligible. Results of the simulation point out that the monthly C_T inventory variation between the surface and deep box was driven primarily by the mixing flux due to the variation of the mixed layer depth, on the scale of -40~35 mol C m^-2 month^-1. The air to sea CO₂ flux was about 2 mol C m^-2 yr^-1 and was lower than 1/100 of the mixing flux. The biological pump flux estimated magnitude, in the range of 4-5 mol C m^-2 yr^-1, is about half the in situ measurement value reported. The C_T inventory of the water column was maximum in April, when mixing by cooling ceases, and decreases slightly throughout the stratified period. Therefore, the total C_T inventory is larger in the stratified period than that of the mixing period. In order to maintain a steady state, 18 mol C m^-2 yr^-1 (= 216 g C m^-2 yr^-1), the difference between the maximum and minimum monthly CT inventory, should be transported out to the East China Sea. Extrapolating this flux over the entire southern Yellow Sea boundary yields 4 × 10⁹ g C yr^-1. Conceptually this flux is equivalent to the proposed continental shelf pump. Since this flux must go through the vast shelf area of the East China Sea before it joins the open Pacific waters the actual contribution as a continental shelf pump would be significantly lower than reported value. Although errors accompanied the simple box model simulation imposed by the paucity of data and assumptions are considerably large, nevertheless it was possible to constrain the relative contribution among the major fluxes and their range that caused the C_T inventory variations, and was able to suggest recommendations for the future studies.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.429-438
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• 2022

This study evaluated the effect of the degree of weathering on the particle size distribution and the amount of fine particles generated in the aggregate production process during the crushing of igneous rock. Rock samples were collected from three areas with differences in strength from the Schmith hammer measurement at the aggregate quarry in Geochang, Gyeongsangbuk-do. After crushing with a jaw crusher under the same conditions in laboratory, particle size analysis, mineral analysis, chemical analysis, and weathering index were calculated. The Schmidt hammer measurements were 56, 28, and <10, and the CIA and CIW values of weathering index were also different, so the rock samples were classified into hard rock, soft rock, and weathered rock according to the weathering degree. It shows a smaller particle size distribution toward weathered rocks under the microscope, and the proportion of altered clay minerals such as sericite increased. The composition of feldspar and quartz was high for hard rock, and the ratio of muscovite and kaolinite was low. As a result of the crushing of the jaw crusher, hard rock produced a lot of coarse crushed material (13.2mm), while soft rock and weathered rock produced fine crushed material (4.75mm). The former showed the characteristics of the beta distribution curve, and the latter showed the bimodal distribution curve. The production of fine rock particles (based on 0.71mm of sieve, wt. %) increased to 13%<21%<22% in hard rock, soft rock, and weathered rock, and the greater the degree of weathering, the more fine rock particles were generated. The fine particles are recovered by the operation of the sand unit in the wet aggregate production process. Therefore, in order to minimize the amount of sludge generated in the aggregate production process, it was judged that a study on the optimal operation of cyclones could be necessary.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.531-540
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• 2022

