• Clean Technology
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• v.25 no.3
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• pp.223-230
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• 2019

This research was performed to evaluate the characteristics of food waste from 5 areas in Gangwon Province, Korea and to predict the $CH_4$ gas production rate. Food wastes were sampled in July and September, 2017. The amount of methane gas generation was evaluated through the biochemical methane potential (BMP) test and a calculation method using chemical composition. Average bulk density and pH of the food wastes were in the range of $0.758{\sim}0.850g\;cm^{-3}$ and 4.29 ~ 4.75, respectively. By physical composition, vegetables were the highest with 56.43 ~ 72.81% with fruits recording 5.31 ~ 8.95%, cereals 1.60 ~ 18.73%, fish and meat 4.47 ~ 12.11%, and filtrate 1.76 ~ 3.64%. The average water content was 69.30 ~ 75.87%, and VS and ash content were 22.50 ~ 27.98% and 1.63 ~ 2.48%, respectively. In addition, $BOD_5$, $COD_{Cr}$, and $COD_{Mn}$ were in the ranges of $17,690.3{\sim}33,154.9mg\;L^{-1}$, $106,212.3{\sim}128,695.5mg\;L^{-1}$, and $51,266.1{\sim}63,426.3mg\;L^{-1}$, respectively. The NaCl content ranged from 0.81 to 1.17%. The results of elemental analysis showed that the contents of C, H, O, N, and S were 44.87 ~ 48.1%, 7.12 ~ 7.57%, 40.13 ~ 43.78%, 3.22 ~ 4.14%, and 0.00 ~ 0.02%, respectively. In a comparison of the methane production yield per VS mass of food waste, there was no significant difference between the cumulative amount (${0.303{\sim}0.354m_{CH4}}^3\;{kg_{VS}}^{-1}$) by the BMP test and the theoretical amount (${0.294{\sim}0.352m_{CH4}}^3\;{kg_{VS}}^{-1}$) calculated by chemical composition.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.640-654
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• 2021

Purpose: Normal education operation was difficult in the national disaster situation of Coronavirus Infection-19. Non-face-to-face education can be an alternative to face to face education, but it is not easy to provide the same level of education. In this study, the professor of disaster management field will identify problems that can occur in the overall operation and progress of non-face-to-face education and seek ways to improve non-face-to-face education. Method: Non-face-to-face real-time education was largely categorized into pre-class, in-class, post-class, and evaluation, and case studies were conducted through the professor's case studies. Result&Conclusion: The results of the survey are as follows: First, pre-class, it was worth considering providing a non-face-to-face educational place for professors, and the need for prior education on non-face-to-face educational equipment and systems was required. In addition, it seems necessary to make sure that education is operated smoothly by giving enough notice on classes and to make efforts to develop non-face-to-face education programs for practical class. Second, communication between professor and learner, and among learners can be an important factor in non-face-to-face mid classes. To this end, it is necessary to actively utilize debate-type classes to lead learners to participate in education and enhance the educational effect through constant interaction. Third, non-face-to-face post classes, policies on the protection of privacy due to video records should be prepared to protect the privacy of professors in advance, and copyright infringement on educational materials should also be considered. In addition, it is necessary to devise various methods for fair and objective evaluation. According to the results of the interview, in the contents, which are components of non-face-to-face education, non-face-to-face education requires detailed plans on the number of students, contents, and curriculum suitable for non-face-to-face education from the design of the education. In the system, it is necessary to give the professor enough time to fully learn and familiarize with the function of the program through pre-education on the program before the professor gives non-face-to-face classes, and to operate the helpdesk, which can thoroughly check the pre-examination before non-face-to-face education and quickly resolve the problem in case of a problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.510-523
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• 2020

This study analyzed groundwater quality in hydroponic cultivation facilities. Through this study, the possibility of groundwater use was evaluated according to the quality of the groundwater for hydroponic cultivation facilities. Good groundwater quality, on average, is pH 6.61, EC 0.27 dS/m, NO3-N 7.64 mg/L, NH4+-N 0.80 mg/L, PO4-P 0.09 mg/L, K+ 6.26 mg/L, Ca2+ 18.57 mg/L, Mg2+ 4.38 mg/L, Na+ 20.85 mg/L, etc. All of these satisfy the water quality standard for raw water in nutrient cultivation. But in the case of farmers experiencing problems with groundwater quality, most of the items exceeded the water quality standard. As a result of the analysis, it was judged that purifying groundwater of unsuitable quality for crop cultivation, and using it as raw water, was effective in terms of water quality and soil purification. If an agricultural water purification system is constructed based on the results of this study, it is judged that the design will be easy because the items to be treated can be estimated. If a purification system is established, it can use groundwater directly in the facility and for horticulture. These study results will be available for use in sustainable agriculture and environments.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.94-106
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• 2022

