• The Research Journal of the Costume Culture
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• v.29 no.3
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• pp.374-387
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• 2021

The purpose of this study is to develop up-cycling fashion design methods centered on discarded denim material for the study of original up-cycling design methods. Up-cycling fashion design work was developed using digital clothing technology. This is a recent hot topic among sustainable fashion design methods. Up-cycling fashion design expression methods (categorized as dismantlement, collages, dépaysement, grafting, weaving, and tearing) were centered on design methods. These methods create various three-dimensional modeling effects in planar forms, whereby five pieces can be applied to the fabric and digitally produced. The results are as follows: First, the use of discarded denim fabric for the development of up-cycling fashion design pieces enabled the recycling of existing resources, provided solutions to environmental pollution problems, and provided expansion opportunities for design processes for sustainable fashion products that expand the design value of denim products and their utility. Second, new eco-friendly fashion designs that attempt to achieve diversity in modern fashion trends could be presented through formative contemporary fashion produced by up-cycling work products. Third, up-cycling fashion design work is expected to provide opportunities for eco-friendly fashion design methods. This will expand the value of sustainable fashion design by recycling simple waste materials through the use of three-dimensional digital clothing technology and further through the presentation of expanded life cycles that extend product planning, production, and life cycles.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.11-20
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• 2022

Currently, the operation rule of agricultural reservoirs in case of drought events follows the drought forecast warning standard of agricultural water supply. However, it is difficult to preemptively manage drought in individual reservoirs because drought forecasting standards are set according to average reservoir storage ratio such as 70%, 60%, 50%, and 40%. The equal standards based on average water level across the country could not reflect the actual drought situation in the region. In this study, we proposed the improvement of drought operation rule for agricultural reservoirs based on the percentile approach using past water level of each reservoir. The percentile approach is applied to monitor drought conditions and determine drought criteria in the U.S. Drought Monitoring (USDM). We applied the drought operation rule to reservoir storage rate in extreme 2017 spring drought year, the one of the most climatologically driest spring seasons over the 1961-2021 period of record. We counted frequency of each drought criteria which are existing and developed operation rules to compare drought operation rule determining the actual drought conditions during 2016-2017. As a result of comparing the current standard and the percentile standard with SPI6, the percentile standard showed severe-level when SPI6 showed severe drought condition, but the current standard fell short of the results. Results can be used to improve the drought operation criteria of drought events that better reflects the actual drought conditions in agricultural reservoirs.

• New & Renewable Energy
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• v.18 no.3
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• pp.10-22
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• 2022

Forest biomass energy is based on scientific evidence in response to carbon neutrality and the climate crisis, international consensus, and environmental-geographic characteristics of each nation. In this study, the authors aimed to analyze macroscopic forest biomass energy policies for ten major countries. They categorized them into six detailed categories (Sustainable utilization, Cascading Uutilization, Replacement of fossil fuel/Carbon intensive products, Utilization of forest by-products/residues as the source of energy, Contribution to carbon-neutral/climate change, and Biomass combined with CCS/CCUS ). In addition, the surveyed nations have developed a policy consensus on the active use of forest biomass with sustainable forest management except for the cascading utilization category. Furthermore, the authors evaluated the mid to long-term plans of the Korean government for improvements in the policy and legal aspects. As a result, the authors derived four major directions that South Korea should approach strategically in the future (1) secure financial resources for sustainable forest management and stimulating investment in the timber industry, (2) promote unified policies to establish a bio-economy, (3) enhancement of the forest biomass energy system, and (4) reorganization and promotion of strategy centered on the opinions of field experts in internal and external instability.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.29-42
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• 2023

Universal Soil Loss Equation (USLE) is suggested and employed in the policy to conserve soil resources and to manage the impact of development, since soil loss is very essential to nonpoint source pollution management. The equation requires only five factors to estimate average annual potential soil loss, USLE is simplicity provides benefits in use of the equation. However, it is also limitation of the model, since the estimated results are very sensitive to the five factors. There is a need to examine the application procedures. Three approaches to estimate potential soil loss were examined, In the first approach, all factors were prepared with raster data, soil loss were computed for each cell, and sum of all cell values was determined as soil loss for the watersheds. In the second approach, the mean values for each factor were defined as representing USLE factors, and then the five factors were multiplied to determine soil loss for the watersheds. The third approach was same as the second approach, except that the Vegetative and Mechanical measure was used instead of the Cover and management factor and Support practice factor. The approaches were applied in 38 watersheds, they displayed significant difference, moreover no trends were detected for the soil loss at watersheds with the approaches. Therefore, it was concluded that there is a need to be developed and provided a typical guideline or public systems so that soil loss estimations have consistency with the users.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.133-140
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• 2023

Soil erosion can cause scouring and failures of underwater structures, therefore, various soil improvement techniques are used to increase the soil erosion resistance. The microbially induced calcium carbonate precipitation (MICP) method is proposed to increase the erosion resistance, however, there are only limited experimental and numerical studies on the use of MICP treatment for improvement of surface erosion resistance. Therefore, this study investigates the improvement in surface erosion resistance of sands by MICP through laboratory experiments and numerical modeling. The surface erosion behaviors of coarse sands with various calcium carbonate contents were first investigated via the erosion function apparatus (EFA). The test results showed that MICP treatment increased the overall erosion resistance, and the contribution of the precipitated calcium carbonate to the erosion resistance and critical shear stress was quantified in relation to the calcium carbonate contents. Further, these surface erosion processes occurring in the EFA test were simulated through the coupled computational fluid dynamics (CFD) and discrete element method (DEM) with the cohesion bonding model to reflect the mineral precipitation effect. The simulation results were compared with the experimental results, and the developed CFD-DEM model with the cohesion bonding model well predicted the critical shear stress of MICP-treated sand. This work demonstrates that the MICP treatment is effective in improving soil erosion resistance, and the coupled CFD-DEM with a bonding model is a useful and promising tool to analyze the soil erosion behavior for MICP-treated sand at a particle scale.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.5
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• pp.689-700
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• 1996

Fisheries products have to be produced and maintained by work processes from the environment, sometimes helped by people. In Korean fisheries both environmental production and its economic use are included within the windows of system approach. EMERGY is the sum of all inputs expressed as one form of solar energy required directly and indirectly to make a product. Calculating EMERGY flows into Korean fisheries evaluates the real wealth contributed by environmental production and its economic use. Several indices calculated from EMERGY analysis table and a three-arm diagram give perspective on the type and efficiency of the environmental uses. Net EMERGY yield ratio is a measure of its net contribution to the economy beyond its own operation. For adjacent waters fisheries in Korea, the net contribution to the economy is 11.85 or higher, which is a stimulus to the economy that is able to purchase it. EMERGY investment ratio measures the intensity of the economic development and the loading of the environment. The ratio for Korean fisheries as a whole is 0.50, for the adjacent waters fisheries 0.09 and for the shallow-sea cultures 1.28, which is lower than the same index for the industry of the developed country (7.0). The component of environment drawn into production are large compared to purchased investment in Korean fisheries. Much more EMERGY is contained in fisheries products than in the paid services used to process the products. The EMERGY exchange ratio for Korean fisheries as a whole is 6.98, for the adjacent waters fisheries is 10.69 and for the shallow-sea cultures is 1.25. Using market values to evaluate wealth of environment resources is found to be many times too small. Money is paid only to people for their contribution, and never to the environment for its contribution. Macroeconomic value is the appropriate measure for discussing large-scale considerations of an economy, including environment and human goods & services.