Various iron minerals that precipitate in acid mine drainage have a great influence on the concentration change and mobility of trace elements in the drainage during phase transition to other minerals as well as the precipitation process. This study investigated the change of mineral properties and the behaviors of trace elements influenced by pH and time for the precipitates collected from the acid mine drainage treatment system of the Dalsung mine, where schwertmannite is mainly precipitated. However, the main mineral precipitated in the drainage was goethite, suggesting schwetmannite has already undergone a phase transition to goethite to some extent, and it was observed that at higher pH, the peak width at half maximum of XRD peak was narrower. This can be interpreted as the transformation of small amount of amorphous schwetmannite to goethite or an increase in the crystallinity of goethite, and it showed that the higher the pH, the greater this change was. The concentration of Fe was also greatly affected by the pH values, and as the pH increased, the concentration of Fe in the drainage decreased. With increasing time, the Fe concentration increased and then decreased, which can be interpreted to indicate the dissolution of schwertmannite and precipitation of goethite. This mineral change probably resulted in the rapid increase of the concentration of S at initial stage, but its concentration was stabilized later. The concentration of S is also related to the stability of schwetmannite, showing a high concentration at a low pH at which schwertmannite is stable and a low concentration at a high pH at which goethite is stable. The trace elements present as cations in the drainage also showed a close relationship with the pH, generally the lower the pH, the higher the concentration, due to the solubility changes by the pH, and the precipitation and the changes in mineral surface charge at high pH. On the other hand, in the case of As, existing as an anion, although it showed a high concentration at low pH, its concentration increased with time at all pH values, which is probably related to the concentration of Fe which can be coprecipitated in the drainage, and the increase of As concentration with time is also considered to be related to the decrease in schwertmannite rather than the mineral surface charge.

• Journal of Food Hygiene and Safety
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• v.37 no.3
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• pp.143-148
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• 2022

This study was to investigate an analytical method for determining dieckol content in Ecklonia stolonifera extract. According to the guidelines of International Conference on Harmonization. Method validation was performed by measuring the specificity, linearity, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ) of dieckol using high-performance liquid chromatography-photodiode array. The results showed that the correlation coefficient of calibration curve (R²) for dieckol was 0.9997. The LOD and LOQ for dieckol were 0.18 and 0.56 ㎍/mL, respectively. The intra- and inter-day precision values of dieckol were approximately 1.58-4.39% and 1.37-4.64%, respectively. Moreover, intra- and inter-day accuracies of dieckol were approximately 96.91-102.33% and 98.41-105.71%, respectively. Thus, we successfully validated the analytical method for estimating dieckol content in E. stolonifera extract.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.462-475
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• 2022

Purpose: Allomyrina dichotoma larvae are one of the approved edible insects with nutritional value and various functional and medicinal properties. Previously we have demonstrated that the Allomyrina dichotoma larval extract (ADLE) ameliorates hepatic insulin resistance in high-fat diet (HFD)-induced diabetic mice through the activation of adenosine monophosphate-activated protein kinase (AMPK). This study investigated the effects of ADLE on insulin resistance in the skeletal muscle and explored mechanisms for enhancing the glucose uptake in palmitate (PAL)-treated C2C12 myotubes. Methods: To induce insulin resistance, the differentiated C2C12 myotubes were treated with PAL (0.5 mM) for 24 hours, and then treated with a 0.5 mg/ml concentration of ADLE, and the resultant effects were measured. The expression levels of glucose transporter-4 (GLUT4), AMPK, and the mitochondrial metabolism-related proteins were analyzed by western blotting. The mRNA expression levels of lipogenesis- related genes were determined by quantitative reverse-transcriptase PCR. Results: The exposure of C2C12 myotubes to 0.5 mg/ml of ADLE increased cell viability significantly compared to PAL-treated cells. ADLE upregulated the protein expression of GLUT4 and enhanced glucose uptake in the PAL-treated cells. ADLE increased the phosphorylated AMPK in both the PAL-treated C2C12 myotubes and HFD-treated skeletal muscle. The reduced expression levels of peroxisome-proliferator-activated receptor gamma co-activator-1 alpha (PGC1α) and uncoupling protein 3 (UCP3) due to the PAL and HFD treatment were reversed by the ADLE treatment. The citrate synthase activity was also significantly increased with the PAL and ADLE co-treatment. Moreover, the mRNA and protein expressions of fatty acid synthesis-related factors were reduced in the PAL and HFD-treated muscle cells, and this effect was significantly attenuated by the ADLE treatment. Conclusion: ADLE activates AMPK, which in turn induces mitochondrial metabolism and reduces fatty acid synthesis in C2C12 myotubes. Therefore, ADLE could be useful for preventing or treating insulin resistance of skeletal muscles in diabetes.