• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.39 no.1 s.119
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• pp.30-37
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• 2007

The seed paper was used in farm field recently for a sound young plant. The most of seed paper are made of synthetic non-woven sheet. Therefore, it is very difficult to bio-degrade in soil and is very hard to have some special function, for example keeping herbicide and/or insecticide activity because of its lack of chemical acceptability. The purpose of this research is manufacture of seedling paper which have a function of herbicide activity from waste paper. The fiber properties from waste paper were remarkably improved by fine removal with washing and/or flotation process. The paper-making ability for seed paper was enhanced with enzyme treatment of secondary fibers. The paper for seedling must have a good bio-degradation ability in soils. The absorption amount of chemical like as dithiopyr was increased remarkably in enzyme treated base paper. The embossing treatment of base paper was very effective for seed attachment and chemicals retention. And also, the developed seed paper showed a good penetration property of young root through embossed paper.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.45 no.6
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• pp.64-71
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• 2013

The biodegradable mulching paper could be applied for the environmental friendly agriculture as an alternative to the current vinyl mulching. In order to increase the usability of the mulching paper, the effects of the addition of various inorganic materials on the functionality of the mulching paper were evaluated in terms of practical benefits. The blend of carbonized rice husk powder and perlite resulted in the higher value in the air permeability of the mulching paper, which would be important for the health of plant root. The heat conservative of the mulching paper could be improved by adding the bottom ash or the fly ash because of the pore structure of the ashes. The pH of acidic soil could be neutralized by using the mulching paper containing paper-mill sludge ash or fly ash. The various results showed the addition of the inorganic materials could improve the functional properties of the mulching paper.

• Horticultural Science & Technology
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• v.33 no.1
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• pp.124-132
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• 2015

Echinacea purpurea (L.) Moench (purple cone flower) is an important medicinal plant; it can enhance immunity, relieve pain, and reduce inflammation, and also has hormonal, antiviral, and antioxidant effects. Adventitious root biomass of Echinacea purpurea was produced in commercial-scale bioreactors for use as a dietary supplement in the food industry and in traditional medicine. Biosafety and toxicological evaluations of tissue-cultured Echinacea purpurea adventitious roots (TCEPARs) were performed. Reverse mutation and chromosomal aberration tests showed no significant mutagenicity. Furthermore, repeated four-week oral dose tests performed in Sprague-Dawley rats did not show any notable changes in the general behavior of the rats, in the gross appearance of their internal organs, or in their mortality rate. There were no differences between the control group and the treatment group in parameters such as absolute body weight, hematology, blood chemistry, and absolute and relative organ weights. These findings indicate that TCEPARs are safe and nontoxic when consumed at an average dietary level and can be used as raw material for traditional medicine and the food industry.

• Annual Conference of KIPS
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of Radiation Industry
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• v.11 no.3
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• pp.139-144
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• 2017

To evaluation of clinical usefulness for B1+RMS limits, we compared image quality of Routine, Specific absorption rate (SAR) and Root mean square (RMS) protocol. 5 volunteers underwent Magnetic Resonance Imaging (MRI) scan of the brain using three different protocols. We draw Region of interest ROI in cortex, white matter, gray matter, putamen and thalamus of axial plan. Signal to noise ratio (SNR) were evaluated in each area and Contrast to noise ration (CNR) were evaluated between white matter and gray matter. Qualitative evaluation was used to score each ROI. B1+RMS is confirmed its usefulness compared to conventional SAR standard on the aspect of improvement of image quality, reduction of scan time and easy adjusting parameter.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Journal of the Korea Safety Management & Science
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• v.25 no.3
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• pp.63-71
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• 2023

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

• Design & Manufacturing
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• v.18 no.2
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• pp.35-40
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• 2024

Injection molding is a process widely used across various industries for molding plastics, and it is the most commonly applied process in root industries utilizing molds. Among the different types of injection molding, insert injection molding, where busbars are used as inserts, is increasingly being applied in the electric vehicle industry. However, currently, the insert injection molding process is manually performed, with workers placing insert components by hand before injection molding. This results in issues related to productivity, safety, and quality. Additionally, there is a growing demand for automation of such production lines due to hazardous working conditions, economic difficulties in the manufacturing industry, and the decline in the labor force caused by an aging population. This study focuses on the application of an automated system for the insert injection molding process used in electric vehicles. The development of an automated system for the transport and insertion of insert components, as well as the inspection and stacking processes after injection, has resulted in over a 25% improvement in productivity and more than a 27% reduction in defect rates.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.27-40
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• 2021

This study was carried out for the description of the conflicts on the measurement of the root collar diameter of the landscape trees that are currently being produced, distributed, and planted in S. Korea, and for determination of the standard for root collar diameter measurement. The difference in consciousness of appropriate measurement of root collar diameter among different ages of landscape practitioners was statistically significant at p<0.05 level. It seemed to be due to the difference in the amount of field experiences among different age groups. On "the ambiguity of measuring the root collar diameter' of landscape trees", the consciousness was significantly different at p<0.05 level among job positions. On "Improvement of measurement criteria for landscape trees," it was significantly different at p<0.05 level among job types. This was thought to be due to the disagreement between the client and the contractor. On "prevention of topsoil removal" when excavating landscape trees, the consciousness was significantly different at p<0.001 level among different age groups, and different at p<0.01 level among different occupations, and different at p<0.05 level among different working area. The consciousness on "removing top soil when excavating landscape trees and rooting after transplantation" was not significantly different. The consciousness on the conflict caused by "ambiguity in root collar diameter measurement criteria" was high with an average of 3.85 for job type, occupation, jop position, and work experience. It was higher for landscape contractors than public institutions. The higher job positions and more experiences, the more conflicts. The consciousness on the appropriate position of root collar diameter measurement for landscape trees revealed that measuring at above-ground part (66.5%) was prefered to the underground part (33.0%). During the excavation of landscape trees for transplant, topsoil removal up to average depth of -2cm to -4cm was favored by 84.0%, and the purpose of removing topsoil was recognized as 'to increase the size and unit cost' by 59.7%.