• Korean Journal of Environmental Biology
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• v.38 no.4
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• pp.733-743
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• 2020

We estimated the carbon storage of coal mines reclaimed using Betula platyphylla (BP), Pinus koraiensis (PK), and Pinus spp. (PS, Pinus densiflora, Pinus rigida, and Pinus thunbergii). The carbon storage of tree biomass (TB), forest floor(FF), mineral soil (MS), and the total forest were quantified. Reclaimed sites were located in Gangwon-do, Gyeongsangbuk-do, and Jeollanam-do; reclamation was conducted at various times in each region. The carbon storage (ton C ha^-1) in FF (BP: 3.31±0.59, PK: 3.60±0.93, PS: 4.65±0.92), MS (BP: 28.62±2.86, PK: 22.26±5.72, PS: 19.95±3.90), and the total forest(BP: 54.81±7.22, PK: 47.29±8.97, PS: 45.50±6.31) were lower than that of natural forests (NF). The carbon storage in TB was lower in BP (22.57±6.18) compared to NF, while those in PK(21.17±8.76) and PS (20.80±6.40) were higher than in NF. While there were no significant differences in the carbon storage of TB, FF, and the total forest among tree species, results from MS showed a significant difference among species. TB and the total forest carbon storages in all sites increased after reclamation. Soil pH and cation exchange capacity values in BP and PS were lower than in NF. Amounts of labile carbon, available phosphate, and microbial biomass carbon in reclaimed sites were less than half of NF. There are a number of methods that could increase the reclamation efficiency. Applications of lime or organic fertilizers, as well as tillage operations, may improve soil properties in reclaimed coal mines. Additionally, pruning and thinning would increase tree growth thereby increasing carbon storage.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1669-1679
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• 2020

This study examines the washing effect of precipitation on particulate matter (PM) and the rainwater quality (pH, electrical conductivity (EC), water-soluble ions concentration). Of six rain events in total, rainwater samples were continuously collected every 50 mL from the beginning of the precipitation using rainwater collecting devices at Pukyong National University, Busan, South Korea, from March 2020 to July 2020. The collected rainwater samples were analyzed for pH, EC, and water-soluble ions (cations: Na+, Mg2+, K+, Ca2+, NH4+, and anions: Cl-, NO3-, SO42-). The concentrations of particulate matter were continuously measured during precipitation events with a custom-built PM sensor node. For initial rainwater samples, the average pH and EC were approximately 4.3 and 81.9 μS/cm, and the major ionic components consisted of NO3- (5.4 mg/L), Ca2+ (4.2 mg/L), Cl- (4.1 mg/L). In all rainfall events, rainwater pH gradually increased with rainfall duration, whereas EC gradually decreased due to the washing effect. When the rainfall intensities were relatively weak (<5 mm/h), PM10 reduction efficiencies were less than 40%. When the rainfall intensities were enhanced to more than 7.5 mm/h, the reduction efficiencies reached more than 60%. For heavy rainfall events, the acidity and EC, as well as ions concentrations of initial rainwater samples, were higher than those in later samples. This appears to be related to the washing effect of precipitation on PM10 in the atmosphere.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.23-48
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• 2021

The Korean economy has achieved continuous economic growth for the past several decades thanks to the government's export strategy policy. This increase in exports is playing a leading role in driving Korea's economic growth by improving economic efficiency, creating jobs, and promoting technology development. Traditionally, the main factors affecting Korea's exports can be found from two perspectives: economic factors and industrial structural factors. First, economic factors are related to exchange rates and global economic fluctuations. The impact of the exchange rate on Korea's exports depends on the exchange rate level and exchange rate volatility. Global economic fluctuations affect global import demand, which is an absolute factor influencing Korea's exports. Second, industrial structural factors are unique characteristics that occur depending on industries or products, such as slow international division of labor, increased domestic substitution of certain imported goods by China, and changes in overseas production patterns of major export industries. Looking at the most recent studies related to global exchanges, several literatures show the importance of cultural aspects as well as economic and industrial structural factors. Therefore, this study attempted to develop a forecasting model by considering cultural factors along with economic and industrial structural factors in calculating the import volume of each country from Korea. In particular, this study approaches the influence of cultural factors on imports of Korean products from the perspective of PUSH-PULL framework. The PUSH dimension is a perspective that Korea develops and actively promotes its own brand and can be defined as the degree of interest in each country for Korean brands represented by K-POP, K-FOOD, and K-CULTURE. In addition, the PULL dimension is a perspective centered on the cultural and psychological characteristics of the people of each country. This can be defined as how much they are inclined to accept Korean Flow as each country's cultural code represented by the country's governance system, masculinity, risk avoidance, and short-term/long-term orientation. The unique feature of this study is that the proposed final prediction model can be selected based on Design Principles. The design principles we presented are as follows. 1) A model was developed to reflect interest in Korea and cultural characteristics through newly added data sources. 2) It was designed in a practical and convenient way so that the forecast value can be immediately recalled by inputting changes in economic factors, item code and country code. 3) In order to derive theoretically meaningful results, an algorithm was selected that can interpret the relationship between the input and the target variable. This study can suggest meaningful implications from the technical, economic and policy aspects, and is expected to make a meaningful contribution to the export support strategies of small and medium-sized enterprises by using the import forecasting model.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Analytical Science and Technology
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• v.34 no.6
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• pp.259-266
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• 2021