In order to increase timber self-sufficiency, Korea's 6th Basic Forest Plan aims to increase the density of forest roads to 12.8 m ha^-1 by 2037. However, due to rapid re-forestation, current management infrastructure is insufficient, with just 4.8 m ha^-1 of forest roads in 2017. This is partly due to time and cost limitations on the process of forest road feasibility evaluation, which considers factors such as topography and forest conditions. To solve this problem, we propose an eco-friendly and efficient forest road network planning method using a geographic information system (GIS), which can evaluate a potential road site remotely based on spatial information. To facilitate such planning, this study identifies forest road construction priorities that can be evaluated using spatial information, such as topography, forest type and forest disasters. A method of predicting the optimal route to connect a forest road with existing roads is also derived. Overlapping analysis was performed using GIS-MCE (which combines GIS with multi-criteria evaluation), targeting the areas of Cheongsong-gun and Buk-gu, Pohang-si, which have a low forest-road density. Each factor affecting the suitability of a proposed new forest road site was assigned a cost, creating a cost surface that facilitates prioritization for each forest type. The forest path's optimal route was then derived using least-cost path analysis. The results of this process were 30 forestry site recommendations in Cheongsong-gun and one in Buk-gu, Pohang-si; this would increase forest road density for the managed forest sites in Cheongsong-gun from 1.58 m ha^-1 to 2.55 m ha^-1. This evaluation method can contribute to the policy of increasing timber self-sufficiency by providing clear guidelines for selecting forest road construction sites and predicting optimal connections to the existing road network.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.49-58
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• 2010

This paper presents experimental results of a series of 1-g shaking table model tests performed on end-bearing single piles and pile groups to investigate the effect of particle size on the dynamic behavior of soil-pile systems. Two soil-pile models were tested twice: first using Jumoonjin sand, and second using Australian Fine sand. In the case of single-pile models, the lateral displacement was almost within 1% of pile diameter which corresponds to the elastic range of the pile. The back-calculated p-y curves show that the subgrade reaction of the Jumoonjin-sand-model ground was larger than that of the Australian Fine-sand-model ground at the same displacement. This phenomenon means that the stress-strain behavior of Jumoonjin sand was initially stiffer than that of Australian Fine sand. This difference was also confirmed by resonant column tests and compression triaxial tests. And the single pile p-y backbone curves of the Australian fine sand were constructed and compared with those of the Jumoonjin sand. As a result, the stiffness of the p-y backbone curves of Jumunjin sand was larger than those of Australian fine sand. Therefore, using the same p-y curves regardless of particle size can lead to inaccurate results when evaluating dynamic behavior of soil-pile system. In the case of the group-pile models, the lateral displacement was much larger than the elastic range of pile movement at the same test conditions in the single-pile models. The back-calculated p-y curves in the case of group pile models were very similar in both sands because the stiffness difference between the Jumoonjin-sand-model ground and the Australian Fine-sand-model ground was not significantly large at a large strain level, where both sands showed non-linear behavior. According to a series of single pile and group pile test results, the evaluation group pile effect using the p-multiplier can lead to inaccurate results on dynamic behavior of soil-pile system.

• Journal of the Korean Geotechnical Society
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• v.24 no.4
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• pp.101-114
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• 2008

Pressurized grouting is a common technique in geotechnical engineering applications to increase the stiffness and strength of the ground mass and to fill boreholes or void space in a tunnel lining and so on. Recently, the pressurized grouting has been applied to a soil-nailing system which is widely used to improve slope stability. Because interaction between pressurized grouting paste and adjacent ground mass is complicated and difficult to analyze, the soil-nailing design has been empirically performed in most geotechnical applications. The purpose of this study is to analyze the ground behavior induced by pressurized grouting paste with the aid of laboratory model tests. The laboratory tests are carried out for four kinds of granitic residual soils. When injecting pressure is applied to grout, the pressure measured in the adjacent ground initially increases for a while, which behaves in the way of the membrane model. With the lapse of time, the pressure in the adjacent ground decreases down to a value of residual stress because a portion of water in the grouting paste seeps into the adjacent ground. The seepage can be indicated by the fact that the ratio of water/cement in the grouting paste has decreased from a initial value of 50% to around 30% during the test. The reduction of the W/C ratio should cause to harden the grouting paste and increase the stiffness of it, which restricts the rebound of out-moved ground into the original position, and thus increase the in-situ stress by approximately 20% of the injecting pressures. The measured radial deformation of the ground under pressure is in good agreement with the expansion of a cylindrical cavity estimated by the cavity expansion theory. In-situ test revealed that the pullout resistance of a soil nailing with pressurized grouting is about 36% larger than that with regular grouting, caused by grout radius increase, residual stress effect, and/or roughness increase.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.