Cannabis (Marijuana) is one of the most widely used drugs in the world, and its distribution has been controlled in South Korea since 1976. Identification of 11-nor-Δ⁹-carboxy-tetrahydrocannabinol (THCCOOH) in urine can provide important proof of cannabis use, and it is considered scientific evidence in the forensic field. In this study, we describe a simultaneous quantitative method for identifying THCCOOH and THCCOOH-glucuronide in urine, using simple liquid-liquid extraction (LLE), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). THCCOOH-D₃ and THCCOOH-glucuronide-D₃ were used as internal standards. Validation results of the matrix effect, as well as recovery, linearity, precision, accuracy, process efficiency, and stability were all satisfactory. No carryover, endogenous or exogenous interferences were observed. The limit of detection (LOD) of THCCOOH and THCCOOH-glucuronide were 0.3 and 0.2 ng/mL, respectively. The developed method was applied to 28 authentic human urine samples that tested positive in immunoassay screening and gas chromatography/mass spectrometry (GC/MS) tests. The ranges of concentrations of THCCOOH and THCCOOH-glucuronide in the samples were less than LOQ~266.90 ng/mL and 6.43~2133.03 ng/mL, respectively. The concentrations of THCCOOH-glucuronide were higher than those of THCCOOH in all samples. This method can be effectively and successfully applied for the confirmation of cannabinoid use in human urine samples in the forensic field.

• Journal of the Korean Chemical Society
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• v.65 no.3
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• pp.197-202
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• 2021

In this study, activated clay treated with H₂SO₄ (20% by weight) and heat at 90 ℃ for 8 h for acid white soil was used as an adsorbent for the removal of PO₄^3--P in water. Prior to the adsorption experiment, the characteristics of activated clay was examined by X-ray Fluorescence Spectrometry (XRF) and BET surface area analyser. The adsorption of PO₄^3--P on activated clay was steeply increased within 0.25 h and reached equilibrium at 4 h. At 5 mg/L of low PO₄^3--P concentration, roughly 98% of adsorption efficiency was accomplished by activated clay. The adsorption data of PO₄^3--P were introduced to the adsorption isotherm and kinetic models. It was seen that both Freundlich and Langmuir isotherms were applied well to describe the adsorption behavior of PO₄^3--P on activated clay. For adsorption PO₄^3--P on activated clay, the Freundlich and Langmuir isotherm coefficients, K_F and Q, were found to be 8.3 and 20.0 mg/g, respectively. The pseudo-second-order kinetics model was more suitable for adsorption of PO₄^3--P in water/activated clay system owing to the higher correlation coefficient R² and the more proximity value of the experimental value q_e,exp and the calculated value q_e,cal than the pseudo-first-order kinetics model. The results of study indicate that activated clay could be used as an efficient adsorbent for the removal of PO₄³-P from water.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.485-496
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• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.23-53
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• 2023

In this review paper, previous studies on layered double hydroxides (LDHs) published in the Korean Citation Index (KCI) were examined to investigate a research trend for LDHs in Korea. Since the first publication in 2002, 160 papers on LDHs have been published until January 2023. Among the 31 academic fields, top 5 fields appeared in the order of chemical engineering, chemistry, materials engineering, environmental engineering, and physics. The chemical engineering shows the highest record of published paper (71 papers) while around 10 papers have been published in the other four fields. All papers were reclassified into 15 research fields based on the industrial and academic purposes of using LDHs. The top 5 in these fields are in order of environmental purification materials, polymer catalyst materials, battery materials, pharmaceutical/medicinal materials, and basic physicochemical properties. These findings suggest that researches on the applications of LDH materials in the academic fields of chemical engineering and chemistry for the improvement of their functions such as environmental purification materials, polymer catalysts, and batteries have been being most actively conducted. The application of LDHs for cosmetic and agricultural purposes and for developing environmental sensors is still at the beginning of research. Considering a market-potential and high-efficiency-eco-friendly trend, however, it will deserve our attention as emerging application fields in the future. All reclassified papers were summarized in our tables and a supplementary file, including information on applied materials, key results, characteristics and synthesis methods of LDHs used. We expect that our findings of overall trends in LDH research in Korea can help design future researches with LDHs and suggest policies for resources and energies as well as environments efficiently.